Update Links/Vignette

DESCRIPTION

@@ -62,5 +62,5 @@ Language: en-US
 LinkingTo: 
     Rcpp
 URL: https://www.borch.dev/uploads/scRepertoire/
-BugReports: https://github.com/ncborcherding/scRepertoire/issues
+BugReports: https://github.com/BorchLab/scRepertoire/issues
 Roxygen: list(markdown = TRUE)

R/combineContigs.R

@@ -251,7 +251,7 @@ combineBCR <- function(input.data,
         (function(x) {
             if (!is.null(samples)) {
                 .modifyBarcodes(x, samples, ID)
-            } else { # https://github.com/ncborcherding/scRepertoire/pull/450
+            } else { # https://github.com/BorchLab/scRepertoire/pull/450
                 x
             }
         }) %>%

README.md

@@ -14,7 +14,7 @@
 Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, the scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell clonal formats and interaction with popular R-based single-cell data pipelines. 
 
 ### Applying Deep Learning to VDJ data
-scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for deep-learning-based autoencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. If you are interested in making your own deep-learning models with immune receptors, please see [immApex](https://github.com/ncborcherding/immApex).
+scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/BorchLab/Trex) for deep-learning-based autoencoding of the T cell receptor and [Ibex](https://github.com/BorchLab/Ibex) for the B cell receptor. If you are interested in making your own deep-learning models with immune receptors, please see [immApex](https://github.com/BorchLab/immApex).
 
 ## Working with scRepertoire
 
@@ -25,7 +25,7 @@ scRepertoire has a comprehensive [website](https://www.borch.dev/uploads/screper
 ### Installation of Master Branch
 
 ```R
-devtools::install_github("ncborcherding/scRepertoire")
+devtools::install_github("BorchLab/scRepertoire")
 ```
 
 ### Installing from Bioconductor
@@ -42,7 +42,7 @@ BiocManager::install("scRepertoire")
 If you are looking for version 1 of scRepertoire - there is a static version available below:
 
 ```R
-devtools::install_github("ncborcherding/scRepertoire@v1")
+devtools::install_github("BorchLab/scRepertoire@v1")
 ```
 
 ## Getting Data
@@ -51,15 +51,15 @@ Unfortunately, Github limits the size of individual files. In order to access th
 
 ## Bug Reports/New Features
 
-#### If you run into any issues or bugs please submit a [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) with details of the issue.
+#### If you run into any issues or bugs please submit a [GitHub issue](https://github.com/BorchLab/scRepertoire/issues) with details of the issue.
 
 - If possible please include a [reproducible example](https://reprex.tidyverse.org/). 
 Alternatively, an example with the internal **scRep_example** and **contig_list** would 
 be extremely helpful.
 
-#### Any requests for new features or enhancements can also be submitted as [GitHub issues](https://github.com/ncborcherding/scRepertoire/issues).
+#### Any requests for new features or enhancements can also be submitted as [GitHub issues](https://github.com/BorchLab/scRepertoire/issues).
 
-#### [Pull Requests](https://github.com/ncborcherding/scRepertoire/pulls) are welcome for bug fixes, new features, or enhancements.
+#### [Pull Requests](https://github.com/BorchLab/scRepertoire/pulls) are welcome for bug fixes, new features, or enhancements.
 
 ## Please Cite
 scRepertoire 2 preprint is available at: [https://www.biorxiv.org/content/10.1101/2024.12.31.630854v1](https://www.biorxiv.org/content/10.1101/2024.12.31.630854v1)

inst/pkgdown.yml

@@ -1,4 +1,4 @@
-pandoc: '3.2'
+pandoc: 3.1.11
 pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
@@ -20,7 +20,7 @@ articles:
   articles/SC_Visualizations: SC_Visualizations.html
   articles/Trex: Trex.html
   vignette: vignette.html
-last_built: 2025-01-26T21:04Z
+last_built: 2025-01-29T13:44Z
 urls:
   reference: https://www.borch.dev/uploads/scRepertoire/reference
   article: https://www.borch.dev/uploads/scRepertoire/articles

vignettes/articles/FAQ.Rmd

@@ -125,5 +125,4 @@ We are working on submitting the scRepertoire as a peer review article,
 
 ### Bug Reports/New Features
 
-Submit a [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) - if possible please include a [reproducible example](https://reprex.tidyverse.org/). Alternatively, an example with the internal **scRep_example** and **contig_list** would 
-be extremely helpful.
+Submit a [GitHub issue](https://github.com/BorchLab/scRepertoire/issues) - if possible please include a [reproducible example](https://reprex.tidyverse.org/). Alternatively, an example with the internal **scRep_example** and **contig_list** would be extremely helpful.

vignettes/articles/Ibex.Rmd

@@ -49,12 +49,12 @@ suppressMessages(library(dplyr))
 
 The idea behind Ibex is to combine BCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries.  This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using BCR and RNA. Ibex was designed to make a customizable approach to this combined approach using R.
 
-More information is available at the [Ibex GitHub Repo](https://github.com/ncborcherding/Ibex).
+More information is available at the [Ibex GitHub Repo](https://github.com/BorchLab/Ibex).
 
 ## Installation
 
 ```{r, eval = FALSE, tidy = FALSE}
-devtools::install_github("ncborcherding/Ibex")
+devtools::install_github("BorchLab/Ibex")
 ```
 
 ## The Data Set
@@ -133,7 +133,7 @@ SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Ibex_FullExample.rd
 
 ## Ibex.matrix Function
 
-Ibex has 2 major functions - the first being ```Ibex.matrix()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runIbex()``` below, ```Ibex.matrix()``` does not filter the input for only B cells with attached BCR data. In addition, ```Ibex.matrix()``` is compatible with the list output from the ```combineBCR()``` function from the [scRepertoire](https://github.com/ncborcherding/scRepertoire) R package, while ```runIbex()``` must be performed on a single-cell object.
+Ibex has 2 major functions - the first being ```Ibex.matrix()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runIbex()``` below, ```Ibex.matrix()``` does not filter the input for only B cells with attached BCR data. In addition, ```Ibex.matrix()``` is compatible with the list output from the ```combineBCR()``` function from the [scRepertoire](https://github.com/BorchLab/scRepertoire) R package, while ```runIbex()``` must be performed on a single-cell object.
 
 **chains**  
 

vignettes/articles/Installation.Rmd

@@ -48,14 +48,14 @@ A full copy of the changes in each version can be found in the [NEWS/ChangeLog](
 scRepertoire is an active project, that receives a number of changes based on user feedback, the most up-to-date versions are available via the GitHub repositories. These versions can be installed using ```devtools::install_github()``` or ```remotes::install_github()```. The **dev** version is the active development branch and might be less stable than the main branch or bioconductor versions. 
 
 ```
-devtools::install_github(repo = "ncborcherding/scRepertoire")
+devtools::install_github(repo = "BorchLab/scRepertoire")
 
-remotes::install_github(repo = "ncborcherding/scRepertoire", ref = "dev")
+remotes::install_github(repo = "BorchLab/scRepertoire", ref = "dev")
 ```
 
 ## Bioconductor
 
-The current version of scRepertoire is also available in the development version of Bioconductor. 
+The current version of scRepertoire is also available in Bioconductor. 
 
 ```
 BiocManager::install("scRepertoire")

vignettes/articles/Repertoire_Summary.Rmd

@@ -192,7 +192,7 @@ ggplot(df, aes(x = PC1, y = PC2)) +
 Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for further dimensional reduction.
 
 ```{r tidy = FALSE}
-percentVJ(combined.TCR, 
+percentVJ(combined.TCR[1:2], #First Two Samples
           chain = "TRB")
 
 df.genes <- percentVJ(combined.TCR, 

vignettes/articles/Running_Escape.Rmd

@@ -67,7 +67,7 @@ colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)
 
 *escape* is a R package designed for **E**asy **s**ingle-**c**ell **a**nalysis **p**latform for **e**nrichment. As the tortuous acronym implies, *escape* was designed as a user-friendly package for gene set enrichment analysis that leverages the heterogeneity of single-cell data. 
 
-More information is available at the [GitHub Repo](https://github.com/ncborcherding/escape).
+More information is available at the [GitHub Repo](https://github.com/BorchLab/escape).
 
 ## Citation 
 If using *escape*, please cite the [article](https://www.nature.com/articles/s42003-020-01625-6): Borcherding, N., Vishwakarma, A., Voigt, A.P. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun Biol 4, 122 (2021). https://doi.org/10.1038/s42003-020-01625-6. 
@@ -77,14 +77,13 @@ If using *escape*, please cite the [article](https://www.nature.com/articles/s42
 *escape* is available via github using:
 
 ```{r eval=FALSE, tidy=FALSE}
-devtools::install_github("ncborcherding/escape")
-remotes::install_github("ncborcherding/escape")
+devtools::install_github("BorchLab/escape")
+remotes::install_github("BorchLab/escape")
 ```
 
-For now, the newest version of *escape* is available in the Bioconductor dev version (3.19).
+Or via Bioconductor with:
 
 ```{r eval = FALSE, tidy=FALSE}
-BiocManager::install(version='devel')
 BiocManager::install("escape")
 ```
 
@@ -294,6 +293,7 @@ heatmapEnrichment(scRep_example,
                   gene.set.use = rownames(scRep_example@assays$escape.ssGSEA@data)[1:12],
                   assay = "escape.ssGSEA")
 ```
+
 Most of the visualizations in *escape* have a defined set of parameters.
 
 **group.by**
@@ -372,6 +372,7 @@ geyserEnrichment(scRep_example,
                  gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE", 
                  facet.by = "Type")
 ```
+
 Lastly, we can select the way the color is applied to the plot using the **color.by** parameter. Here we can set it to the gene set of interest *"HALLMARK-INTERFERON-GAMMA-RESPONSE"*.
 
 ```{r tidy=FALSE}

vignettes/articles/Trex.Rmd

@@ -43,19 +43,18 @@ suppressMessages(library(ggplot2))
 suppressMessages(library(viridis))
 suppressMessages(library(dplyr))
 suppressMessages(library(reticulate))
-#use_condaenv(condaenv = "r-reticulate", required = TRUE)
 ```
 
 # Getting Started 
 
 The idea behind Trex is to combine TCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using TCR and RNA with python. Trex was designed to make a customizable approach to this combined approach using R.
 
-More information is available at the [Trex GitHub Repo](https://github.com/ncborcherding/Trex).
+More information is available at the [Trex GitHub Repo](https://github.com/BorchLab/Trex).
 
 ## Installation
 
 ```{r, eval = FALSE}
-devtools::install_github("ncborcherding/Trex")
+devtools::install_github("BorchLab/Trex")
 ```
 
 ## The Data Set
@@ -133,7 +132,7 @@ SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Trex_FullExample.rd
 
 ## maTrex Function
 
-Trex has 2 major functions - the first being ```maTrex()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runTrex()``` below, ```maTrex()``` does not filter the input for only T cells with attached TCR data. In addition, ```maTrex()``` is compatible with the list output from the ```combineTCR()``` function from the [scRepertoire](https://github.com/ncborcherding/scRepertoire) R package, while ```runTrex()``` must be performed on a single-cell object.
+Trex has 2 major functions - the first being ```maTrex()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runTrex()``` below, ```maTrex()``` does not filter the input for only T cells with attached TCR data. In addition, ```maTrex()``` is compatible with the list output from the ```combineTCR()``` function from the [scRepertoire](https://github.com/BorchLab/scRepertoire) R package, while ```runTrex()``` must be performed on a single-cell object.
 
 **chains** 
 

vignettes/articles/immApex.Rmd

@@ -47,9 +47,9 @@ set.seed(42)
 
 # Getting Started 
 
-**immApex** is meant to serve as an API for deep-learning models based on immune receptor sequencing. These functions extract or generate amino acid or nucleotide sequences and prepare them for deep learning tasks through [Keras](https://tensorflow.rstudio.com/guides/keras/basics). **immApex** is the underlying structure for the BCR models in [Ibex](https://github.com/ncborcherding/Ibex) and TCR models in [Trex](https://github.com/ncborcherding/Trex). It should be noted that the tools here are created for immune receptor sequences; they will work more generally for nucleotide or amino acid sequences. The package itself supports AIRR, Adaptive, and 10x formats and interacts with the **scRepertoire** R package.
+**immApex** is meant to serve as an API for deep-learning models based on immune receptor sequencing. These functions extract or generate amino acid or nucleotide sequences and prepare them for deep learning tasks through [Keras](https://tensorflow.rstudio.com/guides/keras/basics). **immApex** is the underlying structure for the BCR models in [Ibex](https://github.com/BorchLab/Ibex) and TCR models in [Trex](https://github.com/BorchLab/Trex). It should be noted that the tools here are created for immune receptor sequences; they will work more generally for nucleotide or amino acid sequences. The package itself supports AIRR, Adaptive, and 10x formats and interacts with the **scRepertoire** R package.
 
-More information is available at the [immApex GitHub Repo](https://github.com/ncborcherding/immApex).
+More information is available at the [immApex GitHub Repo](https://github.com/BorchLab/immApex).
 
 ## Loading Libraries
 
@@ -403,7 +403,7 @@ head(OHE.sequences)
 
 ## Autoencoder
 
-For the vignette - we will use an autoencoder for sequence embedding. The code below is based on the [Trex](https://github.com/ncborcherding/Trex) R package. The overall structure of the autoencoder is the same. However, some of the parameters are modified for the sake of the vignette. We will use the **sequence.matrix** we generated above from the ```onehotEncoder()```.
+For the vignette - we will use an autoencoder for sequence embedding. The code below is based on the [Trex](https://github.com/BorchLab/Trex) R package. The overall structure of the autoencoder is the same. However, some of the parameters are modified for the sake of the vignette. We will use the **sequence.matrix** we generated above from the ```onehotEncoder()```.
 
 The steps to train the model include: 
 
@@ -554,7 +554,7 @@ Here, we can achieve a validation accuracy of 98.25%, which is impressive. But t
 ***
 # Conclusion
 
-This has been a general overview of the capabilities of **immApex** for processing immune receptor sequences and making deep learning models. If you have any questions, comments, or suggestions, feel free to visit the [GitHub repository](https://github.com/ncborcherding/immApex).
+This has been a general overview of the capabilities of **immApex** for processing immune receptor sequences and making deep learning models. If you have any questions, comments, or suggestions, feel free to visit the [GitHub repository](https://github.com/BorchLab/immApex).
 
 ## Session Info
 

vignettes/vignette.Rmd

@@ -1205,7 +1205,7 @@ legend("topleft", legend = levels(color.legend), pch = 16, col = unique(col_samp
 
 # Conclusion  
 
-This has been a general overview of the capabilities of scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a single-cell object. If you have any questions, comments, or suggestions, please visit the [GitHub repository](https://github.com/ncborcherding/scRepertoire).
+This has been a general overview of the capabilities of scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a single-cell object. If you have any questions, comments, or suggestions, please visit the [GitHub repository](https://github.com/BorchLab/scRepertoire).
 
 ### Session Info