Updated README files

components/dictionary.txt

@@ -402,6 +402,7 @@ SCE
 SCE's
 ScPCA
 scran
+ScPCA
 scRNA
 Sebire
 Sergushichev

intro-to-R-tidyverse/README.md

@@ -1,6 +1,6 @@
 # Intro to R and Tidyverse Training Module
 
-This CCDL-designed module introduces the R programming language and some [tidyverse](https://www.tidyverse.org/) packages.
+This Childhood Cancer Data Lab-designed module introduces the R programming language and some [tidyverse](https://www.tidyverse.org/) packages.
 The tidyverse is a collection of packages designed for data science.
 It goes through the basics of R _(including data types, data structures)_, ggplot2 visualizations, and other tidyverse data manipulation packages _(including dplyr pipes, common tidyverse functions)_, thus providing the tools needed to move onto the subsequent training modules.
 
@@ -9,9 +9,12 @@ The notebooks that comprise this module are (estimated time to complete notebook
 * [Introduction to Base R](https://alexslemonade.github.io/training-modules/intro-to-R-tidyverse/01-intro_to_base_R.nb.html) (1 hr 40 minutes)
 * [Introduction to ggplot2](https://alexslemonade.github.io/training-modules/intro-to-R-tidyverse/02-intro_to_ggplot2.nb.html) (45 minutes)
 * [Introduction to tidyverse](https://alexslemonade.github.io/training-modules/intro-to-R-tidyverse/03-intro_to_tidyverse.nb.html) (1 hr 10 minutes)
+
+Additional exercise notebooks:
+
 * [Additional exercises for introduction to base R](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_01-intro_to_base_R.Rmd)
-* [Additional exercises for introduction to R](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_02-intro_to_R.Rmd)  
-* [Additional exercises for introduction to tidyverse part 1](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_03a-intro_to_tidyverse.Rmd)  
-* [Additional exercises for introduction to tidyverse part 2](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_03b-intro_to_tidyverse.Rmd)  
+* [Additional exercises for introduction to R](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_02-intro_to_R.Rmd)
+* [Additional exercises for introduction to tidyverse part 1](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_03a-intro_to_tidyverse.Rmd)
+* [Additional exercises for introduction to tidyverse part 2](https://github.com/AlexsLemonade/training-modules/blob/master/intro-to-R-tidyverse/exercise_03b-intro_to_tidyverse.Rmd)
 
 _Total estimated time to complete instruction notebooks: 3 hours 35 minutes_

pathway-analysis/README.md

@@ -9,9 +9,11 @@ The notebooks that comprise this module are (estimated time to complete notebook
 * [Over-representation Analysis](https://alexslemonade.github.io/training-modules/pathway-analysis/01-overrepresentation_analysis.nb.html) (45 minutes)
 * [Gene Set Enrichment Analysis](https://alexslemonade.github.io/training-modules/pathway-analysis/02-gene_set_enrichment_analysis.nb.html) (30 minutes)
 * [Gene Set Variation Analysis](https://alexslemonade.github.io/training-modules/pathway-analysis/03-gene_set_variation_analysis.nb.html) (30 minutes)
-* Additional exercises
-  * [Over-representation Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_01-ora.Rmd)
-  * [Gene Set Enrichment Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_02-gsea.Rmd)
-  * [Gene Set Variation Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_03-gsva.Rmd)
+
+Additional exercise notebooks:
+
+* [Over-representation Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_01-ora.Rmd)
+* [Gene Set Enrichment Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_02-gsea.Rmd)
+* [Gene Set Variation Analysis](https://github.com/AlexsLemonade/training-modules/blob/master/pathway-analysis/exercise_03-gsva.Rmd)
 
 _Total estimated time to complete instruction notebooks: 1 hour 45 minutes_

scRNA-seq-advanced/README.md

@@ -2,25 +2,24 @@
 
 This Childhood Cancer Data Lab-designed module covers more advanced topics in the analysis of single-cell RNA-seq data.
 
-The module builds on material in the [scRNA-seq module](https://github.com/AlexsLemonade/training-modules/tree/master/scRNA-seq), and analyses are designed to be performed within a [Docker container](https://github.com/AlexsLemonade/training-modules/tree/master/docker-install) or on the Data Lab RStudio server.
-It covers cell-type identification, integration of multiple single-cell RNA-seq libraries, and differential expression analyses, among other related topics.
+The module builds on material in the [scRNA-seq module](https://github.com/AlexsLemonade/training-modules/tree/master/scRNA-seq), and analyses are designed to be performed within a [Docker container](https://github.com/AlexsLemonade/training-modules/blob/master/Dockerfile) or on the Data Lab RStudio server.
+It covers an overview of scRNA-seq processing, integration of multiple single-cell RNA-seq libraries, and differential expression analyses, among other related topics.
 
 The notebooks that comprise this module are:
 
 - [Introduction and review of processing scRNA-seq data](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/01-read_filter_normalize_scRNA.nb.html)
-- [Integration of multiple scRNA-seq libraries](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/03-dataset_integration.nb.html)
-- [Differential expression analysis](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/04-differential_expression.nb.html)
-
+- [Integration of multiple scRNA-seq libraries](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/02-dataset_integration.nb.html)
+- [Differential expression analysis](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/03-differential_expression.nb.html)
 
 This directory also contains pathway analysis modules customized to these data:
 
-- [Overrepresentation analysis](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/04-overrepresentation_analysis.nb.html)
-- [Gene set enrichment analysis](https://alexslemonade.github.io/training-modules/scRNA-seq-advanced/05-gene_set_enrichment_analysis.nb.html)
-
+- [Gene-set enrichment analysis](04-gene_set_enrichment_analysis.nb.html)
+- [Pathway analysis with AUCell](05-aucell.nb.html)
 
 Additional exercise notebooks:
 
 - [Working with CITE-seq data](https://github.com/AlexsLemonade/training-modules/blob/master/scRNA-seq-advanced/exercise_01-citeseq.Rmd)
 - [Integration of multiple scRNA-seq libraries](https://github.com/AlexsLemonade/training-modules/blob/master/scRNA-seq-advanced/exercise_02-integration.Rmd)
 - [Differential expression analysis](https://github.com/AlexsLemonade/training-modules/blob/master/scRNA-seq-advanced/exercise_03-diffexp.Rmd)
 - [Pathway analysis](https://github.com/AlexsLemonade/training-modules/blob/master/scRNA-seq-advanced/exercise_04-scrna_pathway.Rmd)
+- [Evaluating clusters](https://github.com/AlexsLemonade/training-modules/blob/master/scRNA-seq-advanced/exercise_05-cluster_evaluation.Rmd)

scRNA-seq-advanced/setup/README.md

@@ -94,3 +94,33 @@ snakemake -j2
 ```
 
 This will place the downloaded files in `/shared/data/training-modules/scRNA-seq-advanced/data/pancreas`
+
+
+### Ewing Sarcoma
+
+The data for these samples comes from project `SCPCP000015` in the [ScPCA Portal](https://scpca.alexslemonade.org/projects/SCPCP000015).
+
+The included script uses the [`download-data.py` from the `OpenScPCA-analysis` repository](https://github.com/AlexsLemonade/OpenScPCA-analysis/blob/main/download-data.py) to download a set of processed library files.
+
+To produce the files, change directories to the `setup/ewing-sarcoma` directory and run:
+
+```sh
+./download-openscpca-data.sh
+```
+
+For additional script options, refer to `ewing-sarcoma/README.md`.
+
+
+### Wilms tumor
+
+The data for this sample comes from project `SCPCP000006` in the [ScPCA Portal](https://scpca.alexslemonade.org/projects/SCPCP000006).
+
+The included script uses the [`download-data.py` from the `OpenScPCA-analysis` repository](https://github.com/AlexsLemonade/OpenScPCA-analysis/blob/main/download-data.py) to download a set of processed library files.
+
+To produce the files, change directories to the `setup/wilms-tumor` directory and run:
+
+```sh
+./download-openscpca-data.sh
+```
+
+For additional script options, refer to `wilms-tumor/README.md`.

scRNA-seq/README.md

@@ -1,8 +1,8 @@
 # scRNA-Seq Training Module
 
-This CCDL-designed module covers the analysis of single-cell RNA-seq data using [Salmon's Alevin](https://salmon.readthedocs.io/en/latest/alevin.html) and [scater/scran](https://bioconductor.org/packages/devel/bioc/vignettes/scran/inst/doc/scran.html) packages.
+This Childhood Cancer Data Lab-designed module covers the analysis of single-cell RNA-seq data using [Salmon's Alevin](https://salmon.readthedocs.io/en/latest/alevin.html) and [scater/scran](https://bioconductor.org/packages/devel/bioc/vignettes/scran/inst/doc/scran.html) packages.
 
-It depends on knowledge gained in the [Intro to R](https://github.com/AlexsLemonade/training-modules/tree/master/intro-to-R-tidyverse) and analyses are performed within a [Docker container](https://github.com/AlexsLemonade/training-modules/tree/master/docker-install) or on the CCDL RStudio server.
+It depends on knowledge gained in the [Intro to R](https://github.com/AlexsLemonade/training-modules/tree/master/intro-to-R-tidyverse) module, and analyses are performed within a [Docker container](https://github.com/AlexsLemonade/training-modules/blob/master/Dockerfile) or on the Data Lab RStudio server.
 It covers normalization and dimension reduction methods that can be used for both tag-based and full-length single-cell data, as well as the quantification of tag-based scRNA-seq data.
 
 The notebooks that comprise this module are: