+Hands-on: Choose Your Own TutorialThis is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial
{% if include.text %} @@ -7,20 +7,20 @@ {% endif %} {% if include.option1 %} - - + + {% endif %} {% if include.option2 %} - - + + {% endif %} {% if include.option3 %} - - + + {% endif %} {% if include.option4 %} - - + + {% endif %} {% if include.default %} @@ -33,7 +33,7 @@ } } ready(function() { - cyoaDefault("{{ include.default | slugify_unsafe }}"); + cyoaDefault("{{ include.default | slugify_unsafe }}", "gtn-cyoa{{ include.disambiguation }}"); }) {% endif %} diff --git a/assets/js/main.js b/assets/js/main.js index b0de75db847c7f..ce4108dc561555 100644 --- a/assets/js/main.js +++ b/assets/js/main.js @@ -57,16 +57,16 @@ $("section#tutorial-content .hands_on,section#tutorial-content .hands-on").each( }) // CYOA Support -function cyoaChoice(text){ +function cyoaChoice(text, cyoaId){ if(text !== undefined && text !== null){ var loc = new URL(document.location) try { - localStorage.setItem(`gtn-cyoa-${loc.pathname}`, text); + localStorage.setItem(`${cyoaId}-${loc.pathname}`, text); } catch(e) { // Helaas pindakaas } - var inputs = document.querySelectorAll(".gtn-cyoa input"), + var inputs = document.querySelectorAll(`#${cyoaId} input`), options = [...inputs].map(x => x.value), nonMatchingOptions = options.filter(x => x !== text); @@ -85,36 +85,36 @@ function cyoaChoice(text){ } } -function cyoaDefault(defaultOption){ +function cyoaDefault(defaultOption, cyoaId){ // Start with the URL parameter var loc = new URL(document.location) - var urlOption = loc.searchParams.get("gtn-cyoa"); + var urlOption = loc.searchParams.get(cyoaId); if(urlOption){ - cyoaChoice(urlOption); + cyoaChoice(urlOption, cyoaId); return; } // Otherwise fall back to local storage (survives refreshes) var lsOption; try { - lsOption = localStorage.getItem(`gtn-cyoa-${loc.pathname}`); + lsOption = localStorage.getItem(`${cyoaId}-${loc.pathname}`); } catch(e) { // Helaas pindakaas } if(lsOption !== null && lsOption !== undefined){ - cyoaChoice(lsOption); + cyoaChoice(lsOption, cyoaId); return; } // Otherwise if the browser is remembering for us, use that. var currentlySelected = [...document.querySelectorAll("input[name='cyoa']")].filter(x => x.checked)[0]; if(currentlySelected){ - cyoaChoice(currentlySelected); + cyoaChoice(currentlySelected, cyoaId); return; } // And failing that, use the default. - cyoaChoice(defaultOption); + cyoaChoice(defaultOption, cyoaId); } (function (window, document) { diff --git a/bin/lint.rb b/bin/lint.rb index 2e618a270c4b3a..0445cef9f9f840 100755 --- a/bin/lint.rb +++ b/bin/lint.rb @@ -1015,6 +1015,12 @@ def self.fix_file(path) code: 'GTN:014')]) end + if path.match(/\?/) + emit_results([ReviewDogEmitter.file_error(path: path, + message: 'There ?s in this filename, that is forbidden.', + code: 'GTN:014')]) + end + case path when /md$/ handle = File.open(path, 'r') diff --git a/news/_posts/2024-01-17-sc-fair-data.md b/news/_posts/2024-01-17-sc-fair-data.md new file mode 100644 index 00000000000000..271ec9577d99bc --- /dev/null +++ b/news/_posts/2024-01-17-sc-fair-data.md @@ -0,0 +1,24 @@ +--- +title: "FAIR Data management in single cell analysis" +tags: [single cell,data management,data import,fair] +contributions: + authorship: [wee-snufkin, hexhowells, nomadscientist] + funding: [elixir-fair-data] +layout: news +--- + +# New single cell section: Changing data formats & preparing objects + +Now a nightmare of switching between single cell datatypes or importing the data is gone! With the new section on Changing data formats & preparing objects, the users can now confidently jump into the analysis. Some of those tutorials were created in the framework of ELIXIR-UK: FAIR Data Stewardship training, which aims to improve Findable Accessible Interoperable Reusable (FAIR) data management in the life sciences. + +We have developed the following tutorials: +- [Converting between common single cell data formats]({% link topics/single-cell/tutorials/scrna-data-ingest/tutorial.md %}) +- [Importing files for single cell analysis from public atlases]({% link topics/single-cell/tutorials/EBI-retrieval/tutorial.md %}) +- [Converting NCBI Data to the AnnData Format]({% link topics/single-cell/tutorials/scrna-ncbi-anndata/tutorial.md %}) +- [Creating a single-cell RNA-seq reference dataset for deconvolution]({% link topics/single-cell/tutorials/bulk-music-2-preparescref/tutorial.md %}) +- [Creating the bulk RNA-seq dataset for deconvolution]({% link topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.md %}) + + +# Where can I learn more? + +You can find out more about the FAIR Data Stewardship training on the [ELIXIR-UK Fellowship site](http://fellowship.elixiruknode.org/). Also, keep an eye on the upcoming live trainings! diff --git a/topics/contributing/tutorials/create-new-tutorial-content/tutorial.md b/topics/contributing/tutorials/create-new-tutorial-content/tutorial.md index 8f012f3c66156d..de4b5ce1ea948a 100644 --- a/topics/contributing/tutorials/create-new-tutorial-content/tutorial.md +++ b/topics/contributing/tutorials/create-new-tutorial-content/tutorial.md @@ -1195,6 +1195,25 @@ And then they can wrap the relevant sections with a `div` block with the relevan This can also be used inline: My favourite fruit is an 🍍🥑. +>Multiple, Disconnected CYOAs +> If you wish to have multiple CYOAs in a single tutorial, you are free to do that! However you must: +> +> 1. Ensure that all options are disjoint, there should not be any shared terms! (I.e if the both CYOAs need to use "STAR", please find a different way to phrase it, or even use "STAR ", it just needs to be different.) +> 2. Provide a disambiguation term for them, passed as a parameter to all, or all but one, includes. +> +> This disambiguation term will affect the URL parameter, which will become `?gtn-cyoa{term}={value}` +> +> E.g.: +> +> ``` +> {% raw %} +> {% include _includes/cyoa-choices.html option1="Oui" option2="Non" default="Oui" text="Vos données ESTAMP sont prêtes ?" %} +> {% include _includes/cyoa-choices.html option1="Yes" option2="No" text="Do the thing?" disambiguation="english" %} +> {% endraw %} +> ``` +{: .tip} + + ### URL Parameter The branch can be selected via URL parameter e.g. for courses, to prevent users selecting the wrong path. Just supply `?gtn-cyoa=Ananas` (or your preferred value) on the tutorial URL. diff --git a/topics/ecology/tutorials/champs-blocs/tutorial_FR.md b/topics/ecology/tutorials/champs-blocs/tutorial_FR.md index 847e729b8c6d5b..eb4e032ecb4046 100644 --- a/topics/ecology/tutorials/champs-blocs/tutorial_FR.md +++ b/topics/ecology/tutorials/champs-blocs/tutorial_FR.md @@ -73,7 +73,7 @@ Concentrons-nous maintenant sur notre workflows d'analyse sur l'état écologiqu # Centraliser les données -{% include _includes/cyoa-choices.html option1="Oui" option2="Non" default="Oui" text="Vos données ESTAMP sont prêtes ?" %} +{% include _includes/cyoa-choice-fr.html option1="Oui" option2="Non" default="Oui" text="Vos données ESTAMP sont prêtes ?" %}> 1. Télécharger les données depuis la base ESTAMP [estamp.afbiodiversite.fr](https://estamp.afbiodiversite.fr/) puis rendez-vous dans l'espace "accédez aux données" en bas à droite de la page. Après aplication des critères de filtre, vous obtenez une archive zip. diff --git a/topics/single-cell/images/scrna-data/HCA.jpg b/topics/single-cell/images/scrna-data/HCA.jpg new file mode 100644 index 00000000000000..61c3420bb6e494 Binary files /dev/null and b/topics/single-cell/images/scrna-data/HCA.jpg differ diff --git a/topics/single-cell/images/scrna-data/exp_id.jpg b/topics/single-cell/images/scrna-data/exp_id.jpg new file mode 100644 index 00000000000000..2c56fa7e6cdb79 Binary files /dev/null and b/topics/single-cell/images/scrna-data/exp_id.jpg differ diff --git a/topics/single-cell/tutorials/EBI-retrieval/tutorial.md b/topics/single-cell/tutorials/EBI-retrieval/tutorial.md index 389ff6a7bd01b3..3e00bf2011aa87 100644 --- a/topics/single-cell/tutorials/EBI-retrieval/tutorial.md +++ b/topics/single-cell/tutorials/EBI-retrieval/tutorial.md @@ -1,13 +1,13 @@ --- layout: tutorial_hands_on subtopic: datamanipulation -priority: 3 -title: EBI Single Cell Expression Atlas files to AnnData (Scanpy) or Seurat Object | Creating preprocessed dataset for sc-RNA Filter, Plot, Explore tutorial +priority: 2 +title: Importing files from public atlases questions: -- How do I use the EBI Single Cell Expression Atlas? +- How do I use the EBI Single Cell Expression Atlas and Human Cell Atlas? - How can I reformat and manipulate the downloaded files to create the correct input for downstream analysis? objectives: -- You will retrieve raw data from the EBI Single Cell Expression Atlas. +- You will retrieve raw data from the EBI Single Cell Expression Atlas and Human Cell Atlas. - You will manipulate the metadata and matrix files. - You will combine the metadata and matrix files into an AnnData or Seurat object for downstream analysis. @@ -46,7 +46,7 @@ tags: # Introduction -Public single cell datasets seem to accumulate by the second. Well annotated, quality datasets are slightly trickier to find. which is why projects like the [Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home) (SCXA) exist - to curate datasets for public use. Here, we will guide you through transforming data imported from the SCXA repository into the input file required for the [Filter, Plot, Explore tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}). +Public single cell datasets seem to accumulate by the second. Well annotated, quality datasets are slightly trickier to find. which is why projects like the [Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home) (SCXA) exist - to curate datasets for public use. Here, we will guide you through transforming data imported from the SCXA repository into the input file required for the [Filter, Plot, Explore tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) and we will also show how to use the public atlases for your own research. >> @@ -61,6 +61,17 @@ Public single cell datasets seem to accumulate by the second. Well annotated, qu Galaxy has a specific tool for importing data from the SCXA ({% cite Moreno2020.04.08.032698 %}), which combines all the preprocessing steps shown in [the previous tutorial]({% link topics/single-cell/tutorials/scrna-case_alevin/tutorial.md %}) into one! For this tutorial, the dataset can be seen [at the EBI](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/downloads) with experiment ID of `E-MTAB-6945`. +> How to access other experiments? +> +> You can search datasets according to various criteria either using search box in **Home** tab or choosing kingdom, experiment collection, technology type (and others) in **Browse experiments** tab. When you find the experiment you are interested in, just click on it and the experiment ID will be displayed in the website URL, as shown below. +> +>  +> +> Once you know the experiment ID, you can use EBI SCXA Data Retrieval tool in Galaxy! +> +{: .details} + + >Retrieving data from Single Cell Expression Atlas > > 1. {% tool [EBI SCXA Data Retrieval](toolshed.g2.bx.psu.edu/repos/ebi-gxa/retrieve_scxa/retrieve_scxa/v0.0.2+galaxy2) %} with the following parameters: @@ -104,7 +115,9 @@ It's important to note that this matrix is processed somewhat through the SCXA p {: .question} -# Metadata manipulation +## Metadata manipulation + +At this point you might want to do some modifications in the files before downstream analysis. That can include re-formating the cell metadata or changing the names of the column headers, it all depends on your dataset and how you want to perfrom your analysis. It's also fine to transform those files straight away. Here, we will show an extended version of metadata manipulation which allows us to create an input file consistent with [the next tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) workflow. Before creating an AnnData object, we need to make a small modification in experimental design table. The dataset contains information about the 7 experimental samples (N701 – N707). However, in the {% icon param-file %} `exp_design.tsv` dataset, which contains the cell metadata, these samples are just numbered from 1 to 7. @@ -171,7 +184,7 @@ While we're renaming things, let's also fix our titles. > 2. Rename {% icon galaxy-pencil %} output `Cell metadata` {: .hands_on} -# Check mitochondrial gene name format +## Check mitochondrial gene name format We might like to flag mitochondrial genes. They can be identified quite easily since - depending on the species and formatting convention - their names often start with `mt`. Since tools for flagging mitochondrial genes are often case-sensitive, it might be a good idea to check the formatting of the mitochondrial genes in our dataset. @@ -197,7 +210,7 @@ Now we can create our single cell object!-# Creating the AnnData object +## Creating the AnnData object We will do several modifications within the AnnData object so that you can follow [the next tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}). @@ -216,7 +229,7 @@ We will do several modifications within the AnnData object so that you can follo > {: .hands_on} -# AnnData manipulation +## AnnData manipulation We will now change the header of the column containing gene names from `gene_symbols` to `Symbol`. This edit is only needed to make our AnnData object compatible with this tutorial's workflow. We will also flag the mitochondrial genes. @@ -241,9 +254,9 @@ And the good news is that we can do both those steps using only one tool! > {: .hands_on} -And that's all! What's even more exciting about the {% icon tool %}) **AnnData Operations** tool is that it automatically calculates a bunch of metrics, such as `log1p_mean_counts`, `log1p_total_counts`, `mean_counts`, `n_cells`, `n_cells_by_counts`, `n_counts`, `pct_dropout_by_counts`, and `total_counts`. Amazing, isn't it? +And that's all! What's even more exciting about the {% icon tool %} **AnnData Operations** tool is that it automatically calculates a bunch of metrics, such as `log1p_mean_counts`, `log1p_total_counts`, `mean_counts`, `n_cells`, `n_cells_by_counts`, `n_counts`, `pct_dropout_by_counts`, and `total_counts`. Amazing, isn't it? -# Conclusion +## Conclusion Now you can use this object as input for the [Filter, Plot, Explore tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) and its associated workflow! Even though this tutorial was designed specifically to modify the AnnData object to be compatible with the subsequent tutorial, it also shows useful tools that you can use for your own, independent data analysis. You can find the [workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/workflow-constructed-from-history-ebi-workflow) and the [answer key history](https://singlecell.usegalaxy.eu/u/j.jakiela/h/ebi-scxa---anndata-scanpy-or-seurat-object-1). However, if you want to use the workflow from this tutorial, you have to keep in mind that different datasets may have different column names. So you have to check them first, and only then you can modify them. @@ -252,7 +265,7 @@ Even though this tutorial was designed specifically to modify the AnnData object-# Creating the Seurat Object +## Creating the Seurat Object >+ + + + +# Human Cell Atlas Matrix Downloader + +Another public atlas that you can use to access the datasets is [Human Cell Atlas data portal](https://data.humancellatlas.org/). We will show you the tool in Galaxy which allows to retrieve expression matrices and metadata for any public experiment available in that repository. + +To use it, simply set the project title, project label or project UUID, which can be found at the [HCA data browser](https://data.humancellatlas.org/explore/projects), and select the desired matrix format (Matrix Market or Loom). + + + +For projects that have more than one organism, one needs to be specified. Otherwise, there is no need to set the species. + +Let's use the suggested example of the project *Single cell transcriptome analysis of human pancreas*. If you check this project in HCA, you'll find out that it's actually its label. But it will work the same if you enter the title or UUID! + +>Create the Seurat Object > @@ -264,9 +277,74 @@ Even though this tutorial was designed specifically to modify the AnnData object > - *"Cell metadata"*: `Cell metadata` > 3. Rename {% icon galaxy-pencil %} output `Seurat object` > +> You can also choose if you want to create Seurat object, Loom or Single Cell Experiment by selecting your option in *"Choose the format of the output"*. +> {: .hands_on} -# Conclusion +## Conclusion And you're there! You now have a usable Seurat object for analysis with Seurat tools in your history! {% icon congratulations %} Congrats!Create AnnData object +> +> {% tool [Human Cell Atlas Matrix Downloader](toolshed.g2.bx.psu.edu/repos/ebi-gxa/hca_matrix_downloader/hca_matrix_downloader/v0.0.4+galaxy0) %} with the following parameters: +> - *"Human Cell Atlas project name/label/UUID"*: `Single cell transcriptome analysis of human pancreas` +> - *"Choose the format of matrix to download"*: `Matrix Market` +{: .hands_on} + +>What will be the output? +> +> When "Matrix Market" is seleted, outputs are in 10X-compatible Matrix Market format: +> - **Matrix (txt)**: Contains the expression values for genes (rows) and cells (columns) in raw counts. This text file is formatted as a Matrix Market file, and as such it is accompanied by separate files for the gene identifiers and the cells identifiers. +> - **Genes (tsv)**: Identifiers (column repeated) for the genes present in the matrix of expression, in the same order as the matrix rows. +> - **Barcodes (tsv)**: Identifiers for the cells of the data matrix. The file is ordered to match the columns of the matrix. +> - **Experiment Design file (tsv)**: Contains metadata for the different cells of the experiment. +> +> When "Loom" is selected, output is a single Loom HDF5 file: +> - **Loom (h5)**: Contains expression values for genes (rows) and cells (columns) in raw counts, cell metadata table and gene metadata table, in a [single HDF5 file](http://linnarssonlab.org/loompy/format/index.html). +> +{: .details} + +If you chose **Loom** format and you need to convert your file to other datatype, you can use {% tool [SCEasy](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy1) %} (more details in the next section). If you chose **Matrix Market** format, you can then transform the output to AnnData or Seurat, as shown in the EBI SCXA example above. Below, you will find an example of transforming the output to AnnData object. + + +>Create AnnData object +> +> {% tool [Scanpy Read10x](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_read_10x/scanpy_read_10x/1.8.1+galaxy0) %} with the following parameters: +> - *"Expression matrix in sparse matrix format (.mtx)"*: `Human Cell Atlas Matrix Downloader on matrix.mtx` +> - *"Gene table"*: `Human Cell Atlas Matrix Downloader on genes.tsv` +> - *"Barcode/cell table"*: `Human Cell Atlas Matrix Downloader on barcodes.tsv` +> - *"Cell metadata table"*: `Human Cell Atlas Matrix Downloader on exp_design.tsv` +{: .hands_on} + + +>Flagging genes by using AnnData Operations +> +> After you create AnnData file, you can additionally use the {% tool [AnnData Operations](toolshed.g2.bx.psu.edu/repos/ebi-gxa/anndata_ops/anndata_ops/1.8.1+galaxy92) %} tool before downstream analysis. It's quite a useful tool since not only does it flag mitochondrial genes, but also automatically calculates a bunch of metrics, such as `log1p_mean_counts`, `log1p_total_counts`, `mean_counts`, `n_cells`, `n_cells_by_counts`, `n_counts`, `pct_dropout_by_counts`, and `total_counts`. +> +> When you use it to flag mitochondrial genes, here are some formatting tips: +> - Remember to check the name of the column with gene symbols +> - This tool is case sensitive +> - No parentheses needed when typing in the values +> - Including a dash is important to identify mitochondrial genes (eg. **MT-**) +{: .tip} + diff --git a/topics/single-cell/tutorials/alevin-commandline/tutorial.md b/topics/single-cell/tutorials/alevin-commandline/tutorial.md index c27a1bc73542bc..07724140e095d0 100644 --- a/topics/single-cell/tutorials/alevin-commandline/tutorial.md +++ b/topics/single-cell/tutorials/alevin-commandline/tutorial.md @@ -42,6 +42,9 @@ contributions: authorship: - wee-snufkin - nomadscientist + editing: + - pavanvidem + - mtekman testing: - pavanvidem funding: diff --git a/topics/single-cell/tutorials/bulk-music-2-preparescref/tutorial.md b/topics/single-cell/tutorials/bulk-music-2-preparescref/tutorial.md index b674fc1a4eed7d..28a049bab9ca39 100644 --- a/topics/single-cell/tutorials/bulk-music-2-preparescref/tutorial.md +++ b/topics/single-cell/tutorials/bulk-music-2-preparescref/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on subtopic: datamanipulation -priority: 1 +priority: 4 title: Matrix Exchange Format to ESet | Creating a single-cell RNA-seq reference dataset for deconvolution questions: - Where can I find good quality scRNA-seq reference datasets? diff --git a/topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.md b/topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.md index 40f5beef881f3f..4c6039bdda8634 100644 --- a/topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.md +++ b/topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on subtopic: datamanipulation -priority: 2 +priority: 5 title: Bulk matrix to ESet | Creating the bulk RNA-seq dataset for deconvolution zenodo_link: 'https://zenodo.org/record/7319173' diff --git a/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md b/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md index e3233d91dfc6e0..59bb938cd66b17 100644 --- a/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md @@ -118,7 +118,7 @@ You can access the data for this tutorial in multiple ways: REMEMBER TO ADD THE CODE FOR SNIPPETS BACK IN --> -Inspect the {% icon galaxy-eye %} `Experimental Design` text file. This shows you how each `N70X` corresponds to a sample, and whether that sample was from a male or female. This will be important metadata to add to our sample, which we will add very similarly to how you added the `gene_name` and `mito` metadata previously! +{% icon galaxy-eye %} Inspect the {% icon param-file %} `Experimental Design` text file. This shows you how each `N70X` corresponds to a sample, and whether that sample was from a male or female. This will be important metadata to add to our sample, which we will add very similarly to how you added the `gene_name` and `mito` metadata previously! # Important tips for easier analysis diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index f7d2e7d35fccf7..4fcf192ee1644d 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -4,7 +4,7 @@ layout: tutorial_hands_on title: 'Inferring single cell trajectories (Monocle3)' subtopic: single-cell-CS priority: 5 -zenodo_link: 'https://zenodo.org/record/7078524' +zenodo_link: 'https://zenodo.org/records/10397366' redirect_from: - /topics/transcriptomics/tutorials/scrna-case_monocle3-trajectories/tutorial @@ -35,6 +35,7 @@ requirements: - scrna-case_alevin-combine-datasets - scrna-case_basic-pipeline - scrna-case_JUPYTER-trajectories + - scrna-data-ingest tags: - 10x - paper-replication @@ -55,7 +56,7 @@ contributions: This tutorial is a follow-up to the ['Single-cell RNA-seq: Case Study']({% link topics/single-cell/index.md %}). We will use the same sample from the previous tutorials. If you haven’t done them yet, it’s highly recommended that you go through them to get an idea how to [prepare a single cell matrix]({% link topics/single-cell/tutorials/scrna-case_alevin/tutorial.md %}), [combine datasets]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}) and [filter, plot and process scRNA-seq data]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) to get the data in the form we’ll be working on today. -In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Trajectory Analysis using Python (Jupyter Notebook) in Galaxy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that! +In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Inferring Trajectories using Scanpy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that! {% snippet faqs/galaxy/tutorial_mode.md %} @@ -68,14 +69,16 @@ In this tutorial we will perform trajectory analysis using [monocle3](https://co > {: .agenda} -## Get data +# Get data We will continue to work on the case study data from a mouse model of fetal growth restriction {% cite Bacon2018 %} (see [the study in Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/results/tsne) and [the project submission](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6945/)). -Monocle3 works great with annotated data, so we will make use of our annotated AnnData object, generated in the previous [tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}). So you see - all the hard work of processing data was not in vain! We will also need a ‘clean’ expression matrix, extracted from the AnnData object just before we started the processing. -You have two options for uploading these datasets. Importing via history is often faster. + +In the previous tutorials, we first created an AnnData object and performed downstream analysis on that file. However, Monocle3 uses another datatype which is Cell Data Set (CDS). To be able to infer trajectories in Monocle, we need to transform our AnnData object into CDS file. And guess what - we already have a tutorial for that! We did it in [format conversion tutorial]({% link topics/single-cell/tutorials/scrna-data-ingest/tutorial.md %}), in the [Anndata -> Cell Data Set (CDS) subsection]({% link topics/single-cell/tutorials/scrna-data-ingest/tutorial.md %}#anndata---cell-data-set-cds). To better understand the structure of CDS object and learn how to create it from expression matrix, cell and gene annotations, it is highly recommended that you complete the mentioned tutorial before importing the prepared CDS file. + +You have two options for uploading the dataset. Importing via history is often faster. >Option 1: Data upload - Import history > -> 1. Import history from: [input history](https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/monocle3-input-files) +> 1. You can import history where we went from AnnData to CDS file. Then you will also have access to extracted cell metadata, gene metadata, and an expression matrix: [Input history](https://singlecell.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3) > > > {% snippet faqs/galaxy/histories_import.md %} @@ -90,288 +93,49 @@ You have two options for uploading these datasets. Importing via history is ofte > 2. Import the AnnData object from [Zenodo]({{ page.zenodo_link }}) > > ``` -> {{ page.zenodo_link }}/files/AnnData_before_processing.h5ad -> {{ page.zenodo_link }}/files/Annotated_AnnData.h5ad +> https://zenodo.org/records/10397366/files/CDS_input_for_Monocle3_tutorial.rdata > ``` > > {% snippet faqs/galaxy/datasets_import_via_link.md %} > -> 3. Check that the datatype is `h5ad` -> -> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} -> -{: .hands_on} - -# Preparing the input files - -## Extracting annotations - -To run Monocle, we need cell metadata, gene metadata, and an expression matrix file of genes by cells. (In theory, the expression matrix alone could do, but then we wouldn’t have all those useful annotations that we worked on so hard in the previous tutorials!). In order to get these files, we will extract the gene and cell annotations from our AnnData object. - - >-> -> How many lines do you expect to be in the gene and cell metadata files? -> -> > -> > -> > If you click on the step with uploaded annotated AnnData file, you will see on a small preview that this object has 8605 observations and 15395 variables, so we expect to get a cell metadata file with 8605 lines and gene metadata file with 15395 lines (without headers of course!). -> > -> {: .solution} -> -{: .question} - -> Extracting annotations -> -> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.7.5+galaxy1) %} with the following parameters: -> - {% icon param-file %} *"Annotated data matrix"*: `Annotated_AnnData` -> - *"What to inspect?"*: `Key-indexed observations annotation (obs)` -> 2. Rename {% icon galaxy-pencil %} the observations annotation `Extracted cell annotations (obs)` -> -> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.7.5+galaxy1) %} with the following parameters: -> - {% icon param-file %} *"Annotated data matrix"*: `Annotated_AnnData` -> - *"What to inspect?"*: `Key-indexed annotation of variables/features (var)` -> -> 4. Rename {% icon galaxy-pencil %} the annotation of variables `Extracted gene annotations (var)` -> -> {: .hands_on} -Quick and easy, isn’t it? However, we need to make some minor changes before we can input these files into the Monocle toolsuite. -## Cell metadata -Our current dataset is not just T-cells: as you might remember from the last tutorial, we identified a cluster of macrophages as well. This might be a problem, because the trajectory algorithm will try to find relationships between all the cells (even if they are not necessarily related!), rather than only the T-cells that we are interested in. We need to remove those unwanted cell types to make the analysis more accurate. - -The Manipulate AnnData tool allows you to filter observations or variables, and that would be the most obvious way to remove those cells. However, given that we don't need an AnnData object, it's a lot quicker to edit a table rather than manipulate an AnnData object. Ultimately, we need cell metadata, gene metadata and expression matrix files that have macrophages removed, and that have the correct metadata that Monocle looks for. With some table manipulation, we’ll end up with three separate files, ready to be passed onto Monocle3. - - >-> -> Where is the information about cell types stored? +> Input files for Monocle3 > -> >-> > -> > We have already extracted the cell annotations file - in one of the columns you can find the information about cell type, assigned to each cell. -> >  -> > -> {: .solution} +> Monocle3 turns the expression matrix, cell and gene annotations into an object called cell_data_set (CDS), which holds single-cell expression data. We provided you with the CDS file already, but it was created by combining the three mentioned elements. Check out [data conversion tutorial]({% link topics/single-cell/tutorials/scrna-data-ingest/tutorial.md %}#anndata---cell-data-set-cds) to see how to do it! +> Here is what [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/starting/) says about the three input files required to create a CDS object: +> - **expression_matrix**: a numeric matrix of expression values, where rows are genes, and columns are cells. Must have the same number of columns as the cell_metadata has rows and the same number of rows as the gene_metadata has rows. +> - **cell_metadata**: a data frame, where rows are cells, and columns are cell attributes (such as cell type, culture condition, day captured, etc.) +> - **gene_metadata**: a data frame, where rows are features (e.g. genes), and columns are gene attributes, such as biotype, gc content, etc. One of its columns should be named "gene_short_name", which represents the gene symbol or simple name (generally used for plotting) for each gene. > -{: .question} - -Click on `Extracted cell annotations (obs)` file to see a small preview window. This shows you that the column containing the cell types has number 22. We’ll need that to filter out unwanted cell types! - -> Check the column number! -> If you are working on a different dataset, the number of the ‘cell_type’ column might be different, so make sure you check it on a preview and use the correct number! -{: .warning} - ->Filter out macrophages -> -> 1. {% tool [Filter](Filter1) %} with the following parameters: -> - {% icon param-file %} *"Filter"*: `Extracted cell annotations (obs)` -> - *"With following condition"*: `c22!='Macrophages'` -> - *"Number of header lines to skip"*: `1` -> - That’s it - our cell annotation file is ready for Monocle! Let’s rename it accordingly. -> 2. **Rename** {% icon galaxy-pencil %} the output: `Cells input data for Monocle3` -> -> >Parameters -> > -> > - `c22` means column no. 22 - that's the column with cell types, and it will be filtered for the macrophages -> > - `!=` means 'not equal to' - we want to keep the cell types which ARE NOT macrophages -> {: .details} -> -> >Other unwanted cell types -> > -> > It might happen that during clustering you’ll find another cell type that you want to get rid of for the trajectory analysis. Then simply re-run this tool on already filtered file and change ‘Macrophages’ to another unwanted cell type. -> {: .tip} -{: .hands_on} +{: .details} -## Gene annotations -Sometimes certain functionalities require a specific indication of where the data should be taken from. Monocle3 tools expect that the genes column is named ‘gene_short_name’. Let's check what the name of that column is in our dataset currently. +Once you get the CDS file in your history, let's have a closer look at this dataset to understand it a little bit better. >> -> 1. Where can you check the header of a column containing genes names? -> 2. What is the name of this column? +> What are the dimensions of the created CDS object? > > > > > -> > 1. Our extracted gene annotations file! Either by clicking on the eye icon {% icon solution %} or having a look at the small preview window. -> > 2. In our dataset the gene names are stored in a column called ‘Symbol’ - we need to change that! -> >  -> > -> {: .solution} -> -{: .question} - -Let’s click on the `Extracted gene annotations (var)` file to see a small preview. We can see that the gene names are in the third column with a header `Symbol`. Keep that in mind - we’ll use that in a second! - -> Changing the column name -> -> 1. {% tool [Column Regex Find And Replace](toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.2) %} with the following parameters: -> - {% icon param-file %} *"Select cells from"*: `Extracted gene annotations (var)` -> - *"using column"*: `c3` or `Column: 3` -> - In *"Check"*: -> - {% icon param-repeat %} *"Insert Check"* -> - *"Find Regex"*: `Symbol` -> - *"Replacement"*: `gene_short_name` -> 2. Check that the datatype is `tabular` -> -> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %} -> - Voila! That’s the gene input for Monocle! Just a quick rename... -> 3. **Rename** {% icon galaxy-pencil %} the output: `Genes input data for Monocle3` -> -{: .hands_on} - -## Expression matrix -Last, but not least! And in fact, the most important! The expression matrix contains all the values representing the expression level of a particular gene in a cell. This is why in theory the expression matrix is the only input file required by Monocle3. Without annotation files the CDS data can still be generated - it will be quite bare and rather unhelpful for interpretation, but it's possible to process. - -So, the values in the expression matrix are just numbers. But do you remember that we have already done some processing such as normalisation and the calculation of principal components in the AnnData object in the previous tutorial? That affected our expression matrix. Preprocessing is one of the steps in the Monocle3 workflow, so we want to make sure that the calculations are done on a ‘clean’ expression matrix. If we apply too many operations on our raw data, it will be too ‘deformed’ to be reliable. The point of the analysis is to use algorithms that make the enormous amount of data understandable in order to draw meaningful conclusions in accordance with biology. - -So how do we do that? ->-> -> 1. How many cells and genes are there in the `Anndata_before_processing` file? -> 2. How many lines are there in `Cells input data for Monocle3`? -> 3. How many lines are there in `Genes input data for Monocle3`? -> -> > -> > You can answer all the questions just by clicking on the given file and looking at the preview window. -> > 1. [n_obs x n_vars] = 31178 x 35734, so there are 31178 cells and 35734 genes. -> > 2. 8570 lines, including a header, which makes 8569 cells. -> > 3. 15396 lines, including a header, which makes 15395 genes. +> > Just click on that dataset - on the preview you’ll see that the dimensions are 15395 x 8569 - so exactly as we predicted genes x cells! +> >  > > > {: .solution} -> {: .question} -As you can see, there are way more genes and cells in the unprocessed AnnData file, so the expression matrix is much bigger than we need it to be. If the genes and cells we prepared for Monocle3 are not the same as in the expression matrix, Monocle3 will crash. Therefore, we have to filter that big, clean matrix and adjust it to our already prepared genes and cells files. But first, let’s extract the matrix from the unprocessed AnnData object. - -> Extracting matrix -> -> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.7.5+galaxy1) %} with the following parameters: -> - {% icon param-file %} *"Annotated data matrix"*: `AnnData_before_processing` -> - *"What to inspect?"*: `The full data matrix` -> 2. **Rename** {% icon galaxy-pencil %} the output: `Unprocessed expression matrix` -> -{: .hands_on} - -If you have a look at the preview of `Unprocessed expression matrix`, you’ll see that the first column contains the cell barcodes, while the first row - the gene IDs. We would like to keep only the values corresponding to the cells and genes that are included in `Cells input data for Monocle3` and `Genes input data for Monocle3`. How do we do it? First, we compare the cell barcodes from `Cells input data for Monocle3` to those in `Unprocessed expression matrix` and ask Galaxy to keep the values of the matrix for which the barcodes in both files are the same. Then, we’ll do the same for gene IDs. We will cut the first columns from `Cells input data for Monocle3` and `Genes input data for Monocle3` to be able to compare those columns side by side with the matrix file. - ->Cutting out the columns -> -> 1. {% tool [Cut](Cut1) %} with the following parameters: -> - *"Cut columns"*: `c1` -> - {% icon param-file %} *"From"*: `Cells input data for Monocle3` -> 2. **Rename** {% icon galaxy-pencil %} the output: `Cells IDs` -> 3. {% tool [Cut](Cut1) %} with the following parameters: -> - *"Cut columns"*: `c1` -> - {% icon param-file %} *"From"*: `Genes input data for Monocle3` -> 4. **Rename** {% icon galaxy-pencil %} the output: `Genes IDs` -> -{: .hands_on} - ->Filter matrix values by cell barcodes -> -> 1. {% tool [Join two Datasets](join1) %} with the following parameters: -> - {% icon param-file %} *"Join"*: `Cells IDs` -> - *"using column"*: `c1`or `Column: 1` -> - {% icon param-file %} *"with"*: `Unprocessed expression matrix` -> - *"and column"*: `c1`or `Column: 1` -> - *"Keep lines of first input that do not join with second input"*: `Yes` -> - *"Keep lines of first input that are incomplete"*: `Yes` -> - *"Fill empty columns"*: `No` -> - *"Keep the header lines"*: `Yes` -> 2. **Rename** {% icon galaxy-pencil %} the output: `Pre-filtered matrix (by cells)` -> -{: .hands_on} - -Look at the preview of the output file. First of all, you can see that there are 8570 lines (8569 cells) instead of 31178 cells that were present in the matrix. That’s exactly what we wanted to achieve - now we have raw information for the T-cells that we have filtered. However, the step that we have already performed left us with the matrix whose first and second columns are the same - let’s get rid of one of those! - ->Remove duplicate column (cells IDs) -> -> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0) %} with the following parameters: -> - {% icon param-file %} *"File to cut"*: `Pre-filtered matrix (by cells)` -> - *"Operation"*: `Discard` -> - *"Cut by"*: `fields` -> - *"List of Fields"*: `c1` -> 2. **Rename** {% icon galaxy-pencil %} the output: `Filtered matrix (by cells)` -> -{: .hands_on} - -Now we will perform the same steps, but for gene IDs. But gene IDs are currently in the first row, so we need to transpose the matrix, and from there we can repeat the same steps as above for Gene IDs. - ->Filter matrix by gene IDs -> -> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.1.0+galaxy2) %} with the following parameters: -> - {% icon param-file %} *"Input tabular dataset"*: `Filtered matrix (by cells)` -> - The matrix is now ready to be filtered by gene IDs! -> 2. {% tool [Join two Datasets](join1) %} with the following parameters: -> - {% icon param-file %} *"Join"*: `Genes IDs` -> - *"using column"*: `c1` or `Column: 1` -> - {% icon param-file %} *"with"*: output of **Transpose** {% icon tool %} -> - *"and column"*: `c1` or `Column: 1` -> - *"Keep lines of first input that do not join with second input"*: `Yes` -> - *"Keep lines of first input that are incomplete"*: `Yes` -> - *"Fill empty columns"*: `No` -> - *"Keep the header lines"*: `Yes` -> 3. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0) %} with the following parameters: -> - {% icon param-file %} *"File to cut"*: output of **Join two Datasets** {% icon tool %} -> - *"Operation"*: `Discard` -> - *"Cut by"*: `fields` -> - *"List of Fields"*: `c1` -> - Monocle3 requires that in the matrix rows are genes, and columns are cells - that is what we've got, so there is no need to transpose matrix again. The expression matrix is ready! Let's just rename it... -> 4. **Rename** {% icon galaxy-pencil %} the output: `Expression matrix for Monocle3` -> -{: .hands_on} - -{% icon congratulations %} Finally! We have prepared all the files to pass them onto the Monocle3 workflow! # Monocle3 workflow -Monocle3 turns the expression matrix, cell and gene annotations into an object called cell_data_set (CDS), which holds single-cell expression data. - ->Input files -> -> Here is what [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/starting/) says about the required three input files: -> - **expression_matrix**: a numeric matrix of expression values, where rows are genes, and columns are cells. Must have the same number of columns as the cell_metadata has rows and the same number of rows as the gene_metadata has rows. -> - **cell_metadata**: a data frame, where rows are cells, and columns are cell attributes (such as cell type, culture condition, day captured, etc.) -> - **gene_metadata**: a data frame, where rows are features (e.g. genes), and columns are gene attributes, such as biotype, gc content, etc. One of its columns should be named "gene_short_name", which represents the gene symbol or simple name (generally used for plotting) for each gene. -> -{: .details} - The Monocle3 workflow looks like the following, which should seem pretty similar to what you've done throughout the case study.  We will follow those steps and see how it all works in practice. ->Create CDS object -> -> >Data format -> > -> > You can provide expression matrix as TSV, CSV, MTX or RDS file, while genes and cells metadata as TSV, CSV or RDS files. In our case all three files are tabular, so we will set the format to TSV. -> {: .details} -> 1. {% tool [Monocle3 create](toolshed.g2.bx.psu.edu/repos/ebi-gxa/monocle3_create/monocle3_create/0.1.4+galaxy2) %} with the following parameters: -> - {% icon param-file %} *"Expression matrix, genes as rows, cells as columns. Required input. Provide as TSV, CSV or RDS."*: `Expression matrix for Monocle3` -> - *"Format of expression matrix"*: `TSV` -> - {% icon param-file %} *"Per-cell annotation, optional. Row names must match the column names of the expression matrix. Provide as TSV, CSV or RDS."*: `Cells input data for Monocle3` -> - *"Format of cell metadata"*: `TSV` -> - {% icon param-file %} *"Per-gene annotation, optional. Row names must match the row names of the expression matrix. Provide as TSV, CSV or RDS."*: `Genes input data for Monocle3` -> - *"Format of gene annotation"*: `TSV` -> -{: .hands_on} - ->-> -> What are the dimensions of the created CDS object? -> -> > -> > -> > Just click on the performed step - on the preview you’ll see that the dimensions are 15395 x 8569 - so exactly as we predicted genes x cells! -> >  -> > -> {: .solution} -{: .question} -## Pre-processing +# Pre-processing In Galaxy, there are currently 2 methods of initial dimensionality reduction included in the pre-processing step: principal component analysis (PCA) and latent semantic indexing (LSI). Given that PCA is more commonly used, and it allows us to perform further steps on the CDS object, we’ll use this method. There is one parameter here that has a great impact on how our analysis will look - the `dimensionality of the initially reduced space`. This is a highly subjective choice - you will want to test a lot of different parameters on your dataset. After much trial and error, we were able to find the value that made the most sense biologically. Have a look at the image below to see how different values affect the outcomes. @@ -397,7 +161,7 @@ Given that PCA is more commonly used, and it allows us to perform further steps > {: .hands_on} -## Dimensionality reduction +# Dimensionality reduction Now it’s time for the proper dimensionality reduction, to turn the original thousands of dimensions (genes), into a 2-dimensional graph. There are several algorithms to do this: UMAP, tSNE, PCA and LSI (only possible when preprocess_method is set to `LSI`), but due to the same reasons as above, we’ll use UMAP (most common + allows further operations + best results). But I’ll let you see how the outputs from the other algorithms look to convince you that **UMAP** is indeed the best for this dataset. Of course, it's possible that by choosing different pre-processing values, tSNE or PCA plots would look better, so don't be afraid to play around with the parameters and test them! @@ -410,7 +174,7 @@ Now it’s time for the proper dimensionality reduction, to turn the original th > {: .hands_on} -## Plotting +# Plotting Alright, now let's have a look at our output! Above you got a sneak peek of how the plot would look, but now you’ll generate the plots on your own! @@ -536,7 +300,7 @@ If we compare the annotated cell types and the clusters that were just formed, w  -## Gene expression +# Gene expression We haven't looked at gene expression yet! This step is particularly important when working with data which is not annotated. Then, based on the expression of marker genes, you are able to identify which clusters correspond to which cell types. This is indeed what we did in the previous tutorial using scanpy. We can do the same using Monocle3! Since we work on annotated data, we can directly check if the expressed genes actually correspond to the previously assigned cell types. If they do, that’s great - if two different methods are consistent, that gives us more confidence that our results are valid. Below is the table that we used in the previous tutorial to identify the cell types. @@ -584,7 +348,7 @@ Below is the table that we used in the previous tutorial to identify the cell ty > {: .tip} -## Top marker genes +# Top marker genes Here we used a priori knowledge regarding the marker genes. If we wanted to approach this problem in an unsupervised manner, we could use Monocle to tell us the top marker genes in each group of cells. This is very useful if we are trying to identify a cell type, or if we want to find novel marker genes for known cell types. @@ -628,7 +392,7 @@ Here we used a priori knowledge regarding the marker genes. If we wanted to appr But what if you want to know how gene expression changes across a trajectory? This is where Monocle is particularly powerful. But in order to do that, we have to infer that trajectory first! -## Learn the trajectory graph +# Learn the trajectory graph We’re getting closer and closer! The next step is to learn the trajectory graph, which means to fit a principal graph within each partition. In that way, we’ll ‘connect’ the existing clusters by creating a path between them. @@ -657,7 +421,7 @@ There are many trajectory patterns: linear, cycle, bifurcation, tree and so on. >  {: .tip} -## Pseudotime analysis +# Pseudotime analysis Finally, it's time to see our cells in pseudotime! We have already learned a trajectory, now we only have to order the cells along it. Monocle3 requires information on where to start ordering the cells, so we need to provide it with this information. We annotated early T-cells as double negative (DN), so those will be our root cells! @@ -715,9 +479,9 @@ Last but not least, you can now identify genes that define the inferred trajecto # Conclusion -{% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/monoce3-tutorial-workflow), or check out the [full workflow](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-anndata-to-monocle-right-1) for this tutorial. I also split this workflow into two separate workflows: [preparing the input files for Monocle3, starting from AnnData](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3), and [Monocle3 only workflow](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3-1). You can use them to accelerate analysis of your own data, paying attention to the requirements of the input data that are mentioned in this tutorial. +{% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/monoce3-tutorial-workflow) (which also includes AnnData to CDS conversion), and check out the [workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3-1) for this tutorial. There is also a separate workflow for [preparing the input files for Monocle3, starting from AnnData](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3), and [full analysis workflow](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-anndata-to-monocle-right-1). You can use them to accelerate analysis of your own data, paying attention to the requirements of the input data that are mentioned in this tutorial. - + If you're following the Case Study tutorials from the beginning, you have already experienced what it’s like to analyse and question a dataset, potentially without clear cut-offs or clear answers. You now know that trajectory analysis is even more sensitive to parameter values, so it's often trying to find the best set of values that would give the most reasonable results and go in accordance with biology. Moreover, not all trajectory analysis methods are designed to infer all kinds of biological processes - due to the fact that they use different algorithms, some would work better for analysing your sample. Since Monocle is quite widely used for trajectory analysis, it might be a good practice to compare its results with other methods. The more evidence you have to confirm your findings, the more confident you can be about their reliability! diff --git a/topics/single-cell/tutorials/scrna-data-ingest/faqs/index.md b/topics/single-cell/tutorials/scrna-data-ingest/faqs/index.md new file mode 100644 index 00000000000000..9ce3fe4fce824b --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/faqs/index.md @@ -0,0 +1,3 @@ +--- +layout: faq-page +--- diff --git a/topics/single-cell/tutorials/scrna-data-ingest/tutorial.bib b/topics/single-cell/tutorials/scrna-data-ingest/tutorial.bib new file mode 100644 index 00000000000000..e741f7f5e3ea45 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/tutorial.bib @@ -0,0 +1,34 @@ + + +@article{Trapnell2014, + doi = {10.1038/nbt.2859}, + url = {https://doi.org/10.1038/nbt.2859}, + year = {2014}, + month = mar, + publisher = {Springer Science and Business Media {LLC}}, + volume = {32}, + number = {4}, + pages = {381--386}, + author = {Cole Trapnell and Davide Cacchiarelli and Jonna Grimsby and Prapti Pokharel and Shuqiang Li and Michael Morse and Niall J Lennon and Kenneth J Livak and Tarjei S Mikkelsen and John L Rinn}, + title = {The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells}, + journal = {Nature Biotechnology} +} + +@article{Bacon2018, + doi = {10.3389/fimmu.2018.02523}, + url = {https://doi.org/10.3389/fimmu.2018.02523}, + year = {2018}, + month = nov, + publisher = {Frontiers Media {SA}}, + volume = {9}, + author = {Wendi A. Bacon and Russell S. Hamilton and Ziyi Yu and Jens Kieckbusch and Delia Hawkes and Ada M. Krzak and Chris Abell and Francesco Colucci and D. Stephen Charnock-Jones}, + title = {Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted Neonatal Mice}, + journal = {Frontiers in Immunology} +} + +@online{monocle3, + author = {cole-trapnell-lab}, + title = {monocle3}, + url = {https://cole-trapnell-lab.github.io/monocle3/}, + urldate = {2022-09-23} +} diff --git a/topics/single-cell/tutorials/scrna-data-ingest/tutorial.md b/topics/single-cell/tutorials/scrna-data-ingest/tutorial.md new file mode 100644 index 00000000000000..fc4729edd85ac1 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/tutorial.md @@ -0,0 +1,947 @@ +--- +layout: tutorial_hands_on +subtopic: datamanipulation +priority: 1 +title: Converting between common single cell data formats +questions: +- What are the most popular single cell datatypes? +- What if the format of my files is different than that used in the tutorial I want to follow? +- Where should I start the analysis, depending on the format of my data? +- How do I ingest data into Galaxy? +- How do I convert datasets between formats? +objectives: +- You will identify different single cell files formats. +- You will import single cell data into Galaxy using different methods. +- You will manipulate the metadata and matrix files. +- You will perform conversions between the most common single cell formats. +- You will downsample FASTQ files. +time_estimation: 1H +key_points: +- Single cell data from different sources may have unfamiliar formatting and thus may require different ways of ingesting it into Galaxy. +- There are many ways of importing single cell files into Galaxy and converting between single cell formats. +contributions: + authorship: + - wee-snufkin + - hexhowells + - nomadscientist + editing: + - hexylena + testing: + - pavanvidem + - mtekman + funding: + - elixir-fair-data +zenodo_link: 'https://zenodo.org/record/4574153' +follow_up_training: + - + type: "internal" + topic_name: single-cell +tags: + - data management + - data import +--- + +You finally decided to analyse some single cell data, you got your files either from the lab or publicly available sources, you opened the first tutorial available on Galaxy Training Network and... you hit the wall! The format of your files is not compatible with the one used in tutorial! Have you been there? +This tutorial was created to help you overcome that problem and ensure data interoperability in single cell analysis. Once you get your data into Galaxy in the right format, that's already 50% of success. Additionally, by using format conversion, you will be able to use different packages presented in tutorials that may require different datatypes. + +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Single cell datatypes + +To start with, here are the most common formats and datatypes that you might come across if you work with single cell data: +- **Tabular** - simply using TSV, CSV or TXT formats to store expression matrix as well as cell and gene metadata. +- **MTX** - it's just a sparse matrix format with genes on the rows and cells on the columns as output by Cell Ranger. +- **HDF5** - Hierarchical Data Format - can store datasets and groups. A dataset is a a multidimensional array of data elements, together with supporting metadata. A group is a structure for organizing objects in an HDF5 file. This format allows for storing both the count matrices and all metadata in a single file rather than having separate features, barcodes and matrix files. +- **AnnData objects** - [anndata](https://anndata.readthedocs.io/en/latest/) is a Python package for handling annotated data matrices. In Galaxy, you'll see AnnData objects in **h5ad** format, which is based on the standard HDF5 (h5) format. There are lots of Python tools that work with this format, such as Scanpy, MUON, Cell Oracle, SquidPy, etc. +- **Loom** - it is simply an HDF5 file that contains specific groups containing the main matrix as well as row and column attributes and can be read by any language supporting HDF5. [Loompy](https://linnarssonlab.org/loompy/) has been released as a Python API to interact with loom files, and [loomR](https://github.com/mojaveazure/loomR) is its implementation in R. +- **Zarr** - a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. The Zarr file format offers powerful compression options, supports multiple data store backends, and can read/write your NumPy arrays. +- **Seurat objects** - a representation of single-cell expression data for R, in Galaxy you might see them in **rdata** format. +- **Single Cell Experiment (SCE) object** - defines a S4 class for storing data from single-cell experiments and provides a more formalized approach towards construction and accession of data. The S4 system is one of R's systems for object oriented programing. In Galaxy you might see SCE objects in **rdata** format. +- **CellDataSet (CDS) object** - the main class used by Monocle to hold single cell expression data. In Galaxy you might see CDS objects in **rdata** format. + + + +{% snippet faqs/galaxy/tutorial_mode.md %} + +# Data import +As you can see above, there are multiple ways to store single cell data. Therefore, there are also many ways how you can get that data! +Obviously, before any format conversion, we need to import the data. In our tutorials we often use [Zenodo](https://zenodo.org/) links, but you can also upload the files directly from your computer. There are also publicly available resources which you can easily access through public atlases, such as [Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home) or [Human Cell Atlas data portal](https://data.humancellatlas.org/). We created a [dedicated tutorial]({% link topics/single-cell/tutorials/EBI-retrieval/tutorial.md %}) to show how to use those atlases to retrieve data. But today we're here to focus on data conversion! + +# SCEasy Tool +In Galaxy Toolshed there is a wonderful tool called {% tool [SCEasy](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} which allows you to convert between common single cell formats, such as: +- AnnData to CellDataSet (CDS) +- AnnData to Seurat +- Loom to AnnData +- Loom to SingleCellExperiment (SCE) +- SingleCellExperiment (SCE) to AnnData +- SingleCellExperiment (SCE) to Loom +- Seurat to AnnData +- Seurat to SingleCellExperiment (SCE) + +> Two SCEasy tools +> +> As of the writing of this tutorial, the updated SCEasy tool is called {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} and it's only available on *usegalaxy.eu*. The second tool is called {% tool [SCEasy convert](toolshed.g2.bx.psu.edu/repos/ebi-gxa/sceasy_convert/sceasy_convert/0.0.5+galaxy1) %} and it works on *usegalaxy.org*, however has limited conversion options. Both tools should be visible on *singlecell.usegalaxy.eu* if you try to find them in the search box. +> +{: .warning} + +In this tutorial you will see multiple examples of SCEasy Tool in action. However, sometimes it is useful to know how to do this conversion manually or at least to know how it all works and better understand the structure of the files. Therefore, we also have examples showing how to convert objects manually or prepare the input files for some of our single cell tutorials and workflows. + +# Seurat -> AnnData +As mentioned above, there are two SCEasy tools currently available. When we speak about conversion to AnnData, the latest tool {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} converts Seurat into the latest version of AnnData object. However, currently it is not possible to use that output with downstream EBI single-cell tools because they only support older versions of the AnnData. If you need to use those tools, the solution is to take advantage of the second SCEasy tool {% tool [SCEasy convert](toolshed.g2.bx.psu.edu/repos/ebi-gxa/sceasy_convert/sceasy_convert/0.0.5+galaxy1) %} which has limited conversion options but can generate AnnData files compatible with the EBI single-cell tools that we are going to use next in the workflow. +Our Seurat starting file was generated by pulling data from [Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home) and transforming it into the desired format, what was shown in the [data import tutorial]({% link topics/single-cell/tutorials/EBI-retrieval/tutorial.md %}). It will be our toy dataset. + +>Get toy data +> +> 1. Create a new history for this tutorial +> 2. Import the AnnData object from [Zenodo](https://zenodo.org/records/10397653) +> +> ``` +> https://zenodo.org/records/10397653/files/Seurat_object.rdata +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> +> 3. Alternatively, you can import history where we created the Seurat object: [Input history](https://singlecell.usegalaxy.eu/u/j.jakiela/h/ebi-scxa---anndata-scanpy-or-seurat-object-1) +> +> {% snippet faqs/galaxy/histories_import.md %} +> +> 4. **Rename** {% icon galaxy-pencil %} the dataset `Seurat object` +> 5. Check that the datatype is `rdata` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="rdata" %} +> +{: .hands_on} + + +{% include _includes/cyoa-choices.html option1="Quick one tool method" option2="Convert to AnnData object compatible with Filter, Plot, Explore workflow" default="Quick one tool method" + text="Choose below if you just want to convert your object quickly or see how it all happens behind the scenes!" disambiguation="seurat2anndata" %} + ++ +## Quick one tool method + +>+ +SCEasy Converter +> +> 1. {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} with the following parameters: +> - *"Convert From / To"*: `Seurat to AnnData` +> - {% icon param-file %} *"Input object in rds,rdata format"*: `Seurat object` +> 2. **Rename** {% icon galaxy-pencil %} the output: `AnnData object` +> +{: .hands_on} + +And that's it! Please note that the output file is the newest AnnData version and is not compatible with EBI tools used in [Filter, Plot, Explore workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/copy-of-filter-plot-and-explore-single-cell-rna-seq-data-imported-from-uploaded-file-3). + ++ +## Convert to AnnData object compatible with Filter, Plot, Explore workflow + +Here, we use the other SCEasy tool which generates older version of the AnnData which is compatible with the EBI single-cell tools that we are going to use next in the workflow. + +>+ +# AnnData -> Seurat + +Let's get an **AnnData object** that we can further work on. It's the object used in [many tutorials]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}), so check it out if you're curious. + +>Seurat to AnnData with SCEasy convert +> +> 1. {% tool [SCEasy convert](toolshed.g2.bx.psu.edu/repos/ebi-gxa/sceasy_convert/sceasy_convert/0.0.5+galaxy1) %} with the following parameters: +> - *"Direction of conversion"*: `Seurat to AnnData` +> - {% icon param-file %} *"Input object in Seurat RDS format"*: `Seurat object` +> (if the dataset does not show up in the corresponding input field, just drag the dataset from the history panel and drop into the input field) +> - *"Name of the assay to be transferred"*: `RNA` +> - *"Data type of the assay to be transferred"*: `data` +> +> 2. **Rename** {% icon galaxy-pencil %} the output `Converted EBI-compatible AnnData file`. +> +> 3. Check that the datatype is `h5ad` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} +> +{: .hands_on} + +Now, if we do small modification to metadata, we can use the generated dataset as an input file in the workflow we created in the [previous tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %})! Let's see how it works: + +>Modify AnnData object +> +> 1. {% tool [AnnData Operations](toolshed.g2.bx.psu.edu/repos/ebi-gxa/anndata_ops/anndata_ops/1.8.1+galaxy92) %} +> 2. Make sure you are using version **1.8.1+galaxy92** of the tool (change by clicking on {% icon tool-versions %} Versions button) +> 3. Set the following parameters: +> - {% icon param-file %} In *"Input object in hdf5 AnnData format"*: `Converted EBI-compatible AnnData file` +> - In *"Change field names in AnnData var"*: +> - {% icon param-repeat %} *"Insert Change field names in AnnData var"* +> - *"Original name"*: `name` +> - *"New name"*: `Symbol` +> - *"Gene symbols field in AnnData"*: `Symbol` +> - In *"Flag genes that start with these names"*: +> - {% icon param-repeat %} *"Insert Flag genes that start with these names"* +> - *"Starts with"*: `mt-` +> - *"Var name"*: `mito` +> 4. Rename {% icon galaxy-pencil %} output `AnnData for Filter, Plot, Explore workflow` +> +{: .hands_on} + +The object is ready to start its journey through the [Filter, Plot, Explore workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/copy-of-filter-plot-and-explore-single-cell-rna-seq-data-imported-from-uploaded-file-3). All thanks to one conversion tool - how awesome that is! + +Get toy data +> +> 1. Create a new history for this tutorial +> 2. Import the AnnData object from [Zenodo]({{ page.zenodo_link }}) +> +> If you do this tutorial just for learning purposes, you can download the downsampled dataset which will be much quicker to process: +> +> ``` +> https://zenodo.org/record/10391629/files/Downsampled_annotated_AnnData.h5ad +> ``` +> +> If you want to use the full dataset used in the other single-cell case study tutorials, here it is! Please note, it will take much longer to process it, so we will only show the conversions on the downsampled objects. +> +> ``` +> https://zenodo.org/record/7053673/files/Mito-counted_AnnData +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> +> 4. **Rename** {% icon galaxy-pencil %} the datasets `Downsampled AnnData object` +> 5. Check that the datatype is `h5ad` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} +> +{: .hands_on} + + +{% include _includes/cyoa-choices.html option1="Quick one step method" option2="Manual conversion" default="Quick one step method" + text="Choose below if you just want to convert your object quickly or see how it all happens behind the scenes!" disambiguation="anndata2seurat" %} + ++ +## Quick one step method + +>+ +SCEasy Converter +> +> 1. {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} with the following parameters: +> - *"Convert From / To"*: `AnnData to Seurat` +> - {% icon param-file %} *"Input object in h5ad,h5 format"*: `Downsampled AnnData object` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Seurat object` +> +{: .hands_on} + ++ +## Manual conversion + +Most of our manual conversions involve extracting tables from different data objects and importing them into the target object. +First, we will extract observations (cell metadata) and the full matrix from our AnnData. + +>+ +# AnnData -> SingleCellExperiment (SCE) +We will work on the same AnnData object so if you create a new history for this exercise, you can either get this file from Zenodo again or just copy this dataset from the previous history. + +>Inspect AnnData +> +> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `Key-indexed observations annotation (obs)` +> +> 2. **Rename** {% icon galaxy-pencil %} the output `Observations`. +> +> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `The full data matrix` +> +> 4. **Rename** {% icon galaxy-pencil %} the output `Matrix`. +> +{: .hands_on} + + +>+> +> What are the rows, and what are the columns in the retrieved Matrix? +> +> > +> > +> > If you just click on the `Matrix` dataset, you will see a preview showing barcodes in the first column, and genes in the first row. +> > +> {: .solution} +> +{: .question} + +However, the next tool we need expects a matrix wherein the genes are listed in the first column and the barcodes are listed in the first row. Therefore, we need to transpose the current matrix. + +> Transpose the matrix +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `Matrix` +> +{: .hands_on} + +And now we are ready to input that data into **DropletUtils** tool, which will separate this matrix into the cells, genes, and matrix tabular files needed to build a Seurat object. + +>DropletUtils +> +> 1. {% tool [DropletUtils](toolshed.g2.bx.psu.edu/repos/iuc/dropletutils/dropletutils/1.10.0+galaxy2) %} with the following parameters: +> - *"Format for the input matrix"*: `Tabular` +> - {% icon param-file %} *"Count Data"*: output of **Transpose** {% icon tool %} +> - *"Operation"*: `Filter for Barcodes` +> - *"Method"*: `DefaultDrops` +> - *"Expected Number of Cells"*: `338` +> - *"Upper Quantile"*: `1.0` +> - *"Lower Proportion"*: `0.0` +> - *"Format for output matrices"*: `Bundled (barcodes.tsv, genes.tsv, matrix.mtx)` +> - *"Random Seed"*: `100` +> +{: .hands_on} + +Finally, let's combine those files that we have just generated and turn them into the Seurat object! + +>Create Seurat object +> +> 1. {% tool [Seurat Read10x](toolshed.g2.bx.psu.edu/repos/ebi-gxa/seurat_read10x/seurat_read10x/3.2.3+galaxy0) %} with the following parameters: +> - *"Choose the format of the input"*: `10X-type MTX` +> - {% icon param-file %} *"Expression matrix in sparse matrix format (.mtx)"*: `DropletUtils 10X Matrices` +> - *"Gene table"*: `DropletUtils 10X Genes` +> - *"Barcode/cell table"*: `DropletUtils 10X Barcodes` +> - {% icon param-file %} *"Cell Metadata"*: `Observations` +> - *"Choose the format of the output"*: `RDS with a Seurat object` +> +> 2. **Rename** {% icon galaxy-pencil %} the output `Converted Seurat object`. +> +{: .hands_on} + +As usual, you can check the [example history](https://usegalaxy.eu/u/j.jakiela/h/anndata-to-seurat-conversion) and the dedicated [workflow](https://usegalaxy.eu/u/j.jakiela/w/anndata---seurat-conversion). + +Get toy data +> +> 1. Create a new history for this section +> 2. Import the files from [Zenodo](https://zenodo.org/record/10391629) +> +> ``` +> https://zenodo.org/record/10391629/files/Downsampled_annotated_AnnData.h5ad +> ``` +> +{: .hands_on} + +{% snippet faqs/galaxy/histories_copy_dataset.md %} + + +{% include _includes/cyoa-choices.html option1="Quick single tool method" option2="Convert manually" default="Quick single tool method" + text="Choose below if you just want to convert your object quickly or see how it all happens behind the scenes!" disambiguation="anndata2sce" %} + ++ +## Quick single tool method + +>+ +SCEasy Converter +> +> 1. {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} with the following parameters: +> - *"Convert From / To"*: `AnnData to Seurat` +> - {% icon param-file %} *"Input object in h5ad,h5 format"*: `Downsampled AnnData object` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Seurat object` +> 3. We will use SCEasy again! {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} with the following parameters: +> - *"Convert From / To"*: `Seurat to SingleCellexperiment` +> - {% icon param-file %} *"Input object in rds,rdata format"*: `Seurat object` +> 4. **Rename** {% icon galaxy-pencil %} the output: `SCE object` +> +{: .hands_on} + ++ +## Convert manually + +First, we will extract observations and the full matrix from our AnnData. + +>+ +# Anndata -> Cell Data Set (CDS) +Cell Data Set (CDS) format is usually used when working with a package called Monocle3 ({% cite monocle3 %}). Below we show two methods on how to transform AnnData to CDS object, one of which creates an input file for [Monocle 3 tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). + +{% include _includes/cyoa-choices.html option1="Create CDS input for Monocle3 tutorial" option2="General AnnData to CDS conversion" default="General AnnData to CDS conversion" + text="You can choose whether you want just to transform AnnData to CDS or to create CDS input file for Monocle3 tutorial to proceed with the downstream analysis described in that tutorial. Please note that depending on your dataset, you might need to refer to the first method which uses both annotated and unprocessed matrices. If you did some pre-processing on your AnnData object, then you might need to choose the first method anyway since Monocle3 performs its own pre-processing, so we would also need an unprocessed expression matrix alongside annotated (pre-processed) AnnData. That method is more expanded and specific, while the general one just shows the main principle of the conversion." disambiguation="anndata2cds" %} + +Skip some steps! +> +> If you are following this entire tutorial (rather than using a specific section necessary!), you can stay in the your previous history and just reuse outputs to build different single cell objects! +{: .tip} + + +>Inspect AnnData +> +> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `Key-indexed observations annotation (obs)` +> +> 2. **Rename** {% icon galaxy-pencil %} the output `Observations`. +> +> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `The full data matrix` +> +> 4. **Rename** {% icon galaxy-pencil %} the output `Matrix`. +> +{: .hands_on} + + +>+> +> What are the rows, and what are the columns in the retrieved Matrix? +> +> > +> > +> > If you just click on the `Matrix` dataset, you will see a preview, showing barcodes in the first column, while genes are in the first row. +> > +> {: .solution} +> +{: .question} + + +However, the next tool we need expects a matrix wherein the genes are listed in the first column and the barcodes are listed in the first row. Therefore, we need to transpose the current matrix. + +> Transpose the matrix +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `Matrix` +> +{: .hands_on} + +And now we are ready to input that data to **DropletUtils** tool. + +>DropletUtils +> +> 1. {% tool [DropletUtils](toolshed.g2.bx.psu.edu/repos/iuc/dropletutils/dropletutils/1.10.0+galaxy2) %} with the following parameters: +> - *"Format for the input matrix"*: `Tabular` +> - {% icon param-file %} *"Count Data"*: output of **Transpose** {% icon tool %} +> - *"Operation"*: `Filter for Barcodes` +> - *"Method"*: `DefaultDrops` +> - *"Expected Number of Cells"*: `338` +> - *"Upper Quantile"*: `1.0` +> - *"Lower Proportion"*: `0.0` +> - *"Format for output matrices"*: `Bundled (barcodes.tsv, genes.tsv, matrix.mtx)` +> - *"Random Seed"*: `100` +> +{: .hands_on} + +Finally, let's combine those files that we have just generated and turn them into the SingleCellExperiment! + +>Create SCE object +> +> 1. {% tool [DropletUtils Read10x](toolshed.g2.bx.psu.edu/repos/ebi-gxa/dropletutils_read_10x/dropletutils_read_10x/1.0.4+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Expression matrix in sparse matrix format (.mtx)"*: `DropletUtils 10X Matrices` +> - {% icon param-file %} *"Gene table"*: `DropletUtils 10X Genes` +> - {% icon param-file %} *"Barcode/cell table"*: `DropletUtils 10X Barcodes` +> - *"Should metadata file be added?"*: {% icon param-toggle %} `Yes` +> - {% icon param-file %} *"Metadata file"*: `Observations` +> - *"Cell ID column"*: `index` +> +> 2. **Rename** {% icon galaxy-pencil %} the output `Converted SCE object`. +> +{: .hands_on} + +As usual, you can check the [example history](https://usegalaxy.eu/u/j.jakiela/h/anndata-to-singlecellexperiment-sce-conversion) and the dedicated [workflow](https://usegalaxy.eu/u/j.jakiela/w/anndata-to-singlecellexperiment-sce-conversion). + ++ +## CDS input for Monocle3 tutorial (use for pre-processed data) +The [dedicated tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}) shows how to perform trajectory analysis using Monocle3 which is the next step in the single-cell case study tutorial series, right after [pre-processing tutorial]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}) and [analysing the metadata]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}). To keep the continuity of the series, we will continue to work on the case study data from a mouse model of fetal growth restriction {% cite Bacon2018 %} (see [the study in Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/results/tsne) and [the project submission](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6945/)). After successfully completing this section with the mentioned dataset, you can use it directly in the [Monocle3 tutorial workflow](https://humancellatlas.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3-1). +If you work on your own data, you might also need to follow this method since it shows how to deal with already pre-processed datasets. Since Monocle3 performs its own pre-processing, you will need both annotated and unprocessed matrices. + +## Get data +Monocle3 works great with annotated data, so we will make use of our annotated AnnData object, generated in the [previous tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}). We will also need a ‘clean’ expression matrix, extracted from the AnnData object just before we started the processing. +You have two options for uploading these datasets. Importing via history is often faster. + +>+ +Option 1: Data upload - Import history +> +> 1. Import history from: [input history](https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/monocle3-input-files) +> +> {% snippet faqs/galaxy/histories_import.md %} +> +> 2. **Rename** {% icon galaxy-pencil %} the history to your name of choice. +> +{: .hands_on} + + +>Option 2: Data upload - Add to history +> +> 1. Create a new history for this tutorial +> 2. Import the AnnData object from [Zenodo](https://zenodo.org/records/7078524) +> +> ``` +> https://zenodo.org/records/7078524/files/AnnData_before_processing.h5ad +> https://zenodo.org/records/7078524/files/Annotated_AnnData.h5ad +> ``` +> +> 3. Check that the datatype is `h5ad` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} +> +{: .hands_on} + + +## Extracting annotations +To run Monocle, we need cell metadata, gene metadata, and an expression matrix file of genes by cells. (In theory, the expression matrix alone could do, but then we wouldn’t have all those useful annotations that we worked on so hard in the previous tutorials!). In order to get these files, we will extract the gene and cell annotations from our AnnData object. + +>+> +> How many lines do you expect to be in the gene and cell metadata files? +> +> > +> > +> > If you click on the step with uploaded annotated AnnData file, you will see on a small preview that this object has 8605 observations and 15395 variables, so we expect to get a cell metadata file with 8605 lines and gene metadata file with 15395 lines (without headers of course!). +> > +> {: .solution} +> +{: .question} + + +> Extracting annotations +> +> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Annotated_AnnData` +> - *"What to inspect?"*: `Key-indexed observations annotation (obs)` +> 2. Rename {% icon galaxy-pencil %} the observations annotation `Extracted cell annotations (obs)` +> +> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Annotated_AnnData` +> - *"What to inspect?"*: `Key-indexed annotation of variables/features (var)` +> +> 4. Rename {% icon galaxy-pencil %} the annotation of variables `Extracted gene annotations (var)` +> +> +{: .hands_on} + +Quick and easy, isn’t it? However, we need to make some minor changes before we can input these files into the Monocle toolsuite. + +## Cell metadata +Our current dataset is not just T-cells: as you might remember from the last tutorial, we identified a cluster of macrophages as well. This might be a problem, because the trajectory algorithm will try to find relationships between all the cells (even if they are not necessarily related!), rather than only the T-cells that we are interested in. We need to remove those unwanted cell types to make the analysis more accurate. + +The **Manipulate AnnData** tool allows you to filter observations or variables, and that would be the most obvious way to remove those cells. However, given that we don't need an AnnData object, it's a lot quicker to edit a table rather than manipulate an AnnData object. Ultimately, we need cell metadata, gene metadata and expression matrix files that have macrophages removed, and that have the correct metadata that Monocle looks for. With some table manipulation, we’ll end up with three separate files, ready to be passed onto Monocle3. + + +>+> +> Where is the information about cell types stored? +> +> > +> > +> > We have already extracted the cell annotations file - in one of the columns you can find the information about cell type, assigned to each cell. +> >  +> > +> {: .solution} +> +{: .question} + +Click on `Extracted cell annotations (obs)` file to see a small preview window. This shows you that the column containing the cell types has number 22. We’ll need that to filter out unwanted cell types! + +> Check the column number! +> If you are working on a different dataset, the number of the ‘cell_type’ column might be different, so make sure you check it on a preview and use the correct number! +{: .warning} + +>Filter out macrophages +> +> 1. {% tool [Filter](Filter1) %} with the following parameters: +> - {% icon param-file %} *"Filter"*: `Extracted cell annotations (obs)` +> - *"With following condition"*: `c22!='Macrophages'` +> - *"Number of header lines to skip"*: `1` +> - That’s it - our cell annotation file is ready for Monocle! Let’s rename it accordingly. +> 2. **Rename** {% icon galaxy-pencil %} the output: `Cells input data for Monocle3` +> +> >Parameters +> > +> > - `c22` means column no. 22 - that's the column with cell types, and it will be filtered for the macrophages +> > - `!=` means 'not equal to' - we want to keep the cell types which ARE NOT macrophages +> {: .details} +> +> >Other unwanted cell types +> > +> > It might happen that during clustering you’ll find another cell type that you want to get rid of for the trajectory analysis. Then simply re-run this tool on already filtered file and change ‘Macrophages’ to another unwanted cell type. +> {: .tip} +{: .hands_on} + +## Gene annotations +Sometimes certain functionalities require a specific indication of where the data should be taken from. Monocle3 tools expect that the genes column is named ‘gene_short_name’. Let's check what the name of that column is in our dataset currently. + +>+> +> 1. Where can you check the header of a column containing genes names? +> 2. What is the name of this column? +> +> > +> > +> > 1. Our extracted gene annotations file! Either by clicking on the eye icon {% icon solution %} or having a look at the small preview window. +> > 2. In our dataset the gene names are stored in a column called ‘Symbol’ - we need to change that! +> >  +> > +> {: .solution} +> +{: .question} + +Let’s click on the `Extracted gene annotations (var)` file to see a small preview. We can see that the gene names are in the third column with a header `Symbol`. Keep that in mind - we’ll use that in a second! + +> Changing the column name +> +> 1. {% tool [Column Regex Find And Replace](toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.2) %} with the following parameters: +> - {% icon param-file %} *"Select cells from"*: `Extracted gene annotations (var)` +> - *"using column"*: `c3` or `Column: 3` +> - In *"Check"*: +> - {% icon param-repeat %} *"Insert Check"* +> - *"Find Regex"*: `Symbol` +> - *"Replacement"*: `gene_short_name` +> 2. Check that the datatype is `tabular` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %} +> - Voila! That’s the gene input for Monocle! Just a quick rename... +> 3. **Rename** {% icon galaxy-pencil %} the output: `Genes input data for Monocle3` +> +{: .hands_on} + +## Expression matrix +Last, but not least! And in fact, the most important! The expression matrix contains all the values representing the expression level of a particular gene in a cell. This is why in theory the expression matrix is the only input file required by Monocle3. Without annotation files the CDS data can still be generated - it will be quite bare and rather unhelpful for interpretation, but it's possible to process. +So, the values in the expression matrix are just numbers. But do you remember that we have already done some processing such as normalisation and the calculation of principal components in the AnnData object in the previous tutorial? That affected our expression matrix. Preprocessing is one of the steps in the Monocle3 workflow, so we want to make sure that the calculations are done on a ‘clean’ expression matrix. If we apply too many operations on our raw data, it will be too ‘deformed’ to be reliable. The point of the analysis is to use algorithms that make the enormous amount of data understandable in order to draw meaningful conclusions in accordance with biology. +So how do we do that? + +>+> +> 1. How many cells and genes are there in the `Anndata_before_processing` file? +> 2. How many lines are there in `Cells input data for Monocle3`? +> 3. How many lines are there in `Genes input data for Monocle3`? +> +> > +> > You can answer all the questions just by clicking on the given file and looking at the preview window. +> > 1. [n_obs x n_vars] = 31178 x 35734, so there are 31178 cells and 35734 genes. +> > 2. 8570 lines, including a header, which makes 8569 cells. +> > 3. 15396 lines, including a header, which makes 15395 genes. +> > +> {: .solution} +> +{: .question} + +As you can see, there are way more genes and cells in the unprocessed AnnData file, so the expression matrix is much bigger than we need it to be. If the genes and cells we prepared for Monocle3 are not the same as in the expression matrix, Monocle3 will crash. Therefore, we have to filter that big, clean matrix and adjust it to our already prepared genes and cells files. But first, let’s extract the matrix from the unprocessed AnnData object. + +> Extracting matrix +> +> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `AnnData_before_processing` +> - *"What to inspect?"*: `The full data matrix` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Unprocessed expression matrix` +> +{: .hands_on} + +If you have a look at the preview of `Unprocessed expression matrix`, you’ll see that the first column contains the cell barcodes, while the first row - the gene IDs. We would like to keep only the values corresponding to the cells and genes that are included in `Cells input data for Monocle3` and `Genes input data for Monocle3`. How do we do it? First, we compare the cell barcodes from `Cells input data for Monocle3` to those in `Unprocessed expression matrix` and ask Galaxy to keep the values of the matrix for which the barcodes in both files are the same. Then, we’ll do the same for gene IDs. We will cut the first columns from `Cells input data for Monocle3` and `Genes input data for Monocle3` to be able to compare those columns side by side with the matrix file. + +>Cutting out the columns +> +> 1. {% tool [Cut](Cut1) %} with the following parameters: +> - *"Cut columns"*: `c1` +> - {% icon param-file %} *"From"*: `Cells input data for Monocle3` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Cells IDs` +> 3. {% tool [Cut](Cut1) %} with the following parameters: +> - *"Cut columns"*: `c1` +> - {% icon param-file %} *"From"*: `Genes input data for Monocle3` +> 4. **Rename** {% icon galaxy-pencil %} the output: `Genes IDs` +> +{: .hands_on} + +>Filter matrix values by cell barcodes +> +> 1. {% tool [Join two Datasets](join1) %} with the following parameters: +> - {% icon param-file %} *"Join"*: `Cells IDs` +> - *"using column"*: `c1`or `Column: 1` +> - {% icon param-file %} *"with"*: `Unprocessed expression matrix` +> - *"and column"*: `c1`or `Column: 1` +> - *"Keep lines of first input that do not join with second input"*: `Yes` +> - *"Keep lines of first input that are incomplete"*: `Yes` +> - *"Fill empty columns"*: `No` +> - *"Keep the header lines"*: `Yes` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Pre-filtered matrix (by cells)` +> +{: .hands_on} + +Look at the preview of the output file. First of all, you can see that there are 8570 lines (8569 cells) instead of 31178 cells that were present in the matrix. That’s exactly what we wanted to achieve - now we have raw information for the T-cells that we have filtered. However, the step that we have already performed left us with the matrix whose first and second columns are the same - let’s get rid of one of those! + +>Remove duplicate column (cells IDs) +> +> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: `Pre-filtered matrix (by cells)` +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"List of Fields"*: `c1` +> 2. **Rename** {% icon galaxy-pencil %} the output: `Filtered matrix (by cells)` +> +{: .hands_on} + +Now we will perform the same steps, but for gene IDs. But gene IDs are currently in the first row, so we need to transpose the matrix, and from there we can repeat the same steps as above for Gene IDs. + +>Filter matrix by gene IDs +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.1.0+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `Filtered matrix (by cells)` +> - The matrix is now ready to be filtered by gene IDs! +> 2. {% tool [Join two Datasets](join1) %} with the following parameters: +> - {% icon param-file %} *"Join"*: `Genes IDs` +> - *"using column"*: `c1` or `Column: 1` +> - {% icon param-file %} *"with"*: output of **Transpose** {% icon tool %} +> - *"and column"*: `c1` or `Column: 1` +> - *"Keep lines of first input that do not join with second input"*: `Yes` +> - *"Keep lines of first input that are incomplete"*: `Yes` +> - *"Fill empty columns"*: `No` +> - *"Keep the header lines"*: `Yes` +> 3. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/1.1.0) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: output of **Join two Datasets** {% icon tool %} +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"List of Fields"*: `c1` +> - Monocle3 requires that in the matrix rows are genes, and columns are cells - that is what we've got, so there is no need to transpose matrix again. The expression matrix is ready! Let's just rename it... +> 4. **Rename** {% icon galaxy-pencil %} the output: `Expression matrix for Monocle3` +> +{: .hands_on} + +{% icon congratulations %} Finally! We have prepared all the files to pass them onto the Monocle3 workflow! + +## Creating CDS object +Monocle3 turns the expression matrix, cell and gene annotations into an object called cell_data_set (CDS), which holds single-cell expression data. + +>Input files +> +> Here is what [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/starting/) says about the required three input files: +> - **expression_matrix**: a numeric matrix of expression values, where rows are genes, and columns are cells. Must have the same number of columns as the cell_metadata has rows and the same number of rows as the gene_metadata has rows. +> - **cell_metadata**: a data frame, where rows are cells, and columns are cell attributes (such as cell type, culture condition, day captured, etc.) +> - **gene_metadata**: a data frame, where rows are features (e.g. genes), and columns are gene attributes, such as biotype, gc content, etc. One of its columns should be named "gene_short_name", which represents the gene symbol or simple name (generally used for plotting) for each gene. +> +{: .details} + +>Create CDS object +> +> >Data format +> > +> > You can provide expression matrix as TSV, CSV, MTX or RDS file, while genes and cells metadata as TSV, CSV or RDS files. In our case all three files are tabular, so we will set the format to TSV. +> {: .details} +> 1. {% tool [Monocle3 create](toolshed.g2.bx.psu.edu/repos/ebi-gxa/monocle3_create/monocle3_create/0.1.4+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"Expression matrix, genes as rows, cells as columns. Required input. Provide as TSV, CSV or RDS."*: `Expression matrix for Monocle3` +> - *"Format of expression matrix"*: `TSV` +> - {% icon param-file %} *"Per-cell annotation, optional. Row names must match the column names of the expression matrix. Provide as TSV, CSV or RDS."*: `Cells input data for Monocle3` +> - *"Format of cell metadata"*: `TSV` +> - {% icon param-file %} *"Per-gene annotation, optional. Row names must match the row names of the expression matrix. Provide as TSV, CSV or RDS."*: `Genes input data for Monocle3` +> - *"Format of gene annotation"*: `TSV` +> +> 2. **Rename** {% icon galaxy-pencil %} the output: `CDS input for Monocle3 tutorial` +{: .hands_on} + +It was quite a long conversion, but we did it! If you're interested, the "Tip" below describes how we could possibly speed up the process using alternative tools. + +>Alternative tools +> +> Generally in coding you can use different ways to achieve the same result. Similarly, in Galaxy you can use different tools but the outcome will be the same. +> For example, we could have used some tools on our starting AnnData object to remove macrophages and rename the column header, and afterwards extract observations and variables. It is a good practice to use methods from the libraries (such as Scanpy below) rather than dealing with text files rows and columns. The shown approach works well, but it might be problematic with much bigger datasets sizes. Then, you can use the following route: +> 1. {% tool [Scanpy FilterCells](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_filter_cells/scanpy_filter_cells/1.8.1+galaxy93) %} on annotated Anndata to remove macrophages. +> 2. {% tool [AnnData Operations](toolshed.g2.bx.psu.edu/repos/ebi-gxa/anndata_ops/anndata_ops/1.8.1+galaxy92) %} to change the genes name from *Symbol* to *gene_short_name*. +> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} to extract genes and cells (to be used for Monocle). +> 4. Follow further steps to filter unprocessed matrix. +> +> You've probably used the mentioned tools before, so **challenge yourself** and try to replicate the process using the tools above! +{: .tip} + +You might want to consult your results with this [control history](https://singlecell.usegalaxy.eu/u/j.jakiela/h/anndata---cds-conversion-for-monocle3-tutorial), or check out the [workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/copy-of-trajectory-analysis-using-monocle3). +And now you have your input file ready to start the [Monocle3 tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %})! + ++ +## General AnnData to CDS conversion +In fact, you can do this conversion just in one step - check the {% icon tip %} **Tip** below! + +>+ +# Downsampling FASTQ files +Sometimes, it is useful to work on smaller subsets of data (especially for teaching / learning purposes). Here is an example of how you can downsample your FASTQ files. +First, let's get some toy data. We just need two FASTQ files - one containing barcodes, the other with transcripts. + +>Conversion in a single step +> +> As mentioned in the previous section, SCEasy tool can do this conversion in a single step. Let's try that out! +> +> >SCEasy Converter +> > +> > 1. {% tool [SCEasy Converter](toolshed.g2.bx.psu.edu/repos/iuc/sceasy_convert/sceasy_convert/0.0.7+galaxy2) %} with the following parameters: +> > - *"Convert From / To"*: `AnnData to CellDataSet` +> > - {% icon param-file %} *"Input object in h5ad,h5 format"*: `Downsampled AnnData object` +> > 2. **Rename** {% icon galaxy-pencil %} the output: `CDS object` +> > +> {: .hands_on} +> +{: .tip} + +Below we will also show the manual conversion, just to make you familiar with the structure of the files and operations needed. +We will continue working on previously used dataset, so you can copy it from your history or download from Zenodo. + +>Get toy data again +> +> 1. Create a new history for this tutorial +> +> 2. You can either copy the previously used dataset from your history: +> +> {% snippet faqs/galaxy/histories_copy_dataset.md %} +> +> Or, alternatively, download the dataset from [Zenodo]({{ page.zenodo_link }}) +> +> ``` +> https://zenodo.org/record/10391629/files/Downsampled_annotated_AnnData.h5ad +> ``` +> +> 3. **Rename** {% icon galaxy-pencil %} the datasets `Downsampled AnnData object` +> 4. Check that the datatype is `h5ad` +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} +> +{: .hands_on} + +Now we just need to extract information about cells, genes and an expression matrix. + +>Inspect AnnData +> +> 1. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `Key-indexed observations annotation (obs)` +> +> **Rename** {% icon galaxy-pencil %} the output `Cell barcodes (obs)`. +> +> 2. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `Downsampled AnnData object` +> - *"What to inspect?"*: `Key-indexed annotation of variables/features (var)` +> +> **Rename** {% icon galaxy-pencil %} the output `Genes (var)`. +> +> 3. {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `output` (Input dataset) +> - *"What to inspect?"*: `The full data matrix` +> +> **Rename** {% icon galaxy-pencil %} the output `Expression matrix`. +> +{: .hands_on} + +Hold on here! As mentioned, if you're converting your files to CDS, you'll probably be working with Monocle. There is one function in downstream analysis in Monocle that requires a specific name of the column containing gene symbols, and that is `gene_short_name`. If you use Galaxy buttons for the analysis, you won't be able to change that name after you create CDS file, so a good piece of advice is to rename it at this stage. There is no harm in doing this, and it might save you some time and frustration later on. You only need to check which column contains the gene symbols and what is its header - you can check that in the preview window, simply by clicking on the `Genes` dataset. In our case, that's column 3 and its name is `Symbol`. Let's change that! + +>Changing the column name +> +> 1. {% tool [Column Regex Find And Replace](toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.2) %} with the following parameters: +> - {% icon param-file %} *"Select cells from"*: `Genes` +> - *"using column"*: `c3` or `Column: 3` +> - In *"Check"*: +> - {% icon param-repeat %} *"Insert Check"* +> - *"Find Regex"*: `Symbol` +> - *"Replacement"*: `gene_short_name` +> 2. Check that the datatype is `tabular`. If not, change it. +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %} +> +> 3. **Rename** {% icon galaxy-pencil %} the output: `Genes renamed` +> +{: .hands_on} + +We're almost there, but there is one last modification we have to do - transpose the matrix to have the genes as rows and cells as columns. + +>Transpose the matrix +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `Expression matrix` +{: .hands_on} + +And the final step is to create the CDS file using Monocle tool! + +>Create Cell Data Set +> +> 1. {% tool [Monocle3 create](toolshed.g2.bx.psu.edu/repos/ebi-gxa/monocle3_create/monocle3_create/0.1.4+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"Expression matrix, genes as rows, cells as columns. Required input. Provide as TSV, CSV or RDS."*: output of **Transpose** {% icon tool %} +> - *"Format of expression matrix"*: `TSV` +> - {% icon param-file %} *"Per-cell annotation, optional. Row names must match the column names of the expression matrix. Provide as TSV, CSV or RDS."*: `Cell barcodes (obs)` +> - *"Format of cell metadata"*: `TSV` +> - {% icon param-file %} *"Per-gene annotation, optional. Row names must match the row names of the expression matrix. Provide as TSV, CSV or RDS."*: `Genes renamed` +> - *"Format of gene annotation"*: `TSV` +> +> 2. **Rename** {% icon galaxy-pencil %} the output: `CDS Monocle file` +{: .hands_on} + +As usual, you can check the [example history](https://usegalaxy.eu/u/j.jakiela/h/anndata-to-cell-data-set-cds-conversion) and the dedicated [workflow](https://usegalaxy.eu/u/j.jakiela/w/anndata-to-cell-data-set-cds-conversion) (it doesn't include the step on renaming the column header though). + +Get toy data +> +> 1. Create a new history for this section *"Downsampling FASTQ Files"* +> 2. Import the files from [Zenodo]({{ page.zenodo_link }}) +> +> ``` +> https://zenodo.org/record/4574153/files/SLX-7632.TAAGGCGA.N701.s_1.r_1.fq-400k.fastq +> https://zenodo.org/record/4574153/files/SLX-7632.TAAGGCGA.N701.s_1.r_2.fq-400k.fastq +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> +{: .hands_on} + +Funnily enough, those files are already downsampled, so you won't have to wait for too long to download them. We are not going to analyse that data anyway, it's just for demonstration purposes. +Quickly check which file contains barcodes and which file contains transcripts. If you click on the two datasets, you will see that one has shorter sequences, while the other has longer. It's quite straight-forward to deduce that shorter sequences are barcodes. + +>Rename the files +> +> 1. Rename file `s_1.r_1` as `Barcodes` +> +> 2. Rename file `s_1.r_2` as `Transcripts` +> +> {% snippet faqs/galaxy/datasets_rename.md %} +> +{: .hands_on} + +Now we will convert the FASTQ files to tabular: + +>FASTQ to tabular +> +> 1. {% tool [FASTQ to Tabular](toolshed.g2.bx.psu.edu/repos/devteam/fastq_to_tabular/fastq_to_tabular/1.1.5) %} with the following parameters: +> - {% icon param-file %} *"FASTQ file to convert"*: {% icon param-files %} Select multiple files: `Barcodes` and `Transcripts` +> +> 3. **Rename** {% icon galaxy-pencil %} the datasets `Barcodes tabular` and `Transcripts tabular` +> +{: .hands_on} + +Now let's select the number of the reads we would like to keep. It's totally up to you, we choose `100000` here. + +>Downsampling +> +> 1. {% tool [Select last](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_tail_tool/1.1.0) %} with the following parameters: +> - {% icon param-file %} *"Text file"*: {% icon param-files %} Select multiple files: `Barcodes tabular` and `Transcripts tabular` +> - *"Operation"*: `Keep last lines` +> - *"Number of lines"*: `100000` +> +> 2. **Rename** {% icon galaxy-pencil %} the dataset `Barcodes cut` and `Transcripts cut` +> +{: .hands_on} + +All done, now we just need to go back to FASTQ from Tabular again! + +>Tabular to FASTQ +> +> 1. {% tool [Tabular to FASTQ](toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fastq/tabular_to_fastq/1.1.5) %} with the following parameters: +> - {% icon param-file %} *"Tabular file to convert"*: `Barcodes cut` (output of **Select last** {% icon tool %}) +> - *"Identifier column"*: `c1` or `Column 1` +> - *"Sequence column"*: `c2` or `Column 2` +> - *"Quality column"*: `c3` or `Column 3` +> +> 2. **Rename** {% icon galaxy-pencil %} the dataset `Downsampled barcode read` and `Downsampled transcript read` +> +{: .hands_on} + +And that's all! Your downsampled data is ready to use. You can check your answers in this [example history](https://usegalaxy.eu/u/j.jakiela/h/how-to-downsample-fastq-files) or if you want to accelerate this process, feel free to use the [workflow](https://singlecell.usegalaxy.eu/u/j.jakiela/w/workflow-constructed-from-history-copy-of-cs1generating-a-single-cell-matrix-using-alevin---how-to-downsample) next time! diff --git a/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion-test.yml b/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion-test.yml new file mode 100644 index 00000000000000..e6cdd07bb55051 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion-test.yml @@ -0,0 +1,15 @@ +- doc: Test outline for AnnData-to-Cell-Data-Set-(CDS)-conversion + job: + AnnData: + class: File + location: https://zenodo.org/record/10391629/files/Downsampled_annotated_AnnData.h5ad + filetype: h5ad + outputs: + 'Inspect AnnData: var': + path: 'test-data/Inspect AnnData: var.tabular' + CDS Monocle file: + path: test-data/CDS Monocle file.rdata + 'Inspect AnnData: obs': + path: 'test-data/Inspect AnnData: obs.tabular' + 'Inspect AnnData: X': + path: 'test-data/Inspect AnnData: X.tabular' diff --git a/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion.ga b/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion.ga new file mode 100644 index 00000000000000..5fc0d4acfdb040 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/workflows/AnnData-to-Cell-Data-Set-(CDS)-conversion.ga @@ -0,0 +1 @@ +{"a_galaxy_workflow": "true", "annotation": "AnnData to CDS format conversion (manually using Galaxy buttons). 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obs.tabular b/topics/single-cell/tutorials/scrna-data-ingest/workflows/test-data/Inspect AnnData_ obs.tabular new file mode 100644 index 00000000000000..dd0cfb25177323 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/workflows/test-data/Inspect AnnData_ obs.tabular @@ -0,0 +1,339 @@ +index Barcode emptyTotal emptyLogProb emptyPValue emptyLimited emptyFDR batch genotype sex n_genes_by_counts log1p_n_genes_by_counts total_counts log1p_total_counts pct_counts_in_top_50_genes total_counts_mito log1p_total_counts_mito pct_counts_mito n_genes n_counts +TTGTTCGCTATT-0 TTGTTCGCTATT 260 -986.8511581975114 0.000999000999000999 True 0.0 N701 wildtype male 321 5.7745515455444085 297.0 5.6970935 25.925928589887377 1.5 0.91629076 0.5050505 321 297.0 +CCATGACGCTGT-0 CCATGACGCTGT 260 -967.0770333179133 0.005994005994005994 False 0.0 N701 wildtype male 290 5.673323267171493 281.0 5.641907 30.960857454983582 3.0 1.3862944 1.0676156 290 281.0 +CCATTCCTGTCT-0 CCATTCCTGTCT 227 -909.4626235241008 0.000999000999000999 True 0.0 N701 wildtype male 252 5.53338948872752 250.0 5.525453 30.00000366210982 2.0 1.0986123 0.8 252 250.0 +TCGTAAATTGCC-0 TCGTAAATTGCC 224 -888.5342290884689 0.000999000999000999 True 0.0 N701 wildtype male 285 5.655991810819852 248.00002 5.517453 28.225806451612907 3.0 1.3862944 1.2096773 285 248.00002 +GATGCATTCCTG-0 GATGCATTCCTG 253 -965.6853780711092 0.000999000999000999 True 0.0 N701 wildtype male 329 5.799092654460526 287.0 5.6629605 26.132409738661018 2.0 1.0986123 0.6968641 329 287.0 +TTTCCGACCTTC-0 TTTCCGACCTTC 230 -936.1786086050013 0.000999000999000999 True 0.0 N701 wildtype male 329 5.799092654460526 267.0 5.590987 27.71536214086689 1.0 0.6931472 0.37453184 329 267.0 +GAGCCCTGTTTA-0 GAGCCCTGTTTA 270 -982.5027228278975 0.02197802197802198 False 0.0 N701 wildtype male 308 5.733341276897746 301.0 5.710427 29.900324125035272 1.5 0.91629076 0.49833888 308 301.0 +GGTCCTGTTCAT-0 GGTCCTGTTCAT 190 -751.1576677225542 0.0969030969030969 False 0.0 N701 wildtype male 220 5.3981627015177525 207.0 5.3375382 31.884060321314738 3.0 1.3862944 1.4492754 220 207.0 +CGTGCCTGAGGA-0 CGTGCCTGAGGA 202 -797.6839550036165 0.028971028971028972 False 0.0 N701 wildtype male 250 5.5254529391317835 225.0 5.420535 30.666670826100102 2.0 1.0986123 0.8888889 250 225.0 +TTTGCCGAACTC-0 TTTGCCGAACTC 197 -769.3704077360069 0.11288711288711288 False 0.0 N701 wildtype male 241 5.488937726156687 225.0 5.420535 29.77778383608341 3.0 1.3862944 1.3333334 241 225.0 +GCGCCGGGCTGT-0 GCGCCGGGCTGT 202 -753.4666171098941 0.5544455544455544 False 0.0 N701 wildtype male 238 5.476463551931511 221.99998 5.4071717 38.06307614406419 2.0 1.0986123 0.9009009 238 221.99998 +CTTCATGAGACG-0 CTTCATGAGACG 192 -810.3816648149929 0.000999000999000999 True 0.0 N701 wildtype male 257 5.552959584921617 213.0 5.365976 28.638497652582164 1.0 0.6931472 0.46948358 257 213.0 +GATCTTGCCATT-0 GATCTTGCCATT 188 -739.7062331840634 0.15384615384615385 False 0.0 N701 wildtype male 238 5.476463551931511 206.99998 5.337538 33.333338247597844 1.0 0.6931472 0.48309183 238 206.99998 +GATAGACACCAA-0 GATAGACACCAA 220 -818.9658895774268 0.2897102897102897 False 0.0 N701 wildtype male 251 5.529429087511423 240.99998 5.488938 33.60996276221853 3.5 1.5040774 1.4522823 251 240.99998 +GACGAGTGTCCC-0 GACGAGTGTCCC 193 -762.8225847626675 0.07192807192807193 False 0.0 N701 wildtype male 235 5.4638318050256105 211.0 5.3565865 33.175355450236964 2.5 1.252763 1.1848341 235 211.0 +CCAACCCCAACA-0 CCAACCCCAACA 202 -783.9932275105768 0.1008991008991009 False 0.0 N701 wildtype male 232 5.4510384535657 224.0 5.4161005 35.26785954528941 1.5 0.91629076 0.66964287 232 224.0 +CAAACATCGATC-0 CAAACATCGATC 175 -727.1663738576353 0.01098901098901099 False 0.0 N701 wildtype male 259 5.560681631015528 201.0 5.3082676 28.855736726991044 1.5 0.91629076 0.74626863 259 201.0 +CTACAACATTCA-0 CTACAACATTCA 184 -732.3212209924959 0.1088911088911089 False 0.0 N701 wildtype male 283 5.648974238161206 222.99998 5.411646 31.390136677020063 1.5 0.91629076 0.67264575 283 222.99998 +ACACCTACAAAG-0 ACACCTACAAAG 183 -745.4646907736969 0.01998001998001998 False 0.0 N701 wildtype male 282 5.645446897643238 215.0 5.3752785 29.302335979492177 2.5 1.252763 1.1627907 282 215.0 +GCGTGTGATCGG-0 GCGTGTGATCGG 174 -710.4137148086195 0.04695304695304695 False 0.0 N701 wildtype male 215 5.375278407684165 196.0 5.2832036 36.224492616030055 2.0 1.0986123 1.0204082 215 196.0 +CCTCTTTCTTGT-0 CCTCTTTCTTGT 185 -766.0243083287389 0.002997002997002997 False 0.0 N701 wildtype male 233 5.455321115357702 204.0 5.32301 31.372556059419832 2.0 1.0986123 0.9803922 233 204.0 +AGAATTTTCCGA-0 AGAATTTTCCGA 166 -660.0246062389172 0.3856143856143856 False 0.0 N701 wildtype male 197 5.288267030694535 184.0 5.220356 35.32609281558042 2.0 1.0986123 1.0869565 197 184.0 +TCACACCCCAGG-0 TCACACCCCAGG 174 -706.1208928414695 0.07392607392607392 False 0.0 N701 wildtype male 229 5.438079308923196 200.0 5.3033047 35.00000534057699 3.0 1.3862944 1.5 229 200.0 +CGTTATTGTTAT-0 CGTTATTGTTAT 161 -684.551045733705 0.008991008991008992 False 0.0 N701 wildtype male 241 5.488937726156687 192.0 5.26269 33.333343929718 2.5 1.252763 1.3020833 241 192.0 +TGTCATCTTGCG-0 TGTCATCTTGCG 162 -651.7574376522348 0.32867132867132864 False 0.0 N701 wildtype male 228 5.43372200355424 186.99998 5.236442 36.89840776524109 1.5 0.91629076 0.8021391 228 186.99998 +TAGGCACTGGGA-0 TAGGCACTGGGA 162 -673.6374777955084 0.05094905094905095 False 0.0 N701 wildtype male 208 5.342334251964811 182.0 5.209486 36.263745384739146 4.0 1.609438 2.1978023 208 182.0 +ACGAAGATACTG-0 ACGAAGATACTG 161 -694.9887998186896 0.001998001998001998 False 0.0 N701 wildtype male 230 5.442417710521793 189.0 5.247024 37.03704600752073 0.5 0.4054651 0.26455027 230 189.0 +CCTTCCTCCTTC-0 CCTTCCTCCTTC 156 -685.7331543398832 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 204 5.3230099791384085 179.99998 5.198497 33.33333615903525 2.0 1.0986123 1.1111113 204 179.99998 +GCTACTTCAGTC-0 GCTACTTCAGTC 162 -693.9450447258657 0.006993006993006993 False 0.0 N701 wildtype male 211 5.356586274672012 176.00002 5.17615 35.22728494376614 1.5 0.91629076 0.8522726 211 176.00002 +TCTCCAGTTATG-0 TCTCCAGTTATG 165 -701.5742004142824 0.005994005994005994 False 0.0 N701 wildtype male 214 5.3706380281276624 182.0 5.209486 32.41757969970873 0.5 0.4054651 0.2747253 214 182.0 +AAGGCGATGTGG-0 AAGGCGATGTGG 162 -713.9557739252693 0.000999000999000999 True 0.0 N701 wildtype male 227 5.429345628954441 189.0 5.247024 31.746026620043132 1.0 0.6931472 0.52910054 227 189.0 +CCCAAGTTTTCC-0 CCCAAGTTTTCC 148 -651.8469678209592 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 208 5.342334251964811 161.00002 5.0875964 36.64594725102464 0.0 0.0 0.0 208 161.00002 +TCACGCATCTTT-0 TCACGCATCTTT 138 -642.178061505857 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 161 5.087596335232384 146.0 4.9904327 36.3013698630137 2.0 1.0986123 1.369863 161 146.0 +ACTCTAACTCTC-0 ACTCTAACTCTC 131 -611.7618672195131 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 170 5.14166355650266 147.0 4.9972124 38.775506179134226 2.0 1.0986123 1.3605442 170 147.0 +AGTTCACCGTTT-0 AGTTCACCGTTT 118 -553.6948863157102 0.002997002997002997 False 0.008016983016983016 N701 wildtype male 176 5.176149732573829 133.99998 4.905275 40.298507462686565 1.0 0.6931472 0.74626875 176 133.99998 +TGGTCCCGTGTC-0 TGGTCCCGTGTC 134 -614.3770360805843 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 181 5.204006687076795 144.00002 4.9767337 40.972217880649794 0.0 0.0 0.0 181 144.00002 +GGGCCTAATACT-0 GGGCCTAATACT 108 -519.8267450237876 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 139 4.941642422609304 122.0 4.8121843 43.44262566754861 1.0 0.6931472 0.8196721 139 122.0 +GTGGCCGGTTAA-0 GTGGCCGGTTAA 121 -561.5783835547463 0.002997002997002997 False 0.008016983016983016 N701 wildtype male 186 5.231108616854587 138.99998 4.9416423 41.007185241439 0.5 0.4054651 0.35971227 186 138.99998 +CAGAATTTTAGT-0 CAGAATTTTAGT 120 -563.0241674270864 0.002997002997002997 False 0.008016983016983016 N701 wildtype male 141 4.955827057601261 129.0 4.8675346 42.63565891472868 0.0 0.0 0.0 141 129.0 +CACCGAGTTACT-0 CACCGAGTTACT 104 -512.6409207634111 0.000999000999000999 True 0.0028890028890028894 N701 wildtype male 157 5.062595033026967 121.0 4.804021 44.62810198748262 0.0 0.0 0.0 157 121.0 +CGGCACCTGACC-1 CGGCACCTGACC 189 -754.9600628703138 0.24875124875124874 False N702 knockout male 225 5.420534999272286 210.0 5.351858 33.33333091130297 4.0 1.609438 1.904762 225 210.0 +GGACAGCCGGTG-1 GGACAGCCGGTG 307 -1072.415163495642 0.3256743256743257 False N702 knockout male 330 5.802118375377063 332.0 5.8081427 28.313207051840173 7.5 2.1400661 2.259036 330 332.0 +CCAACATCCATC-1 CCAACATCCATC 279 -1093.288585588144 0.000999000999000999 True N702 knockout male 344 5.84354441703136 306.0 5.7268476 23.529409418102645 2.0 1.0986123 0.6535948 344 306.0 +CCGCGCTACCCT-1 CCGCGCTACCCT 263 -908.6323645845596 0.968031968031968 False N702 knockout male 279 5.634789603169249 283.0 5.6489744 35.689038239271994 2.5 1.252763 0.8833922 279 283.0 +CAGCCTTAAAGC-1 CAGCCTTAAAGC 219 -833.5956938782125 0.3996003996003996 False N702 knockout male 246 5.5093883366279774 238.99998 5.480639 31.799167240307824 1.0 0.6931472 0.41841006 246 238.99998 +ATCATGACTGGT-1 ATCATGACTGGT 244 -917.6270320406585 0.1958041958041958 False N702 knockout male 278 5.631211781821365 264.00003 5.57973 29.16667340981996 1.5 0.91629076 0.56818175 278 264.00003 +TCTCTCCTCCGT-1 TCTCTCCTCCGT 233 -883.5547324675151 0.23476523476523475 False N702 knockout male 275 5.62040086571715 252.99998 5.5373344 30.830052541521262 2.0 1.0986123 0.7905139 275 252.99998 +GATTAGGCATTT-1 GATTAGGCATTT 189 -721.964194686444 0.8381618381618382 False N702 knockout male 244 5.501258210544727 208.0 5.3423343 31.250006877460528 4.0 1.609438 1.923077 244 208.0 +TGTTCGATTTGT-1 TGTTCGATTTGT 188 -724.8659843745686 0.7602397602397603 False N702 knockout male 246 5.5093883366279774 217.0 5.3844953 33.179730501601504 2.0 1.0986123 0.921659 246 217.0 +ATTTTCCGGTGT-1 ATTTTCCGGTGT 185 -771.5561922521365 0.01098901098901099 False N702 knockout male 249 5.521460917862246 210.0 5.351858 31.428573712199892 0.5 0.4054651 0.23809525 249 210.0 +TAATTTGCAGCC-1 TAATTTGCAGCC 178 -704.1203899544255 0.6143856143856143 False N702 knockout male 206 5.332718793265369 193.0 5.267858 34.1968884880668 2.0 1.0986123 1.0362694 206 193.0 +GAGTCTTTACAG-1 GAGTCTTTACAG 175 -720.3207273031171 0.14185814185814186 False N702 knockout male 225 5.420534999272286 200.0 5.3033047 31.0 2.5 1.252763 1.25 225 200.0 +ACAATCGTATTT-1 ACAATCGTATTT 169 -710.2777269876996 0.061938061938061936 False N702 knockout male 215 5.375278407684165 184.0 5.220356 32.60870106053577 1.5 0.91629076 0.8152174 215 184.0 +GCGCCTCATCGT-1 GCGCCTCATCGT 173 -711.8142504507895 0.16183816183816183 False N702 knockout male 203 5.318119993844216 191.0 5.2574954 35.07853403141361 0.0 0.0 0.0 203 191.0 +CCCGAAACCCTT-1 CCCGAAACCCTT 167 -691.3255232576656 0.1918081918081918 False N702 knockout male 212 5.3612921657094255 183.99998 5.220356 35.32609574511049 1.5 0.91629076 0.8152175 212 183.99998 +ACTCTGGCTTCT-1 ACTCTGGCTTCT 175 -717.4564710756578 0.17082917082917082 False N702 knockout male 203 5.318119993844216 193.0 5.267858 36.26943291931413 0.0 0.0 0.0 203 193.0 +TTTTCCTGGTTA-1 TTTTCCTGGTTA 156 -651.136789609909 0.2887112887112887 False N702 knockout male 185 5.225746673713202 176.0 5.17615 36.36363951627901 2.5 1.252763 1.4204546 185 176.0 +CTAGCGACGGCT-1 CTAGCGACGGCT 160 -670.5210672039145 0.16883116883116883 False N702 knockout male 200 5.303304908059076 176.0 5.17615 36.36363636363637 0.0 0.0 0.0 200 176.0 +GGTGAAGGTTTC-1 GGTGAAGGTTTC 160 -665.358079406398 0.26073926073926074 False N702 knockout male 204 5.3230099791384085 180.0 5.198497 35.00000296698701 3.5 1.5040774 1.9444445 204 180.0 +GTGAAAATCTGC-1 GTGAAAATCTGC 161 -677.9173449935167 0.12387612387612387 False N702 knockout male 199 5.298317366548036 180.00002 5.1984973 37.22222537758936 2.0 1.0986123 1.111111 199 180.00002 +CTAGCGTGTTAA-1 CTAGCGTGTTAA 142 -590.2536397708864 0.6623376623376623 False N702 knockout male 180 5.198497031265826 155.0 5.049856 41.93548799925655 2.0 1.0986123 1.2903225 180 155.0 +CGTACGGGTATT-1 CGTACGGGTATT 153 -659.4339973274073 0.058941058941058944 False N702 knockout male 171 5.147494476813453 163.0 5.0998664 34.9693284269301 0.0 0.0 0.0 171 163.0 +ATTCTCTCCACA-1 ATTCTCTCCACA 144 -624.9708595119838 0.1018981018981019 False N702 knockout male 194 5.272999558563747 157.99998 5.068904 38.60759120819563 2.0 1.0986123 1.2658229 194 157.99998 +CCGTCGTTCGAT-1 CCGTCGTTCGAT 141 -609.9493074058214 0.16483516483516483 False N702 knockout male 184 5.220355825078325 157.0 5.062595 36.3057289555321 2.0 1.0986123 1.2738854 184 157.0 +GGCTTTACCAGG-1 GGCTTTACCAGG 142 -585.0738378047961 0.7552447552447552 False N702 knockout male 179 5.19295685089021 157.0 5.062595 41.40127388535032 1.0 0.6931472 0.6369427 179 157.0 +CTGTCTTAACTC-1 CTGTCTTAACTC 152 -611.0609706100622 0.8431568431568431 False N702 knockout male 188 5.241747015059643 170.0 5.1416636 36.47058496178257 2.0 1.0986123 1.1764706 188 170.0 +CCTGGCATAATT-1 CCTGGCATAATT 145 -588.8457689499608 0.8471528471528471 False N702 knockout male 180 5.198497031265826 160.0 5.081404 40.625 0.5 0.4054651 0.3125 180 160.0 +CATGTCAAATAG-1 CATGTCAAATAG 155 -643.4586021697119 0.3886113886113886 False N702 knockout male 173 5.159055299214529 163.0 5.0998664 38.650299512186905 1.0 0.6931472 0.6134969 173 163.0 +GCAAGATCGGGC-1 GCAAGATCGGGC 149 -638.6323848676006 0.12787212787212787 False N702 knockout male 188 5.241747015059643 161.0 5.0875964 39.75156409740494 0.5 0.4054651 0.310559 188 161.0 +CCGAACTCGCCG-1 CCGAACTCGCCG 136 -587.2880143846223 0.27372627372627373 False N702 knockout male 162 5.093750200806762 147.0 4.9972124 36.734697690659885 0.5 0.4054651 0.34013605 162 147.0 +TCATCCTGAGCT-1 TCATCCTGAGCT 148 -628.6067100912492 0.21578421578421578 False N702 knockout male 200 5.303304908059076 166.99998 5.123964 34.73054209548512 1.5 0.91629076 0.89820373 200 166.99998 +GCGTTTAATGGG-1 GCGTTTAATGGG 135 -587.6716902487163 0.1968031968031968 False N702 knockout male 168 5.1298987149230735 148.0 5.0039463 40.540544720267704 1.0 0.6931472 0.6756757 168 148.0 +TAGTGTTCCCTA-1 TAGTGTTCCCTA 131 -587.7514748862466 0.04295704295704296 False N702 knockout male 153 5.0369526024136295 146.0 4.9904327 40.410963127550424 0.0 0.0 0.0 153 146.0 +AAGTTCCCATCA-1 AAGTTCCCATCA 150 -631.8611932087492 0.2957042957042957 False N702 knockout male 178 5.187385805840755 159.0 5.075174 36.477983920692694 0.0 0.0 0.0 178 159.0 +GATGTCAGAACA-1 GATGTCAGAACA 130 -553.292011295988 0.5984015984015985 False N702 knockout male 203 5.318119993844216 151.0 5.0238805 41.72186273675633 1.0 0.6931472 0.66225165 203 151.0 +CCGCTAAATAAT-1 CCGCTAAATAAT 155 -630.3541141123503 0.6833166833166833 False N702 knockout male 163 5.099866427824199 163.0 5.0998664 39.87730434649924 0.0 0.0 0.0 163 163.0 +ACTGTCGTACTT-1 ACTGTCGTACTT 153 -620.1936771940045 0.7542457542457542 False N702 knockout male 210 5.351858133476067 167.0 5.123964 37.72455779198886 0.5 0.4054651 0.29940122 210 167.0 +TTCAAAGAGTTA-1 TTCAAAGAGTTA 134 -556.8522246637232 0.7882117882117882 False N702 knockout male 156 5.056245805348308 147.0 4.9972124 42.17687074829932 1.0 0.6931472 0.6802721 156 147.0 +ACGATTGTCTCA-1 ACGATTGTCTCA 130 -541.6116395970314 0.8281718281718282 False N702 knockout male 146 4.990432586778736 143.0 4.9698133 44.75524475524475 1.0 0.6931472 0.6993007 146 143.0 +CTCATCGAGTGT-1 CTCATCGAGTGT 136 -571.9328917866326 0.6273726273726273 False N702 knockout male 173 5.159055299214529 153.0 5.0369525 39.21568627450981 2.0 1.0986123 1.3071896 173 153.0 +GCGCAACCTTCC-1 GCGCAACCTTCC 134 -581.9856725873617 0.24975024975024976 False N702 knockout male 145 4.983606621708336 139.0 4.9416423 45.32374100719424 1.0 0.6931472 0.7194245 145 139.0 +GGAACAAAACTT-1 GGAACAAAACTT 131 -571.4312182351518 0.2597402597402597 False N702 knockout male 161 5.087596335232384 147.0 4.9972124 41.49659863945578 0.333333 0.28768182 0.22675714 161 147.0 +GCGTAAGGGTCC-1 GCGTAAGGGTCC 134 -571.322540765249 0.4925074925074925 False N702 knockout male 167 5.123963979403259 145.0 4.983607 38.62068965517241 0.5 0.4054651 0.3448276 167 145.0 +GGACCAGAGACA-1 GGACCAGAGACA 128 -561.2588834635708 0.26573426573426573 False N702 knockout male 142 4.962844630259907 137.0 4.9272537 39.416058394160586 1.5 0.91629076 1.0948905 142 137.0 +TTAGGCCACGAT-1 TTAGGCCACGAT 134 -576.4288692161917 0.36363636363636365 False N702 knockout male 165 5.111987788356544 147.0 4.9972124 36.734693877551024 0.0 0.0 0.0 165 147.0 +ATAGTCTCAGTT-1 ATAGTCTCAGTT 126 -557.0851349628151 0.2077922077922078 False N702 knockout male 140 4.948759890378168 135.0 4.912655 47.40740740740741 2.0 1.0986123 1.4814814 140 135.0 +CATCCGTCGCCC-1 CATCCGTCGCCC 127 -545.0904820017511 0.5694305694305695 False N702 knockout male 143 4.969813299576001 140.0 4.94876 44.285714285714285 4.0 1.609438 2.857143 143 140.0 +CACTGTCGACAC-1 CACTGTCGACAC 125 -553.9263194594143 0.2077922077922078 False N702 knockout male 179 5.19295685089021 140.0 4.94876 40.00000871930994 0.0 0.0 0.0 179 140.0 +TTAAGACCGGAT-1 TTAAGACCGGAT 126 -539.4621430262334 0.6243756243756243 False N702 knockout male 162 5.093750200806762 134.0 4.905275 45.52239842711456 2.0 1.0986123 1.4925373 162 134.0 +GCCCGCGTATGA-2 GCCCGCGTATGA 255 -964.3083611205002 0.000999000999000999 True 0.0 N703 knockout female 285 5.655991810819852 275.0 5.620401 29.81818512719562 3.5 1.5040774 1.2727273 285 275.0 +TCGAGCCCGACT-2 TCGAGCCCGACT 219 -841.6561608168961 0.002997002997002997 False 0.0 N703 knockout female 253 5.537334267018537 237.0 5.4722705 32.06751674233933 3.5 1.5040774 1.4767933 253 237.0 +ATGCTGCCTGCT-2 ATGCTGCCTGCT 222 -848.1085597584329 0.002997002997002997 False 0.0 N703 knockout female 238 5.476463551931511 242.0 5.4930615 32.64461548860408 2.0 1.0986123 0.82644624 238 242.0 +TGGGTTTGTCTG-2 TGGGTTTGTCTG 222 -862.9369282986436 0.000999000999000999 True 0.0 N703 knockout female 251 5.529429087511423 239.0 5.480639 30.962351003313522 2.0 1.0986123 0.83682007 251 239.0 +GGCCATTTTGCG-2 GGCCATTTTGCG 232 -905.6114775741012 0.000999000999000999 True 0.0 N703 knockout female 269 5.598421958998375 253.0 5.5373344 30.830037666288373 2.5 1.252763 0.9881423 269 253.0 +CCTTGACCCCTC-2 CCTTGACCCCTC 397 -1311.2350966645668 0.000999000999000999 True 0.0 N703 knockout female 416 6.0330862217988015 428.0 6.0614567 30.062294345094386 3.5 1.5040774 0.817757 416 428.0 +AGAGTTGCCACA-2 AGAGTTGCCACA 334 -1227.477777297785 0.000999000999000999 True 0.0 N703 knockout female 386 5.958424693029782 364.0 5.8998976 27.197793076804672 2.5 1.252763 0.6868132 386 364.0 +CTGTTTCGACAG-2 CTGTTTCGACAG 206 -848.6520388382162 0.000999000999000999 True 0.0 N703 knockout female 216 5.37989735354046 216.0 5.379897 31.481485929345865 2.0 1.0986123 0.9259259 216 216.0 +CGTGCTTGCTGA-2 CGTGCTTGCTGA 182 -742.4328632507406 0.001998001998001998 False 0.0 N703 knockout female 245 5.5053315359323625 202.00002 5.313206 31.188111744166065 3.0 1.3862944 1.4851484 245 202.00002 +ACTCCATGACGA-2 ACTCCATGACGA 187 -752.0924947998436 0.001998001998001998 False 0.0 N703 knockout female 211 5.356586274672012 209.0 5.3471074 37.320579612122984 0.0 0.0 0.0 211 209.0 +GTCAGCCCCGCA-2 GTCAGCCCCGCA 199 -819.7420491701766 0.000999000999000999 True 0.0 N703 knockout female 229 5.438079308923196 216.0 5.379897 31.944448957718595 2.333333 1.2039727 1.0802467 229 216.0 +CGTCTAATTGGC-2 CGTCTAATTGGC 174 -784.3126181443054 0.000999000999000999 True 0.0 N703 knockout female 218 5.389071729816501 189.0 5.247024 31.216931216931215 6.0 1.9459102 3.1746035 218 189.0 +CCACCCCGAAGT-2 CCACCCCGAAGT 196 -756.0403326953127 0.016983016983016984 False 0.0 N703 knockout female 221 5.402677381872279 208.0 5.3423343 34.13462039281704 3.0 1.3862944 1.4423077 221 208.0 +GGTGTCTCTTCG-2 GGTGTCTCTTCG 166 -743.5653867605473 0.000999000999000999 True 0.0 N703 knockout female 207 5.337538079701318 186.0 5.2311087 33.33333059878355 2.5 1.252763 1.344086 207 186.0 +CCACATGACAGT-2 CCACATGACAGT 168 -701.2071290765266 0.001998001998001998 False 0.0 N703 knockout female 187 5.236441962829949 183.0 5.214936 36.612024910683964 2.5 1.252763 1.3661202 187 183.0 +CCGCGAGGACCT-2 CCGCGAGGACCT 179 -723.3199904544801 0.003996003996003996 False 0.0 N703 knockout female 210 5.351858133476067 197.00002 5.288267 36.54822618112525 3.5 1.5040774 1.7766496 210 197.00002 +CATCAACGAGCT-2 CATCAACGAGCT 173 -705.1762834602287 0.003996003996003996 False 0.0 N703 knockout female 217 5.384495062789089 193.0 5.267858 35.23316619290623 1.5 0.91629076 0.77720207 217 193.0 +CCAGCAACTCTG-2 CCAGCAACTCTG 168 -719.1530447714448 0.000999000999000999 True 0.0 N703 knockout female 239 5.480638923341991 190.0 5.2522736 33.157900062625295 0.5 0.4054651 0.2631579 239 190.0 +GAGTTTGAGGTT-2 GAGTTTGAGGTT 165 -710.7681402440315 0.000999000999000999 True 0.0 N703 knockout female 212 5.3612921657094255 184.0 5.220356 35.86957414118911 2.0 1.0986123 1.0869565 212 184.0 +GTCGGGGAGTCC-2 GTCGGGGAGTCC 171 -758.0376432970845 0.000999000999000999 True 0.0 N703 knockout female 208 5.342334251964811 190.0 5.2522736 30.52632314412725 1.5 0.91629076 0.78947365 208 190.0 +ATAATTATCAAC-2 ATAATTATCAAC 168 -678.8842671128609 0.01898101898101898 False 0.0 N703 knockout female 226 5.424950017481403 187.0 5.236442 35.828888699581924 4.0 1.609438 2.1390374 226 187.0 +AAGCTGTTGGGT-2 AAGCTGTTGGGT 166 -760.7727325101281 0.000999000999000999 True 0.0 N703 knockout female 231 5.44673737166631 182.0 5.209486 31.868139883558644 4.0 1.609438 2.1978023 231 182.0 +TGAAGAGCCCCC-2 TGAAGAGCCCCC 160 -635.8287604159189 0.11888111888111888 False 0.0 N703 knockout female 190 5.25227342804663 177.0 5.1817837 41.24293785310734 1.5 0.91629076 0.84745765 190 177.0 +GCGCAGGAAGGC-2 GCGCAGGAAGGC 146 -618.1037840672358 0.006993006993006993 False 0.009932096888618628 N703 knockout female 167 5.123963979403259 157.0 5.062595 43.312085072867006 1.0 0.6931472 0.6369427 167 157.0 +CGTTACAGCTCA-2 CGTTACAGCTCA 163 -702.7375447938359 0.000999000999000999 True 0.0 N703 knockout female 215 5.375278407684165 178.99998 5.192957 35.75419908769467 3.0 1.3862944 1.6759777 215 178.99998 +TGAGTTGCGCCG-2 TGAGTTGCGCCG 151 -640.2181197020163 0.002997002997002997 False 0.0 N703 knockout female 156 5.056245805348308 158.0 5.0689044 38.607594936708864 1.5 0.91629076 0.94936705 156 158.0 +CAGAAGGACTTC-2 CAGAAGGACTTC 148 -631.0120534494708 0.001998001998001998 False 0.003375934410417169 N703 knockout female 213 5.365976015021851 171.0 5.1474943 35.67252735250457 1.0 0.6931472 0.58479536 213 171.0 +AAGGATTCACTG-2 AAGGATTCACTG 156 -641.8928649437239 0.016983016983016984 False 0.0 N703 knockout female 168 5.1298987149230735 163.0 5.0998664 38.036814597065856 1.0 0.6931472 0.6134969 168 163.0 +CACCGATCGTGC-2 CACCGATCGTGC 157 -652.0416003673944 0.007992007992007992 False 0.0 N703 knockout female 193 5.267858159063328 174.0 5.164786 35.05747126436782 4.5 1.7047482 2.586207 193 174.0 +ATCATACTATCC-2 ATCATACTATCC 139 -608.7778524620744 0.001998001998001998 False 0.003375934410417169 N703 knockout female 195 5.278114659230518 162.0 5.09375 33.95061408614038 0.5 0.4054651 0.30864197 195 162.0 +CACGCGACTCAC-2 CACGCGACTCAC 147 -649.1625327547363 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 186 5.231108616854587 164.0 5.1059456 37.195128872592996 1.0 0.6931472 0.60975605 186 164.0 +TTTCGGAATCCC-2 TTTCGGAATCCC 150 -641.0585906942097 0.001998001998001998 False 0.003375934410417169 N703 knockout female 192 5.262690188904886 169.0 5.1357985 37.27811660625298 1.0 0.6931472 0.591716 192 169.0 +GCCACATTACAG-2 GCCACATTACAG 144 -622.0673660082189 0.001998001998001998 False 0.003375934410417169 N703 knockout female 159 5.075173815233827 154.0 5.043425 40.25974025974026 1.0 0.6931472 0.64935064 159 154.0 +CATACGAAACCG-2 CATACGAAACCG 131 -574.7843555967071 0.003996003996003996 False 0.006024744486282947 N703 knockout female 167 5.123963979403259 149.0 5.0106354 38.25503099684747 1.5 0.91629076 1.0067114 167 149.0 +GATCCGCTCGTC-2 GATCCGCTCGTC 146 -652.10122977006 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 189 5.247024072160486 160.99998 5.0875964 34.16149392088839 0.5 0.4054651 0.31055903 189 160.99998 +ACCCGCCTGCAA-2 ACCCGCCTGCAA 145 -616.7128366505202 0.004995004995004995 False 0.0074168255986437805 N703 knockout female 182 5.209486152841421 163.0 5.0998664 37.423312883435585 2.0 1.0986123 1.2269938 182 163.0 +TAAATCAGCCTA-2 TAAATCAGCCTA 137 -594.7549081746274 0.002997002997002997 False 0.004737198285585382 N703 knockout female 184 5.220355825078325 152.0 5.030438 38.15790239793926 4.0 1.609438 2.631579 184 152.0 +CTCCGCTGGCGC-2 CTCCGCTGGCGC 126 -573.6816841017137 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 163 5.099866427824199 143.0 4.9698133 41.25874566124823 3.0 1.3862944 2.097902 163 143.0 +GCAGGTCAGAGC-2 GCAGGTCAGAGC 131 -617.1780956044493 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 184 5.220355825078325 149.99998 5.0172796 36.0 4.0 1.609438 2.666667 184 149.99998 +GCGAGTGCAGCA-2 GCGAGTGCAGCA 143 -628.9349036824252 0.001998001998001998 False 0.003375934410417169 N703 knockout female 159 5.075173815233827 154.0 5.043425 38.311688311688314 0.0 0.0 0.0 159 154.0 +TTTTTGACATTC-2 TTTTTGACATTC 124 -568.5967671627595 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 152 5.030437921392435 139.0 4.9416423 41.72661412447767 2.0 1.0986123 1.438849 152 139.0 +CCAAGAGATTTG-2 CCAAGAGATTTG 122 -597.321066491653 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 156 5.056245805348308 135.0 4.912655 38.518514164845314 1.0 0.6931472 0.7407407 156 135.0 +GGTGTAGTGCAG-2 GGTGTAGTGCAG 115 -523.5498581338138 0.003996003996003996 False 0.006024744486282947 N703 knockout female 157 5.062595033026967 128.0 4.8598123 44.5312526542695 0.0 0.0 0.0 157 128.0 +CTCGCGAGACGC-2 CTCGCGAGACGC 113 -541.8329540382795 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 151 5.0238805208462765 126.0 4.8441873 44.44444982673401 1.5 0.91629076 1.1904762 151 126.0 +GTTGCCTCCGGC-2 GTTGCCTCCGGC 120 -540.7478829936797 0.002997002997002997 False 0.004737198285585382 N703 knockout female 162 5.093750200806762 134.99998 4.912655 42.96296296296296 1.0 0.6931472 0.74074084 162 134.99998 +GATGGGCCGCTG-2 GATGGGCCGCTG 134 -591.9098846092123 0.001998001998001998 False 0.003375934410417169 N703 knockout female 152 5.030437921392435 144.0 4.9767337 41.66666666666667 1.0 0.6931472 0.6944445 152 144.0 +GAACGTAATGCC-2 GAACGTAATGCC 104 -485.45432556736876 0.005994005994005994 False 0.00876735205093414 N703 knockout female 123 4.820281565605037 113.99999 4.744932 49.122807017543856 0.5 0.4054651 0.43859655 123 113.99999 +AGTCGATGCTGT-2 AGTCGATGCTGT 119 -541.1082416280009 0.001998001998001998 False 0.003375934410417169 N703 knockout female 133 4.897839799950911 128.0 4.8598123 45.3125 4.0 1.609438 3.125 133 128.0 +ATGTAGCCTTCT-2 ATGTAGCCTTCT 113 -531.8418586877461 0.001998001998001998 False 0.003375934410417169 N703 knockout female 143 4.969813299576001 127.0 4.8520303 43.30708661417323 0.5 0.4054651 0.39370078 143 127.0 +TCCCTGCTCCAG-2 TCCCTGCTCCAG 119 -551.0297060300095 0.001998001998001998 False 0.003375934410417169 N703 knockout female 207 5.337538079701318 136.00002 4.919981 41.91176000350952 0.5 0.4054651 0.36764702 207 136.00002 +GGATTATTTCGT-2 GGATTATTTCGT 106 -501.54905057197686 0.001998001998001998 False 0.003375934410417169 N703 knockout female 160 5.081404364984463 126.0 4.8441873 40.476190476190474 1.5 0.91629076 1.1904762 160 126.0 +CTCGCCGGTGCC-2 CTCGCCGGTGCC 117 -556.6924274587442 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 162 5.093750200806762 132.0 4.890349 41.66666666666667 1.5 0.91629076 1.1363636 162 132.0 +ATGTAATCGGCT-2 ATGTAATCGGCT 116 -530.8948704481976 0.001998001998001998 False 0.003375934410417169 N703 knockout female 148 5.003946305945459 130.0 4.8751974 43.84615384615385 2.0 1.0986123 1.5384616 148 130.0 +GCCCGTGGAGTA-2 GCCCGTGGAGTA 131 -578.606770033291 0.002997002997002997 False 0.004737198285585382 N703 knockout female 185 5.225746673713202 148.0 5.0039463 41.21621621621622 1.5 0.91629076 1.0135134 185 148.0 +ATCTTTAAGAAG-2 ATCTTTAAGAAG 110 -511.0075188182034 0.001998001998001998 False 0.003375934410417169 N703 knockout female 138 4.9344739331306915 123.99999 4.828314 48.38709379706267 1.0 0.6931472 0.8064517 138 123.99999 +TAACCGTTCGAC-2 TAACCGTTCGAC 120 -554.5526674690219 0.001998001998001998 False 0.003375934410417169 N703 knockout female 154 5.043425116919247 137.0 4.9272537 43.065698227229234 1.5 0.91629076 1.0948905 154 137.0 +TTAACTACATCC-2 TTAACTACATCC 108 -503.09139122987176 0.001998001998001998 False 0.003375934410417169 N703 knockout female 143 4.969813299576001 121.0 4.804021 46.28100632628333 0.0 0.0 0.0 143 121.0 +ATTTTCGACCCG-2 ATTTTCGACCCG 118 -540.2286915661391 0.001998001998001998 False 0.003375934410417169 N703 knockout female 168 5.1298987149230735 134.99998 4.912655 42.22222699451893 1.5 0.91629076 1.1111113 168 134.99998 +AATTCCTGACGC-2 AATTCCTGACGC 108 -511.0270028660607 0.001998001998001998 False 0.003375934410417169 N703 knockout female 152 5.030437921392435 119.00001 4.787492 47.899150451054204 3.0 1.3862944 2.5210083 152 119.00001 +TTCCATCACCAT-2 TTCCATCACCAT 114 -524.7865561333665 0.001998001998001998 False 0.003375934410417169 N703 knockout female 127 4.852030263919617 123.0 4.8202815 46.34146341463415 0.5 0.4054651 0.40650403 127 123.0 +GGAACCTAGTTC-2 GGAACCTAGTTC 119 -555.862463685298 0.001998001998001998 False 0.003375934410417169 N703 knockout female 152 5.030437921392435 129.0 4.8675346 41.86046511627907 1.0 0.6931472 0.7751938 152 129.0 +CACCCTTGATGC-2 CACCCTTGATGC 109 -510.02723895915403 0.001998001998001998 False 0.003375934410417169 N703 knockout female 134 4.90527477843843 120.0 4.7957907 45.83333624733837 1.0 0.6931472 0.8333334 134 120.0 +CATACCCCGATT-2 CATACCCCGATT 116 -533.8933457382059 0.001998001998001998 False 0.003375934410417169 N703 knockout female 188 5.241747015059643 135.00002 4.912655 40.74073613589408 0.5 0.4054651 0.37037033 188 135.00002 +CGGTCTATCGCT-2 CGGTCTATCGCT 107 -499.09665147196085 0.002997002997002997 False 0.004737198285585382 N703 knockout female 135 4.912654885736052 120.0 4.7957907 44.999997138977236 2.0 1.0986123 1.6666667 135 120.0 +CGTAAAATGTGC-2 CGTAAAATGTGC 116 -533.1865299414258 0.001998001998001998 False 0.003375934410417169 N703 knockout female 138 4.9344739331306915 127.0 4.8520303 44.88189246001311 1.5 0.91629076 1.1811024 138 127.0 +TTAGTAACGTCC-2 TTAGTAACGTCC 111 -524.0453060326985 0.001998001998001998 False 0.003375934410417169 N703 knockout female 145 4.983606621708336 124.0 4.828314 46.77419930417452 0.5 0.4054651 0.40322578 145 124.0 +CTCGTGAAGCAG-2 CTCGTGAAGCAG 102 -482.21145682185005 0.003996003996003996 False 0.006024744486282947 N703 knockout female 132 4.890349128221754 112.999985 4.7361984 52.212392905742874 1.0 0.6931472 0.8849558 132 112.999985 +ATCTTACTCTAT-2 ATCTTACTCTAT 104 -484.857420871059 0.006993006993006993 False 0.009932096888618628 N703 knockout female 133 4.897839799950911 117.0 4.7706847 48.7179518947733 1.5 0.91629076 1.2820513 133 117.0 +TTACTCATGACG-2 TTACTCATGACG 101 -506.0943713193028 0.000999000999000999 True 0.0027195027195027195 N703 knockout female 126 4.844187086458591 110.0 4.7095304 48.1818148400175 2.0 1.0986123 1.8181818 126 110.0 +TGTTCGTGATTT-3 TGTTCGTGATTT 229 -885.7581587270417 0.0969030969030969 nan N704 wildtype male 318 5.765191102784844 256.0 5.549076 28.125 1.0 0.6931472 0.390625 318 256.0 +CATCCATATTCC-3 CATCCATATTCC 354 -1222.534517124025 0.030969030969030968 nan N704 wildtype male 414 6.028278520230698 390.0 5.9687076 26.923072860086073 4.0 1.609438 1.0256411 414 390.0 +TTGTTTCCGTCG-3 TTGTTTCCGTCG 244 -906.57995838804 0.3386613386613387 nan N704 wildtype male 305 5.723585101952381 275.0 5.620401 27.63635750258401 1.0 0.6931472 0.36363637 305 275.0 +ATGCTTTAGTTT-3 ATGCTTTAGTTT 242 -910.5148577760874 0.21878121878121878 nan N704 wildtype male 299 5.703782474656201 269.0 5.598422 31.598520180745787 3.0 1.3862944 1.1152416 299 269.0 +CACGCTCCAAAG-4 CACGCTCCAAAG 263 -987.8145213482136 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 304 5.720311776607412 290.0 5.673323 31.379313646967777 4.0 1.609438 1.3793104 304 290.0 +TGCTTGGTCGTG-4 TGCTTGGTCGTG 314 -1328.0637374145222 0.000999000999000999 True 0.0 N705 wildtype male 404 6.003887067106539 346.0 5.8493247 23.41040875392473 4.833333 1.7635885 1.3969171 404 346.0 +ATACCCGCCATT-4 ATACCCGCCATT 277 -1102.4268925335523 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 380 5.942799375126701 307.0 5.7300997 28.013015392681012 3.5 1.5040774 1.1400651 380 307.0 +GTCCTTCTAGCA-4 GTCCTTCTAGCA 293 -1148.797704391 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 357 5.8805329864007 321.00003 5.774552 27.72585406191303 6.0 1.9459102 1.8691586 357 321.00003 +GATCATCGACAT-4 GATCATCGACAT 277 -855.0806341803705 0.007992007992007992 False 0.009102009102009102 N705 wildtype male 286 5.659482215759621 304.0 5.7203116 45.723670440371535 3.5 1.5040774 1.1513158 286 304.0 +GCTTCATCCGTC-4 GCTTCATCCGTC 584 -1952.578780842442 0.000999000999000999 True 0.0 N705 wildtype male 586 6.375024819828097 629.0 6.4457197 24.324317243377568 12.5 2.6026897 1.9872814 586 629.0 +CCGTCTGACCTT-4 CCGTCTGACCTT 571 -1957.3134631514636 0.000999000999000999 True 0.0 N705 wildtype male 612 6.418364935936212 626.0 6.4409466 22.52353997276233 6.5 2.014903 1.0383387 612 626.0 +AGCGATCGTGGC-4 AGCGATCGTGGC 262 -1094.6698211037622 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 359 5.886104031450156 300.0 5.7071104 26.0 2.5 1.252763 0.8333334 359 300.0 +TTTGAATCAGGG-4 TTTGAATCAGGG 236 -950.0486204008344 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 298 5.700443573390686 268.0 5.5947113 29.850746268656714 4.5 1.7047482 1.6791046 298 268.0 +AGCGGACCCTTT-4 AGCGGACCCTTT 235 -976.5108744204255 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 333 5.8111409929767 269.0 5.598422 30.111524163568777 4.5 1.7047482 1.6728625 333 269.0 +CCCAGCTCTCCG-4 CCCAGCTCTCCG 214 -939.5874767828609 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 295 5.69035945432406 248.0 5.5174527 29.435487493144315 4.833333 1.7635885 1.9489245 295 248.0 +AGTCCGGAATTC-4 AGTCCGGAATTC 209 -865.9083742757545 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 331 5.805134968916488 249.0 5.521461 28.915676826287978 3.5 1.5040774 1.4056225 331 249.0 +CACCGTCTTGGA-4 CACCGTCTTGGA 210 -867.0255777978524 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 336 5.820082930352362 245.0 5.5053315 30.612244897959183 2.0 1.0986123 0.8163265 336 245.0 +TACGTTGGTCAT-4 TACGTTGGTCAT 206 -820.5327764690379 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 282 5.645446897643238 241.0 5.488938 29.460588373990106 5.0 1.7917595 2.074689 282 241.0 +TAACCATCGCCT-4 TAACCATCGCCT 198 -847.56975541266 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 297 5.697093486505405 233.0 5.4553213 29.18454553372683 2.833333 1.3437346 1.2160227 297 233.0 +AGTATCTGGTCC-4 AGTATCTGGTCC 208 -870.806931349005 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 279 5.634789603169249 234.0 5.4595857 29.914537717258476 4.0 1.609438 1.7094018 279 234.0 +GGTCCCCACGCA-4 GGTCCCCACGCA 212 -898.0357838603915 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 293 5.683579767338681 241.00002 5.488938 28.630707206929134 1.5 0.91629076 0.6224066 293 241.00002 +CTAACCTCCTCT-4 CTAACCTCCTCT 187 -826.2471298779619 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 253 5.537334267018537 215.00002 5.3752785 31.627906976744185 0.5 0.4054651 0.23255812 253 215.00002 +TTCCTTTCAGCG-4 TTCCTTTCAGCG 194 -800.2517387115299 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 237 5.472270673671475 218.0 5.389072 33.48624321981213 2.5 1.252763 1.146789 237 218.0 +CCTTTCGTATCA-4 CCTTTCGTATCA 186 -792.5252524204233 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 298 5.700443573390686 225.0 5.420535 30.666666666666664 4.5 1.7047482 2.0 298 225.0 +TTTCCAAACATG-4 TTTCCAAACATG 191 -784.2174091546372 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 239 5.480638923341991 221.0 5.4026775 33.93665626997538 7.5 2.1400661 3.3936653 239 221.0 +TTTCCAGATTAA-4 TTTCCAGATTAA 199 -796.3055719864309 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 265 5.583496308781699 226.0 5.42495 35.84070796460177 0.0 0.0 0.0 265 226.0 +TCGCCCAGGTTT-4 TCGCCCAGGTTT 196 -843.794284526716 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 243 5.497168225293202 220.0 5.398163 33.18182968896682 1.0 0.6931472 0.45454544 243 220.0 +CTCAGTTCTAGT-4 CTCAGTTCTAGT 207 -872.4968487151901 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 269 5.598421958998375 230.0 5.4424176 31.739132540437947 2.5 1.252763 1.0869565 269 230.0 +CTACATTTAGCA-4 CTACATTTAGCA 169 -748.1979545978127 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 272 5.60947179518496 202.99998 5.31812 31.03448042586901 3.0 1.3862944 1.4778327 272 202.99998 +GCCCGGATTGGC-4 GCCCGGATTGGC 187 -790.6194467957084 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 256 5.54907608489522 215.0 5.3752785 32.09301870045951 5.0 1.7917595 2.3255813 256 215.0 +GACAAGACCACT-4 GACAAGACCACT 170 -717.386367009595 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 244 5.501258210544727 204.0 5.32301 34.80393458494844 4.0 1.609438 1.9607844 244 204.0 +GTGTTGTGGTAT-4 GTGTTGTGGTAT 178 -713.0606403736595 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 286 5.659482215759621 209.0 5.3471074 34.44977585632143 3.0 1.3862944 1.4354067 286 209.0 +AAACCAGAGTGA-4 AAACCAGAGTGA 178 -787.3930819691197 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 280 5.638354669333745 209.0 5.3471074 33.97130922740331 5.0 1.7917595 2.3923445 280 209.0 +CAGTCGCTCCTT-4 CAGTCGCTCCTT 195 -808.3148109151884 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 287 5.662960480135946 226.0 5.42495 35.39823486844424 0.5 0.4054651 0.22123894 287 226.0 +ATAGTCCGCGAA-4 ATAGTCCGCGAA 178 -786.8280762221325 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 257 5.552959584921617 214.0 5.370638 30.841125893441447 2.5 1.252763 1.1682243 257 214.0 +TCGTGCGTCAGT-4 TCGTGCGTCAGT 167 -690.2325326506494 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 223 5.4116460518550396 196.99998 5.288267 38.578625433529865 0.5 0.4054651 0.25380713 223 196.99998 +AATTAGTTGTGA-4 AATTAGTTGTGA 157 -698.3202339678131 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 263 5.575949103146316 186.99998 5.236442 34.759386651665714 1.5 0.91629076 0.8021391 263 186.99998 +GCTAGGGCAGCC-4 GCTAGGGCAGCC 157 -667.0782010978269 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 237 5.472270673671475 187.99998 5.241747 36.17022450880481 1.0 0.6931472 0.53191495 237 187.99998 +GCTCCGTGATGT-4 GCTCCGTGATGT 161 -695.4106329165852 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 221 5.402677381872279 189.0 5.247024 35.44974403576984 2.0 1.0986123 1.0582011 221 189.0 +CAAAATACTGGG-4 CAAAATACTGGG 157 -682.8958264143544 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 227 5.429345628954441 191.0 5.2574954 35.60209992926631 3.0 1.3862944 1.5706805 227 191.0 +CCACCGCGGTTA-4 CCACCGCGGTTA 163 -680.3255413787629 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 231 5.44673737166631 193.0 5.267858 36.787570583769735 0.5 0.4054651 0.25906736 231 193.0 +AAGGGTTAGCCG-4 AAGGGTTAGCCG 159 -659.4919836674642 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 224 5.41610040220442 189.0 5.247024 38.624344860960115 2.0 1.0986123 1.0582011 224 189.0 +TCCGTCGAATCT-4 TCCGTCGAATCT 162 -702.5992372377762 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 215 5.375278407684165 186.0 5.2311087 33.87096436785334 4.0 1.609438 2.1505377 215 186.0 +TACGCATGACTC-4 TACGCATGACTC 166 -740.5552544147722 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 251 5.529429087511423 194.0 5.273 33.50516254507538 2.0 1.0986123 1.0309278 251 194.0 +GTGCGGTCGCGG-4 GTGCGGTCGCGG 154 -698.3012148817704 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 214 5.3706380281276624 176.0 5.17615 35.22728188964199 2.5 1.252763 1.4204546 214 176.0 +ACGAAGACTGCT-4 ACGAAGACTGCT 169 -714.5463657522557 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 226 5.424950017481403 190.0 5.2522736 35.26316355866499 3.833333 1.5755363 2.0175436 226 190.0 +GTGGTCGTGAGT-4 GTGGTCGTGAGT 142 -621.6534371201761 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 202 5.313205979041787 173.0 5.159055 39.88440009927917 1.5 0.91629076 0.867052 202 173.0 +ATCAAACCCGGT-4 ATCAAACCCGGT 153 -711.7710965324725 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 204 5.3230099791384085 174.0 5.164786 35.63218703278176 5.5 1.8718022 3.1609197 204 174.0 +GCTGGTTAGCAA-4 GCTGGTTAGCAA 158 -674.5891777396863 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 213 5.365976015021851 182.0 5.209486 37.36263423017277 2.0 1.0986123 1.0989012 213 182.0 +GATCAGATTTAA-4 GATCAGATTTAA 147 -675.4689351633986 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 215 5.375278407684165 169.0 5.1357985 34.91124890773234 3.5 1.5040774 2.0710058 215 169.0 +TACAACTCCTCC-4 TACAACTCCTCC 139 -654.9702518916838 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 215 5.375278407684165 161.99998 5.09375 35.18518849928006 3.5 1.5040774 2.160494 215 161.99998 +ATTTGTTGGTCC-4 ATTTGTTGGTCC 155 -693.6417140542374 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 237 5.472270673671475 186.0 5.2311087 35.483876789688075 0.5 0.4054651 0.26881722 237 186.0 +CCCAAAATGGCA-4 CCCAAAATGGCA 135 -597.6758501118557 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 238 5.476463551931511 169.99998 5.1416636 37.058836834658216 1.0 0.6931472 0.5882354 238 169.99998 +GTGAGCGGCCCA-4 GTGAGCGGCCCA 141 -655.0758149625667 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 208 5.342334251964811 169.99998 5.1416636 35.29412398288933 4.0 1.609438 2.3529415 208 169.99998 +TATAAACTAGGG-4 TATAAACTAGGG 143 -614.9367840832033 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 222 5.407171771460119 168.0 5.1298985 35.119060377960345 3.0 1.3862944 1.7857144 222 168.0 +GTGTAATGGAAA-4 GTGTAATGGAAA 148 -638.0553044980863 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 222 5.407171771460119 169.0 5.1357985 39.64497757318758 1.0 0.6931472 0.591716 222 169.0 +CAGGTTATAGGC-4 CAGGTTATAGGC 118 -542.6514600659976 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 239 5.480638923341991 156.0 5.056246 39.10256792729382 2.0 1.0986123 1.2820513 239 156.0 +ACCCCAATACCG-4 ACCCCAATACCG 140 -597.2412051009387 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 220 5.3981627015177525 165.0 5.1119876 43.636343459461806 3.0 1.3862944 1.8181818 220 165.0 +AGGTTGTCTGTC-4 AGGTTGTCTGTC 128 -582.0747042463345 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 183 5.214935757608986 152.0 5.030438 38.1578985673903 2.0 1.0986123 1.3157895 183 152.0 +GCAGAAACGTTA-4 GCAGAAACGTTA 137 -598.4335889663387 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 214 5.3706380281276624 166.0 5.117994 37.95180025182513 0.5 0.4054651 0.3012048 214 166.0 +CGTCGCGCTCTT-4 CGTCGCGCTCTT 133 -608.4123485533646 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 228 5.43372200355424 163.99998 5.1059456 39.63414634146341 2.0 1.0986123 1.2195123 228 163.99998 +ATGGCTGTATAG-4 ATGGCTGTATAG 134 -581.2803351563514 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 172 5.153291594497779 154.0 5.043425 40.25974424879835 5.0 1.7917595 3.2467532 172 154.0 +GGTTTGCTATTT-4 GGTTTGCTATTT 130 -565.0771168059714 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 168 5.1298987149230735 147.0 4.9972124 40.13606692029481 0.5 0.4054651 0.34013605 168 147.0 +GATTTTGAGGCA-4 GATTTTGAGGCA 119 -540.3683262789506 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 202 5.313205979041787 149.99998 5.0172796 40.0 1.0 0.6931472 0.66666675 202 149.99998 +AGCCCGATTTAC-4 AGCCCGATTTAC 133 -628.6224894011692 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 186 5.231108616854587 153.99998 5.043425 39.61038568567195 3.0 1.3862944 1.948052 186 153.99998 +TGGTCTGTCGTC-4 TGGTCTGTCGTC 128 -598.1744526537285 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 192 5.262690188904886 153.99998 5.043425 39.61039353510805 2.5 1.252763 1.6233768 192 153.99998 +ATTCGGGACGTA-4 ATTCGGGACGTA 116 -547.0682714318971 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 203 5.318119993844216 144.0 4.9767337 39.583337527734706 3.0 1.3862944 2.0833335 203 144.0 +AACAGCATTGAC-4 AACAGCATTGAC 112 -520.7567063821155 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 206 5.332718793265369 138.99998 4.9416423 42.44606180361161 0.5 0.4054651 0.35971227 206 138.99998 +TGCACCTCCGAG-4 TGCACCTCCGAG 128 -590.951817964267 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 187 5.236441962829949 148.0 5.0039463 40.540548899995734 3.0 1.3862944 2.027027 187 148.0 +CTGTGAAGGTTT-4 CTGTGAAGGTTT 124 -559.7478452151013 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 220 5.3981627015177525 151.99998 5.030438 42.105267384706536 4.0 1.609438 2.6315792 220 151.99998 +CGCGCATGCTCC-4 CGCGCATGCTCC 120 -572.7592454722012 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 218 5.389071729816501 150.0 5.0172796 38.6666706000438 1.0 0.6931472 0.6666667 218 150.0 +CATTTCTTCGAT-4 CATTTCTTCGAT 123 -554.5586773457007 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 213 5.365976015021851 150.0 5.0172796 39.33333733452732 3.5 1.5040774 2.3333333 213 150.0 +ATGTCATTGCTT-4 ATGTCATTGCTT 129 -584.1227696293812 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 213 5.365976015021851 148.0 5.0039463 40.540544720267704 2.5 1.252763 1.6891891 213 148.0 +GCCTGTTCTTTA-4 GCCTGTTCTTTA 113 -533.8959957085619 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 158 5.0689042022202315 135.0 4.912655 45.18518518518518 0.5 0.4054651 0.37037036 158 135.0 +TTGCCTCCCCTC-4 TTGCCTCCCCTC 119 -563.042787461033 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 164 5.10594547390058 136.0 4.919981 43.3823480738081 0.5 0.4054651 0.36764705 164 136.0 +TCGGCCCCACGG-4 TCGGCCCCACGG 110 -558.8298504608326 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 175 5.170483995038151 130.0 4.8751974 40.76923076923077 0.5 0.4054651 0.3846154 175 130.0 +CAGAAGACAGAC-4 CAGAAGACAGAC 110 -504.04153706151556 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 163 5.099866427824199 130.99998 4.882802 45.03815744690467 2.0 1.0986123 1.5267178 163 130.99998 +CCGCAGTCCCTC-4 CCGCAGTCCCTC 133 -638.9159773219259 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 169 5.135798437050262 145.0 4.983607 39.99999158135953 4.5 1.7047482 3.1034482 169 145.0 +TCCCGATGTTAT-4 TCCCGATGTTAT 133 -579.5284592831357 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 168 5.1298987149230735 148.0 5.0039463 44.594599192294474 6.0 1.9459102 4.054054 168 148.0 +GTCTTTTAGGAT-4 GTCTTTTAGGAT 123 -577.1805664342082 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 192 5.262690188904886 148.0 5.0039463 39.86487719506254 1.5 0.91629076 1.0135134 192 148.0 +CAGAGTATGGCC-4 CAGAGTATGGCC 114 -530.1774688624108 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 209 5.3471075307174685 145.99998 4.9904323 38.35615636618494 2.0 1.0986123 1.3698632 209 145.99998 +TTCTTGCCGACG-4 TTCTTGCCGACG 132 -591.7213252433271 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 155 5.049856007249537 145.0 4.983607 42.758620689655174 2.5 1.252763 1.7241379 155 145.0 +ACCCCGACCACT-4 ACCCCGACCACT 124 -592.1074106919411 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 176 5.176149732573829 149.0 5.0106354 38.92618246934775 0.5 0.4054651 0.33557048 176 149.0 +CATGAGGCATAC-4 CATGAGGCATAC 115 -523.3350952653241 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 150 5.017279836814924 126.00001 4.8441873 46.82540249602333 1.0 0.6931472 0.79365075 150 126.00001 +CGGCGAAACTCC-4 CGGCGAAACTCC 118 -549.5798145563863 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 169 5.135798437050262 140.0 4.94876 42.14286173606456 1.5 0.91629076 1.0714287 169 140.0 +AAGTAAGAAGTT-4 AAGTAAGAAGTT 114 -546.7319205261572 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 165 5.111987788356544 135.0 4.912655 43.7037037037037 3.0 1.3862944 2.2222223 165 135.0 +CTTTTTTCTTAG-4 CTTTTTTCTTAG 113 -526.8506393399093 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 183 5.214935757608986 136.0 4.919981 41.1764752081114 1.5 0.91629076 1.1029412 183 136.0 +CTACCGTAGCTT-4 CTACCGTAGCTT 111 -517.4482200936466 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 173 5.159055299214529 134.0 4.905275 47.76119946849305 2.0 1.0986123 1.4925373 173 134.0 +GTTCTCCATGCC-4 GTTCTCCATGCC 112 -534.8635528493023 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 170 5.14166355650266 133.0 4.89784 42.10525832725372 3.0 1.3862944 2.255639 170 133.0 +CAGATGACAGAA-4 CAGATGACAGAA 117 -541.4155205081261 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 170 5.14166355650266 138.0 4.934474 44.20289855072464 1.5 0.91629076 1.0869565 170 138.0 +CGTCAGTGCGAC-4 CGTCAGTGCGAC 117 -506.55366779943785 0.001998001998001998 False 0.002296768651908839 N705 wildtype male 160 5.081404364984463 133.0 4.89784 48.1203007518797 3.0 1.3862944 2.255639 160 133.0 +CTTGCCTTTGCT-4 CTTGCCTTTGCT 116 -515.5545426355874 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 170 5.14166355650266 133.0 4.89784 47.368415618160434 3.833333 1.5755363 2.8822052 170 133.0 +CTAATGCAGTGT-4 CTAATGCAGTGT 101 -508.98323885625484 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 148 5.003946305945459 122.0 4.8121843 46.72131147540984 2.0 1.0986123 1.6393442 148 122.0 +CCCCGCAAGAAA-4 CCCCGCAAGAAA 105 -495.5557632611211 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 157 5.062595033026967 127.99999 4.8598123 44.5312526542695 0.5 0.4054651 0.39062506 157 127.99999 +TATCTCTATCAG-4 TATCTCTATCAG 108 -513.9223652114415 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 169 5.135798437050262 127.0 4.8520303 44.88189515624701 0.0 0.0 0.0 169 127.0 +TATTAGTGTTGA-4 TATTAGTGTTGA 103 -489.37967557326226 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 175 5.170483995038151 129.0 4.8675346 44.96123499182945 2.5 1.252763 1.9379845 175 129.0 +CACGGTCCTCGC-4 CACGGTCCTCGC 106 -507.05776154460034 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 162 5.093750200806762 126.0 4.8441873 46.0317348827217 1.5 0.91629076 1.1904762 162 126.0 +GCTGGAAATTGT-4 GCTGGAAATTGT 106 -518.5882724312935 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 190 5.25227342804663 129.0 4.8675346 41.08527131782946 1.0 0.6931472 0.7751938 190 129.0 +CCAGAAAAGTAT-4 CCAGAAAAGTAT 112 -509.14846536623253 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 127 4.852030263919617 120.99999 4.804021 48.760336727468534 0.0 0.0 0.0 127 120.99999 +GTCCCTGCCGAA-4 GTCCCTGCCGAA 102 -477.7027944113992 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 163 5.099866427824199 126.0 4.8441873 46.03175718077576 1.5 0.91629076 1.1904762 163 126.0 +CCTTCATGTCCA-4 CCTTCATGTCCA 115 -530.5831631738016 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 159 5.075173815233827 136.0 4.919981 45.588235294117645 1.0 0.6931472 0.7352941 159 136.0 +AAGTGTGGTTAT-4 AAGTGTGGTTAT 109 -525.4325537229098 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 183 5.214935757608986 130.99998 4.882802 41.98474749549021 3.0 1.3862944 2.2900765 183 130.99998 +AGTTTTTTTGCC-4 AGTTTTTTTGCC 106 -475.00805051840086 0.001998001998001998 False 0.002296768651908839 N705 wildtype male 163 5.099866427824199 126.99999 4.8520303 47.24409732632959 1.0 0.6931472 0.7874017 163 126.99999 +GTACAGATCACG-4 GTACAGATCACG 112 -558.8293336185283 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 148 5.003946305945459 127.00001 4.8520303 44.09449083790762 0.0 0.0 0.0 148 127.00001 +CGTTTCGGATGA-4 CGTTTCGGATGA 105 -479.28185306597163 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 169 5.135798437050262 126.99999 4.8520303 48.03150183387839 2.0 1.0986123 1.5748034 169 126.99999 +TTCCCACTGCAC-4 TTCCCACTGCAC 111 -512.6057053463226 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 136 4.919980925828125 123.0 4.8202815 46.34146916353417 3.0 1.3862944 2.4390242 136 123.0 +GTCCGGGCAGCA-4 GTCCGGGCAGCA 102 -493.630625734797 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 141 4.955827057601261 117.0 4.7706847 46.15385217309312 2.833333 1.3437346 2.421652 141 117.0 +GTAGTCTCATGA-4 GTAGTCTCATGA 102 -495.6213586478311 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 162 5.093750200806762 124.0 4.828314 45.161281986615215 1.0 0.6931472 0.80645156 162 124.0 +ACCAATGGATCG-4 ACCAATGGATCG 102 -470.5698254072621 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 160 5.081404364984463 123.0 4.8202815 47.15446861983703 3.333333 1.466337 2.710027 160 123.0 +AGCACCTCGGCA-4 AGCACCTCGGCA 106 -486.92206455101666 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 163 5.099866427824199 121.000015 4.8040214 47.9338903422572 0.0 0.0 0.0 163 121.000015 +ATCGCGCTCAGT-4 ATCGCGCTCAGT 105 -529.8434280542499 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 133 4.897839799950911 120.0 4.7957907 47.50000301996886 2.5 1.252763 2.0833335 133 120.0 +CACTTAACGTCA-4 CACTTAACGTCA 104 -479.65131679505487 0.000999000999000999 True 0.001170258313115456 N705 wildtype male 112 4.727387818712341 108.00001 4.691348 54.629629629629626 2.5 1.252763 2.3148148 112 108.00001 +ATTCAAGATCGT-5 ATTCAAGATCGT 296 -1029.9596425890281 0.000999000999000999 True 0.0 N706 wildtype male 346 5.849324779946859 336.0 5.820083 31.84523003982725 3.833333 1.5755363 1.140873 346 336.0 +CCGCCTTGCTAT-5 CCGCCTTGCTAT 306 -1031.6366163252558 0.000999000999000999 True 0.0 N706 wildtype male 342 5.83773044716594 336.0 5.820083 34.22618425892707 4.5 1.7047482 1.3392857 342 336.0 +TAACCAGAATGA-5 TAACCAGAATGA 292 -1064.2911113174578 0.000999000999000999 True 0.0 N706 wildtype male 356 5.877735781779639 324.0 5.7838254 28.703692889293947 4.0 1.609438 1.2345679 356 324.0 +CTTGTCCGCAAC-5 CTTGTCCGCAAC 286 -1034.4270914165559 0.000999000999000999 True 0.0 N706 wildtype male 353 5.869296913133774 322.00003 5.7776523 31.0558976777926 2.5 1.252763 0.77639747 353 322.00003 +TTTCATCAACGT-5 TTTCATCAACGT 324 -1122.655557797145 0.000999000999000999 True 0.0 N706 wildtype male 346 5.849324779946859 351.0 5.8636312 31.623934373464067 2.5 1.252763 0.7122507 346 351.0 +ATGGTTTGGTTA-5 ATGGTTTGGTTA 268 -955.71144201148 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 368 5.910796644040527 310.0 5.739793 32.25806769173581 8.0 2.1972246 2.580645 368 310.0 +CTTTCTCCACAT-5 CTTTCTCCACAT 447 -1501.4900910860579 0.000999000999000999 True 0.0 N706 wildtype male 467 6.148468295917647 479.0 6.173786 28.914406852604742 2.0 1.0986123 0.41753656 467 479.0 +TACGTTCTCATT-5 TACGTTCTCATT 277 -928.5895835465506 0.015984015984015984 False 0.0 N706 wildtype male 323 5.780743515792329 311.0 5.7430034 35.04819226034968 2.5 1.252763 0.8038585 323 311.0 +CTGTTCCCTATT-5 CTGTTCCCTATT 272 -954.0805998701788 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 298 5.700443573390686 306.0 5.7268476 38.56204797702844 1.0 0.6931472 0.3267974 298 306.0 +CCCCATGTCTTG-5 CCCCATGTCTTG 266 -924.9633038414715 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 316 5.75890177387728 300.0 5.7071104 33.66667351616893 3.0 1.3862944 1.0 316 300.0 +CCTGTCCGCTGC-5 CCTGTCCGCTGC 270 -947.6738658071708 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 308 5.733341276897746 297.0 5.6970935 34.00673236338116 4.5 1.7047482 1.5151515 308 297.0 +ACTATCGTATCA-5 ACTATCGTATCA 289 -1035.2905869824808 0.000999000999000999 True 0.0 N706 wildtype male 353 5.869296913133774 322.0 5.7776523 30.745344528799546 7.0 2.0794415 2.173913 353 322.0 +CATTCGTTCCGC-5 CATTCGTTCCGC 275 -989.5063061152397 0.000999000999000999 True 0.0 N706 wildtype male 301 5.71042701737487 294.0 5.6869755 32.99316800978555 3.5 1.5040774 1.1904762 301 294.0 +ACCGATTCGGAC-5 ACCGATTCGGAC 281 -969.0482204927415 0.000999000999000999 True 0.0 N706 wildtype male 328 5.796057750765372 316.99997 5.762051 33.123018824911846 3.5 1.5040774 1.1041011 328 316.99997 +CCGCTCGTCCCC-5 CCGCTCGTCCCC 260 -905.8112245557008 0.001998001998001998 False 0.005015392770494811 N706 wildtype male 309 5.736572297479192 293.0 5.68358 34.81229031531328 3.5 1.5040774 1.1945393 309 293.0 +GCTCCTGATGTC-5 GCTCCTGATGTC 262 -971.7874105050363 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 311 5.7430031878094825 292.0 5.6801724 32.191774093048075 1.5 0.91629076 0.51369864 311 292.0 +GCTTGACAACTG-5 GCTTGACAACTG 265 -950.2785152591149 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 281 5.641907070938114 286.0 5.659482 34.79021350249321 5.0 1.7917595 1.7482517 281 286.0 +AGCTAGTTCCCT-5 AGCTAGTTCCCT 267 -972.450980638338 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 325 5.786897381366708 291.0 5.676754 28.865988463093384 7.5 2.1400661 2.5773196 325 291.0 +CGATCGGATTCG-5 CGATCGGATTCG 254 -912.1877973894223 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 292 5.680172609017068 281.0 5.641907 33.09609435648282 2.5 1.252763 0.8896797 292 281.0 +TGAAGTATAGCA-5 TGAAGTATAGCA 274 -994.008017636968 0.000999000999000999 True 0.0 N706 wildtype male 350 5.860786223465865 306.00003 5.7268476 30.392156862745097 5.0 1.7917595 1.6339868 350 306.00003 +CCGATTATCCTC-5 CCGATTATCCTC 254 -961.022280703615 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 263 5.575949103146316 274.0 5.616771 33.94152429767788 5.5 1.8718022 2.0072992 263 274.0 +GCTTTCAGCCGC-5 GCTTTCAGCCGC 222 -843.3736121888421 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 304 5.720311776607412 259.0 5.560682 33.204631225660314 3.5 1.5040774 1.3513514 304 259.0 +TGTTTCTTTGGC-5 TGTTTCTTTGGC 224 -853.7794262694918 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 300 5.707110264748875 262.00003 5.572154 34.160309322480565 2.5 1.252763 0.95419836 300 262.00003 +GACTCGTCGCAA-5 GACTCGTCGCAA 211 -786.4262725008707 0.002997002997002997 False 0.007228066051595463 N706 wildtype male 253 5.537334267018537 239.0 5.480639 37.2384961013153 1.0 0.6931472 0.41841003 253 239.0 +TCGTAGTACTTG-5 TCGTAGTACTTG 205 -814.8189977482895 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 280 5.638354669333745 232.99998 5.455321 32.18885806568225 0.333333 0.28768182 0.14306138 280 232.99998 +AATCTACCCTCC-5 AATCTACCCTCC 211 -858.6609849572525 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 281 5.641907070938114 237.0 5.4722705 30.80169371303646 0.5 0.4054651 0.21097046 281 237.0 +GAAGCGCGGGCT-5 GAAGCGCGGGCT 198 -793.2199381391633 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 269 5.598421958998375 227.0 5.4293456 34.36124271913577 3.5 1.5040774 1.5418502 269 227.0 +ATGCGAGGTCAC-5 ATGCGAGGTCAC 206 -797.4315928627425 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 279 5.634789603169249 236.00002 5.46806 30.50846668606343 3.833333 1.5755363 1.6242936 279 236.00002 +TTGGGTGTTTTC-5 TTGGGTGTTTTC 207 -795.0651158923014 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 287 5.662960480135946 239.0 5.480639 33.47280762137666 4.5 1.7047482 1.8828452 287 239.0 +AGTCCGCGAGTG-5 AGTCCGCGAGTG 196 -796.6802810940701 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 288 5.666426688112432 234.0 5.4595857 32.051279961266154 2.0 1.0986123 0.8547009 288 234.0 +CCAAAGTCTGCA-5 CCAAAGTCTGCA 205 -796.4254292363225 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 255 5.545177444479562 229.0 5.4380794 35.80786741988685 1.0 0.6931472 0.43668124 255 229.0 +GTAGCAAGTTCG-5 GTAGCAAGTTCG 194 -774.766131620011 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 269 5.598421958998375 225.0 5.420535 31.555561975551072 4.0 1.609438 1.7777778 269 225.0 +TGTTAGTGTCCT-5 TGTTAGTGTCCT 197 -796.7331072638606 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 238 5.476463551931511 220.0 5.398163 35.00000728260539 1.5 0.91629076 0.6818182 238 220.0 +CGCTTGGTCGGA-5 CGCTTGGTCGGA 183 -740.7102557247711 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 210 5.351858133476067 201.0 5.3082676 35.32338576612981 2.0 1.0986123 0.99502486 210 201.0 +GCTTTGGACCGA-5 GCTTTGGACCGA 152 -639.3804367758787 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 202 5.313205979041787 169.99998 5.1416636 37.64706558174862 2.0 1.0986123 1.1764708 202 169.99998 +TTCAAGGCGTTG-5 TTCAAGGCGTTG 154 -644.7240484228446 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 208 5.342334251964811 179.0 5.192957 36.871514666085645 0.5 0.4054651 0.2793296 208 179.0 +TATTTTCTGGTC-5 TATTTTCTGGTC 154 -657.4892175536017 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 227 5.429345628954441 181.0 5.2040067 35.35912794557955 0.5 0.4054651 0.2762431 227 181.0 +ATGACCCGTTTT-5 ATGACCCGTTTT 143 -643.6828096086582 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 227 5.429345628954441 170.0 5.1416636 34.11766543273859 1.5 0.91629076 0.882353 227 170.0 +TCGTATCCATAT-5 TCGTATCCATAT 152 -642.3966142044655 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 200 5.303304908059076 171.99998 5.1532917 39.534894242812996 3.0 1.3862944 1.7441862 200 171.99998 +CGTCAAACTGTC-5 CGTCAAACTGTC 142 -597.6293188772925 0.003996003996003996 False 0.009273745122801727 N706 wildtype male 183 5.214935757608986 160.0 5.081404 41.25 2.0 1.0986123 1.25 183 160.0 +GTTTTCTGGGAT-5 GTTTTCTGGGAT 130 -583.3295639376855 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 176 5.176149732573829 153.0 5.0369525 40.52287177562122 3.0 1.3862944 1.9607844 176 153.0 +AAGCAAATAGCG-5 AAGCAAATAGCG 132 -571.5359135536528 0.001998001998001998 False 0.005015392770494811 N706 wildtype male 199 5.298317366548036 156.0 5.056246 40.38462723500977 2.5 1.252763 1.6025642 199 156.0 +TGGAAAACGTTT-5 TGGAAAACGTTT 148 -617.350183904081 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 210 5.351858133476067 167.0 5.123964 36.52695612018776 1.0 0.6931472 0.59880245 210 167.0 +TGTTTTTCCATT-5 TGTTTTTCCATT 135 -588.4389958224461 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 175 5.170483995038151 153.99998 5.043425 38.311688311688314 2.0 1.0986123 1.2987014 175 153.99998 +TAGCCAACATTT-5 TAGCCAACATTT 119 -529.6295861768235 0.003996003996003996 False 0.009273745122801727 N706 wildtype male 178 5.187385805840755 142.0 4.962845 42.95775109495255 0.5 0.4054651 0.35211268 178 142.0 +CCCGCTTGGCAG-5 CCCGCTTGGCAG 119 -532.7683507701806 0.002997002997002997 False 0.007228066051595463 N706 wildtype male 167 5.123963979403259 136.99998 4.9272537 41.240871319082075 2.5 1.252763 1.8248178 167 136.99998 +GACCAACGCAAC-5 GACCAACGCAAC 124 -549.6252481726892 0.001998001998001998 False 0.005015392770494811 N706 wildtype male 161 5.087596335232384 140.0 4.94876 40.714281276781215 2.5 1.252763 1.7857144 161 140.0 +GCAGTAAATACG-5 GCAGTAAATACG 134 -581.5214062683365 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 194 5.272999558563747 161.0 5.0875964 40.37267080745342 5.5 1.8718022 3.416149 194 161.0 +CTTTTTGCTTGG-5 CTTTTTGCTTGG 109 -528.289995996347 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 162 5.093750200806762 135.0 4.912655 42.22221744992659 3.0 1.3862944 2.2222223 162 135.0 +GCGTGCGAAGTG-5 GCGTGCGAAGTG 119 -535.077905117311 0.001998001998001998 False 0.005015392770494811 N706 wildtype male 171 5.147494476813453 137.0 4.9272537 39.416058394160586 1.5 0.91629076 1.0948905 171 137.0 +CAGAACCAGGAA-5 CAGAACCAGGAA 117 -532.3735808437816 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 169 5.135798437050262 138.0 4.934474 44.92752629652686 1.0 0.6931472 0.7246377 169 138.0 +CATCATGTCTAA-5 CATCATGTCTAA 103 -498.05763194391915 0.000999000999000999 True 0.0027926618835709743 N706 wildtype male 170 5.14166355650266 126.99999 4.8520303 42.51968503937008 0.5 0.4054651 0.39370084 170 126.99999 +ATAATAGGTTAG-5 ATAATAGGTTAG 102 -475.198501072865 0.001998001998001998 False 0.005015392770494811 N706 wildtype male 139 4.941642422609304 113.99999 4.744932 48.24562049271707 0.0 0.0 0.0 139 113.99999 +CCGGTCAATGCC-6 CCGGTCAATGCC 146 -438.93757507949425 nan 0.0014652014652014652 N707 knockout male 130 4.875197323201151 163.0 5.0998664 69.32515986391405 0.5 0.4054651 0.30674845 130 163.0 +GTCACCGTGCCC-6 GTCACCGTGCCC 138 -691.2351615321784 nan 0.0014652014652014652 N707 knockout male 172 5.153291594497779 155.0 5.049856 37.41935483870968 1.0 0.6931472 0.6451613 172 155.0 +CCGGTGTCCTCA-6 CCGGTGTCCTCA 140 -734.8496912108815 nan 0.0014652014652014652 N707 knockout male 197 5.288267030694535 162.0 5.09375 35.80247250803936 0.5 0.4054651 0.30864197 197 162.0 +GTCACGTCGACA-6 GTCACGTCGACA 131 -637.6488028106626 nan 0.0014652014652014652 N707 knockout male 197 5.288267030694535 152.0 5.030438 39.47367628525658 1.5 0.91629076 0.98684204 197 152.0 +CGTTTGGACAAT-6 CGTTTGGACAAT 118 -615.6123567604623 nan 0.0014652014652014652 N707 knockout male 165 5.111987788356544 132.0 4.890349 42.424247328353346 1.0 0.6931472 0.75757575 165 132.0 +TTGGCTTGCAAC-6 TTGGCTTGCAAC 137 -690.668759819393 nan 0.0014652014652014652 N707 knockout male 180 5.198497031265826 160.0 5.081404 38.12500363588368 1.5 0.91629076 0.93750006 180 160.0 +AATCGACATCTG-6 AATCGACATCTG 160 -797.8370828460304 nan 0.0 N707 knockout male 229 5.438079308923196 185.99998 5.2311087 33.333336067883565 9.5 2.3513753 5.1075273 229 185.99998 +GCCCGCAGTGAG-6 GCCCGCAGTGAG 125 -606.3144868119491 nan 0.0014652014652014652 N707 knockout male 179 5.19295685089021 145.0 4.983607 44.13792638971511 3.0 1.3862944 2.0689654 179 145.0 +CTGTTCTTCGGC-6 CTGTTCTTCGGC 119 -610.9276216571876 nan 0.0014652014652014652 N707 knockout male 166 5.117993812416755 136.0 4.919981 41.91176940825624 1.0 0.6931472 0.7352941 166 136.0 +AGCCACGAAATT-6 AGCCACGAAATT 109 -585.4848185283494 nan 0.0014652014652014652 N707 knockout male 147 4.997212273764115 125.99999 4.8441873 44.444441753300154 1.0 0.6931472 0.79365087 147 125.99999 +GTCCCCGGACGC-6 GTCCCCGGACGC 107 -556.0806547020735 nan 0.0014652014652014652 N707 knockout male 140 4.948759890378168 121.0 4.804021 45.45454258850712 2.0 1.0986123 1.6528925 140 121.0 +AGAGTATTACTT-6 AGAGTATTACTT 108 -586.9883372957092 nan 0.0014652014652014652 N707 knockout male 140 4.948759890378168 120.0 4.7957907 47.50000301996886 0.0 0.0 0.0 140 120.0 +CGTTGCTCGCAT-6 CGTTGCTCGCAT 103 -472.9291165290679 nan 0.0014652014652014652 N707 knockout male 155 5.049856007249537 122.0 4.8121843 54.09836403883412 0.5 0.4054651 0.40983605 155 122.0 +TGTCCTTAGCCT-6 TGTCCTTAGCCT 107 -542.4274070137658 nan 0.0014652014652014652 N707 knockout male 159 5.075173815233827 123.0 4.8202815 46.341454791286786 2.0 1.0986123 1.6260161 159 123.0 +GCCGAACACGAT-6 GCCGAACACGAT 109 -534.5015681761836 nan 0.0014652014652014652 N707 knockout male 134 4.90527477843843 122.0 4.8121843 49.99999687319916 1.5 0.91629076 1.2295082 134 122.0 diff --git a/topics/single-cell/tutorials/scrna-data-ingest/workflows/test-data/Inspect AnnData_ var.tabular b/topics/single-cell/tutorials/scrna-data-ingest/workflows/test-data/Inspect AnnData_ var.tabular new file mode 100644 index 00000000000000..533a282fdefa12 --- /dev/null +++ b/topics/single-cell/tutorials/scrna-data-ingest/workflows/test-data/Inspect AnnData_ var.tabular @@ -0,0 +1,35735 @@ +index ID Symbol NA. mito n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts log1p_total_counts n_cells n_counts +ENSMUSG00000051951 ENSMUSG00000051951 Xkr4 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102851 ENSMUSG00000102851 Gm18956 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103147 ENSMUSG00000103147 Gm7341 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025900 ENSMUSG00000025900 Rp1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102948 ENSMUSG00000102948 Gm6101 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025902 ENSMUSG00000025902 Sox17 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102269 ENSMUSG00000102269 Gm7357 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103003 ENSMUSG00000103003 Gm38076 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102735 ENSMUSG00000102735 Gm7369 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000098104 ENSMUSG00000098104 Gm6085 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102175 ENSMUSG00000102175 Gm6119 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103265 ENSMUSG00000103265 Gm2053 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103922 ENSMUSG00000103922 Gm6123 False False 2 0.00295858 0.002954212 99.40828402366864 1.0 0.6931472 2 1.0 +ENSMUSG00000033845 ENSMUSG00000033845 Mrpl15 False False 14 0.041420117 0.04058528 95.85798816568047 14.0 2.7080503 14 14.0 +ENSMUSG00000025903 ENSMUSG00000025903 Lypla1 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000104217 ENSMUSG00000104217 Gm37988 False False 1 0.0009861923 0.0009857063 99.70414201183432 0.333333 0.28768182 1 0.333333 +ENSMUSG00000033813 ENSMUSG00000033813 Tcea1 False False 12 0.026134122 0.025798462 96.44970414201184 8.833333 2.285778 12 8.833333 +ENSMUSG00000062588 ENSMUSG00000062588 Gm6104 False False 7 0.010355029 0.010301783 97.92899408284023 3.5 1.5040774 7 3.5 +ENSMUSG00000002459 ENSMUSG00000002459 Rgs20 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000091305 ENSMUSG00000091305 Gm17100 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000091665 ENSMUSG00000091665 Gm17101 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000033793 ENSMUSG00000033793 Atp6v1h False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000104352 ENSMUSG00000104352 Gm7182 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025905 ENSMUSG00000025905 Oprk1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103936 ENSMUSG00000103936 Gm36965 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000033774 ENSMUSG00000033774 Npbwr1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103090 ENSMUSG00000103090 Gm19214 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025907 ENSMUSG00000025907 Rb1cc1 False False 3 0.00887574 0.008836582 99.11242603550295 3.0 1.3862944 3 3.0 +ENSMUSG00000087247 ENSMUSG00000087247 Alkal1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103355 ENSMUSG00000103355 Gm2147 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102706 ENSMUSG00000102706 Gm7417 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103845 ENSMUSG00000103845 Gm19026 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000033740 ENSMUSG00000033740 St18 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000104385 ENSMUSG00000104385 Gm7449 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102135 ENSMUSG00000102135 Gm37108 False False 3 0.001775148 0.0017735743 99.11242603550295 0.6 0.47000366 3 0.6 +ENSMUSG00000103282 ENSMUSG00000103282 Gm37275 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102534 ENSMUSG00000102534 Gm37225 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102213 ENSMUSG00000102213 Gm37489 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103629 ENSMUSG00000103629 Gm5694 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000051285 ENSMUSG00000051285 Pcmtd1 False False 8 0.02366864 0.02339288 97.63313609467455 8.0 2.1972246 8 8.0 +ENSMUSG00000098201 ENSMUSG00000098201 Gm26983 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000048538 ENSMUSG00000048538 Gm9826 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103709 ENSMUSG00000103709 Nras-ps2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102768 ENSMUSG00000102768 Gm19002 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103498 ENSMUSG00000103498 Gm18984 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102320 ENSMUSG00000102320 Gm37791 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000104226 ENSMUSG00000104226 Gm7470 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103903 ENSMUSG00000103903 Rps2-ps2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103557 ENSMUSG00000103557 Gm38216 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025909 ENSMUSG00000025909 Sntg1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000086235 ENSMUSG00000086235 Gm16284 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000084353 ENSMUSG00000084353 Gm15452 False False 5 0.0049309614 0.004918844 98.5207100591716 1.666665 0.98082864 5 1.666665 +ENSMUSG00000103933 ENSMUSG00000103933 Gm36964 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000086195 ENSMUSG00000086195 Gm6152 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000104504 ENSMUSG00000104504 Gm7445 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000066693 ENSMUSG00000066693 Gm7493 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103819 ENSMUSG00000103819 Gm38008 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000101571 ENSMUSG00000101571 Gm6187 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000104428 ENSMUSG00000104428 Gm18299 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000102272 ENSMUSG00000102272 Gm7512 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000103411 ENSMUSG00000103411 Gm18300 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000061024 ENSMUSG00000061024 Rrs1 False False 25 0.07988165 0.07685146 92.60355029585799 27.0 3.3322046 25 27.0 +ENSMUSG00000025911 ENSMUSG00000025911 Adhfe1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000081441 ENSMUSG00000081441 Gm6161 False False 44 0.014808343 0.01469977 86.98224852071006 5.00522 1.7926291 44 5.00522 +ENSMUSG00000067879 ENSMUSG00000067879 Vxn False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025912 ENSMUSG00000025912 Mybl1 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000045210 ENSMUSG00000045210 Vcpip1 False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000025915 ENSMUSG00000025915 Sgk3 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000046334 ENSMUSG00000046334 Gm6195 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000046101 ENSMUSG00000046101 Mcmdc2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000099032 ENSMUSG00000099032 Tcf24 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025916 ENSMUSG00000025916 Ppp1r42 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025917 ENSMUSG00000025917 Cops5 False False 11 0.03254438 0.032026026 96.7455621301775 11.0 2.4849067 11 11.0 +ENSMUSG00000056763 ENSMUSG00000056763 Cspp1 False False 5 0.0147929 0.014684552 98.5207100591716 5.0 1.7917595 5 5.0 +ENSMUSG00000067851 ENSMUSG00000067851 Arfgef1 False False 4 0.01183432 0.011764842 98.81656804733728 4.0 1.609438 4 4.0 +ENSMUSG00000042501 ENSMUSG00000042501 Cpa6 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000083422 ENSMUSG00000083422 Gm15604 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000081417 ENSMUSG00000081417 Gm15603 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000100212 ENSMUSG00000100212 Gm5522 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000100648 ENSMUSG00000100648 Gm28659 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000048960 ENSMUSG00000048960 Prex2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000101827 ENSMUSG00000101827 Gm28686 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000057715 ENSMUSG00000057715 A830018L16Rik False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000099845 ENSMUSG00000099845 Gm29273 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000101610 ENSMUSG00000101610 Gm7560 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000097628 ENSMUSG00000097628 Gm2383 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000098163 ENSMUSG00000098163 Gm6216 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000016918 ENSMUSG00000016918 Sulf1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025938 ENSMUSG00000025938 Slco5a1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000069620 ENSMUSG00000069620 Gm5250 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000042414 ENSMUSG00000042414 Prdm14 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000005886 ENSMUSG00000005886 Ncoa2 False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000101171 ENSMUSG00000101171 Gm7593 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025935 ENSMUSG00000025935 Tram1 False False 9 0.026627218 0.026278883 97.33727810650888 9.0 2.3025851 9 9.0 +ENSMUSG00000025937 ENSMUSG00000025937 Lactb2 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000067813 ENSMUSG00000067813 Xkr9 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000097711 ENSMUSG00000097711 Gm5523 False False 1 0.00011379291 0.00011378643 99.70414201183432 0.038462 0.037740774 1 0.038462 +ENSMUSG00000025932 ENSMUSG00000025932 Eya1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025930 ENSMUSG00000025930 Msc False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000081201 ENSMUSG00000081201 Smt3h2-ps4 False False 2 0.0015779083 0.0015766647 99.40828402366864 0.533333 0.4274438 2 0.533333 +ENSMUSG00000082193 ENSMUSG00000082193 Rpl5-ps1 False False 15 0.014053243 0.0139554115 95.56213017751479 4.749996 1.7491992 15 4.749996 +ENSMUSG00000032769 ENSMUSG00000032769 Trpa1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000073737 ENSMUSG00000073737 Gm10566 False False 5 0.0005889527 0.00058877934 98.5207100591716 0.199066 0.18154292 5 0.199066 +ENSMUSG00000092083 ENSMUSG00000092083 Kcnb2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000101652 ENSMUSG00000101652 Gm6060 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000100814 ENSMUSG00000100814 Gm28669 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025925 ENSMUSG00000025925 Terf1 False False 12 0.03550296 0.034887258 96.44970414201184 12.0 2.5649493 12 12.0 +ENSMUSG00000032719 ENSMUSG00000032719 Sbspon False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000100652 ENSMUSG00000100652 Gm7634 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000067795 ENSMUSG00000067795 4930444P10Rik False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000043716 ENSMUSG00000043716 Rpl7 False False 76 0.20266272 0.18453804 77.51479289940828 68.5 4.241327 76 68.5 +ENSMUSG00000025921 ENSMUSG00000025921 Rdh10 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025920 ENSMUSG00000025920 Stau2 False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000025939 ENSMUSG00000025939 Ube2w False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000079658 ENSMUSG00000079658 Eloc False False 17 0.056213018 0.054689884 94.97041420118343 19.0 2.9957323 17 19.0 +ENSMUSG00000100959 ENSMUSG00000100959 Gm7654 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000025940 ENSMUSG00000025940 Tmem70 False False 8 0.02366864 0.02339288 97.63313609467455 8.0 2.1972246 8 8.0 +ENSMUSG00000025779 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0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000095486 ENSMUSG00000095486 Vmn2r22 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000091620 ENSMUSG00000091620 Vmn2r23 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000072780 ENSMUSG00000072780 Vmn2r24 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000094672 ENSMUSG00000094672 Vmn2r25 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000108240 ENSMUSG00000108240 Gm5317 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000108198 ENSMUSG00000108198 Gm44823 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000093385 ENSMUSG00000093385 A330044P14Rik False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000096630 ENSMUSG00000096630 Vmn2r26 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000095544 ENSMUSG00000095544 Vmn2r-ps33 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000107718 ENSMUSG00000107718 Vmn2r-ps34 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000072778 ENSMUSG00000072778 Vmn2r27 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000107921 ENSMUSG00000107921 Gm6423 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000107600 ENSMUSG00000107600 Gm6308 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000008845 ENSMUSG00000008845 Cd163 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000005069 ENSMUSG00000005069 Pex5 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000008153 ENSMUSG00000008153 Clstn3 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000118414 ENSMUSG00000118414 Gm50463 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000038527 ENSMUSG00000038527 C1rl False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000055172 ENSMUSG00000055172 C1ra False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000038521 ENSMUSG00000038521 C1s1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000107568 ENSMUSG00000107568 Gm18840 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000098470 ENSMUSG00000098470 C1rb False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000079343 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ENSMUSG00000030125 Lrrc23 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000038451 ENSMUSG00000038451 Spsb2 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000023456 ENSMUSG00000023456 Tpi1 False False 13 0.03846154 0.03774033 96.15384615384616 13.0 2.6390574 13 13.0 +ENSMUSG00000038429 ENSMUSG00000038429 Usp5 False False 5 0.01775148 0.017595762 98.5207100591716 6.0 1.9459102 5 6.0 +ENSMUSG00000023505 ENSMUSG00000023505 Cdca3 False False 14 0.04733728 0.046251018 95.85798816568047 16.0 2.8332133 14 16.0 +ENSMUSG00000023439 ENSMUSG00000023439 Gnb3 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000023191 ENSMUSG00000023191 P3h3 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000038390 ENSMUSG00000038390 Gpr162 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000023274 ENSMUSG00000023274 Cd4 False False 4 0.01775148 0.017595762 98.81656804733728 6.0 1.9459102 4 6.0 +ENSMUSG00000030124 ENSMUSG00000030124 Lag3 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000030122 ENSMUSG00000030122 Ptms False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000030120 ENSMUSG00000030120 Mlf2 False False 6 0.01775148 0.017595762 98.22485207100591 6.0 1.9459102 6 6.0 +ENSMUSG00000107578 ENSMUSG00000107578 Gm32536 False False 1 0.000295858 0.00029581424 99.70414201183432 0.1 0.09531018 1 0.1 +ENSMUSG00000030127 ENSMUSG00000030127 Cops7a False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000030329 ENSMUSG00000030329 Pianp False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000038346 ENSMUSG00000038346 Zfp384 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000030330 ENSMUSG00000030330 Ing4 False False 7 0.01612426 0.015995644 97.92899408284023 5.45 1.8640801 7 5.45 +ENSMUSG00000072770 ENSMUSG00000072770 Acrbp False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000067714 ENSMUSG00000067714 Lpar5 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000063870 ENSMUSG00000063870 Chd4 False False 35 0.112426035 0.10654325 89.64497041420118 38.0 3.6635616 35 38.0 +ENSMUSG00000038279 ENSMUSG00000038279 Nop2 False False 5 0.0147929 0.014684552 98.5207100591716 5.0 1.7917595 5 5.0 +ENSMUSG00000038271 ENSMUSG00000038271 Iffo1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000057666 ENSMUSG00000057666 Gapdh False False 43 0.05696689 0.05540338 87.27810650887574 19.254808 3.0083923 43 19.254808 +ENSMUSG00000038252 ENSMUSG00000038252 Ncapd2 False False 5 0.0147929 0.014684552 98.5207100591716 5.0 1.7917595 5 5.0 +ENSMUSG00000030335 ENSMUSG00000030335 Mrpl51 False False 7 0.020710059 0.020498522 97.92899408284023 7.0 2.0794415 7 7.0 +ENSMUSG00000107995 ENSMUSG00000107995 Gm44487 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000030337 ENSMUSG00000030337 Vamp1 False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000038213 ENSMUSG00000038213 Tapbpl False False 2 0.0013313609 0.0013304754 99.40828402366864 0.45 0.37156355 2 0.45 +ENSMUSG00000030336 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1.5 0.91629076 2 1.5 +ENSMUSG00000022905 ENSMUSG00000022905 Kpna1 False False 4 0.01183432 0.011764842 98.81656804733728 4.0 1.609438 4 4.0 +ENSMUSG00000034379 ENSMUSG00000034379 Wdr5b False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000003955 ENSMUSG00000003955 Fam162a False False 10 0.03550296 0.034887258 97.0414201183432 12.0 2.5649493 10 12.0 +ENSMUSG00000075229 ENSMUSG00000075229 Ccdc58 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000034362 ENSMUSG00000034362 Csta1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000060956 ENSMUSG00000060956 Gm10092 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000059657 ENSMUSG00000059657 Stfa2l1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000079597 ENSMUSG00000079597 Cstdc4 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000116872 ENSMUSG00000116872 Gm6815 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000078097 ENSMUSG00000078097 Gm10913 False 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100.0 0.0 0.0 0 0.0 +ENSMUSG00000022821 ENSMUSG00000022821 Hgd False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000022820 ENSMUSG00000022820 Ndufb4 False False 9 0.013313609 0.013225762 97.33727810650888 4.5 1.7047482 9 4.5 +ENSMUSG00000116529 ENSMUSG00000116529 Gm4600 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000022816 ENSMUSG00000022816 Fstl1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000108763 ENSMUSG00000108763 Gm36028 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000034158 ENSMUSG00000034158 Lrrc58 False False 6 0.007889542 0.007858582 98.22485207100591 2.666665 1.2992826 6 2.666665 +ENSMUSG00000083405 ENSMUSG00000083405 Gm15725 False False 5 0.0049309614 0.004918844 98.5207100591716 1.666665 0.98082864 5 1.666665 +ENSMUSG00000046961 ENSMUSG00000046961 Gpr156 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000022812 ENSMUSG00000022812 Gsk3b False False 10 0.0295858 0.029156584 97.0414201183432 10.0 2.3978953 10 10.0 +ENSMUSG00000022809 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Sidt1 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000043065 ENSMUSG00000043065 Spice1 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000071550 ENSMUSG00000071550 Cfap44 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000022687 ENSMUSG00000022687 Boc False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000036208 ENSMUSG00000036208 Nepro False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000022668 ENSMUSG00000022668 Gtpbp8 False False 2 0.00591716 0.005899722 99.40828402366864 2.0 1.0986123 2 2.0 +ENSMUSG00000022667 ENSMUSG00000022667 Cd200r1 False False 1 0.00295858 0.002954212 99.70414201183432 1.0 0.6931472 1 1.0 +ENSMUSG00000116827 ENSMUSG00000116827 Gm5063 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000116605 ENSMUSG00000116605 Gm19142 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000062082 ENSMUSG00000062082 Cd200r4 False False 0 0.0 0.0 100.0 0.0 0.0 0 0.0 +ENSMUSG00000116911 ENSMUSG00000116911 Gm5964 False False 0 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0.0 0.0 0 0.0 diff --git a/topics/single-cell/tutorials/scrna-ncbi-anndata/tutorial.md b/topics/single-cell/tutorials/scrna-ncbi-anndata/tutorial.md index bc06bf5ca77475..665697dfdc31ec 100644 --- a/topics/single-cell/tutorials/scrna-ncbi-anndata/tutorial.md +++ b/topics/single-cell/tutorials/scrna-ncbi-anndata/tutorial.md @@ -33,6 +33,8 @@ contributions: - hexylena testing: - pavanvidem + funding: + - elixir-fair-data follow_up_training: -