diff --git a/learn/index.html b/learn/index.html index 4e8f227..0f86565 100644 --- a/learn/index.html +++ b/learn/index.html @@ -6,21 +6,19 @@ For more tutorials as well as API documentation and user guides, see the sites of individual packages.

You can also find recordings of past talks and workshops on our YouTube channel.

Tutorials

Data structures

These tutorials teach you how to work with scverse data structures. If you are new to Python and/or scverse, we recommend you read the "getting started" and "axes" tutorials first.

Axes in AnnData and MuData

In this tutorial we showcase operations on independent AnnData objects -(scRNAseq matrix + metadata), demonstrating how varied processing +(scRNAseq matrix + metadata), demonstrating how various processing workflows can be stored in one MuData object.

Working with scverse objects in backed mode

In this tutorial, we demonstrate working with scverse data objects -(AnnData and MuData are saved as .h5ad and .h5mu files) without loading full datasets.

Getting started with AnnData

This tutorial helps you to explore the structure and content of single-cell +without loading full datasets. (AnnData and MuData are saved as .h5ad and .h5mu files)

Getting started with AnnData

This tutorial helps you to explore the structure and content of single-cell data analysis results in a *.h5ad file using AnnData, Scanpy, and Python.

Concatenation

In this notebook we showcase how to perform concatenation, meaning to keep all sub elements of each object, and stack these elements in an ordered way.

Concatenation of multimodal data

This tutorial shows how you can concatenate 2 MuData objects that may represent complementary slices of the same dataset or 2 modalities into one AnnData.

scRNA-seq

The following tutorials show show to analyze single-cell gene expression data.

Compositional analysis

This tutorial introduces compositional analysis at cell identity cluster level, based on known cell types or states affected by -perturbations using Haber dataset.

Batch-effect removal with scvi-tools

In this tutorial, we demonstrate how to use scvi-tools to fit a model to single-cell count data, +perturbations.

Batch-effect removal with scvi-tools

In this tutorial, we demonstrate how to use scvi-tools to fit a model to single-cell count data, correct batch effects, and perform differential gene expression analysis.

Pseudo-bulk differential expression and functional analysis

This notebook showcases decoupler for pathway and TF enrichment on ~5k -Blood myeloid cells from healthy and COVID-19 infected patients in the -Single Cell Expression Atlas.

Pseudotemporal ordering

This tutorial show how a pseudotime can be constructed and compares different pseudotimes.

Perturbation modeling

This tutorial covers 3 approaches using single-cell perturbation data: -Augur - identify affected cell types -scGen - predict transcriptional response -Mixscape - quantify CRISPR sensitivity.

Preprocessing, clustering and cell-type annotation

This fundamental tutorial covers common analysis steps: quality control, +Blood myeloid cells from healthy and COVID-19 infected patients.

Pseudotemporal ordering

This tutorial show how a pseudotime can be constructed and compares different pseudotimes.

Perturbation modeling

This tutorial covers 3 approaches using single-cell perturbation data: +Augur (identify affected cell types), scGen (predict transcriptional response), +Mixscape (quantify CRISPR sensitivity).

Preprocessing, clustering and cell-type annotation

This fundamental tutorial covers common analysis steps: quality control, normalization, feature selection, dimensionality reduction, clustering, and cell-type annotation.

RNA velocity

This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data using scVelo.

Spatial

Analyze spatial data generated with different technologies

Spatial analysis with squidpy

This tutorial demonstrate how to use squidpy to analyse transcriptomics