misc updates to README and index

README.md

@@ -8,14 +8,15 @@ the analysis of single-cell data has been developed, making it hard to understan
 the critical steps in the analysis workflow and the best methods for each objective
 of one’s study.
 
-This tutorial aims to provide a solid foundation in using Bioconductor tools
-for single-cell RNA-seq analysis by walking through various steps of typical
-workflows using example datasets.
+This [Carpentries-style](https://carpentries.org/) tutorial aims to provide a
+solid foundation in using [Bioconductor](https://bioconductor.org) tools for
+single-cell RNA-seq analysis by walking through various steps of typical workflows
+using example datasets.
 
 This tutorial uses as a "text-book" the online book "Orchestrating Single-Cell
-Analysis with Bioconductor"
-([OSCA](https://bioconductor.org/books/release/OSCA/)), 
-started in 2018 and continuously updated by many contributors from the Bioconductor
+Analysis with Bioconductor" ([OSCA](https://bioconductor.org/books/release/OSCA/)),
+[published in 2020](https://doi.org/10.1038%2Fs41592-019-0654-x), 
+and continuously updated by many contributors from the Bioconductor
 community. Like the book, this tutorial strives to be of interest to the
 experimental biologists wanting to analyze their data and to the bioinformaticians
 approaching single-cell data.
@@ -35,11 +36,45 @@ In particular, participants will learn:
 * How to correct for batch effects and integrate multiple samples.
 * How to perform differential expression and differential abundance analysis between conditions.
 * How to work with large out-of-memory datasets.
+* How to interoperate with other popular single-cell analysis ecosystems.
 
-## Source
+## Other tools and tutorials for single-cell analysis
+
+The focus of this tutorial is on single-cell analysis with R packages from the 
+[Bioconductor](https://bioconductor.org) repository. Bioconductor packages are
+collaboratively developed by an international community of developers that agree
+on data and software standards to promote interoperability between packages,
+extensibility of analysis workflows, and reproducibility of published research.
+
+Other popular tools for single-cell analysis include:
+
+* [Seurat](https://satijalab.org/seurat/), a stand-alone R package that has
+pioneered elementary steps of typical single-cell analysis workflows, and 
+* [scverse](https://scverse.org/), a collection of Python packages for single-cell
+omics data analysis including [scanpy](https://scanpy.readthedocs.io) and
+[scvi-tools](https://scvi-tools.org/).
 
-This lesson uses [The Carpentries Workbench](https://carpentries.github.io/sandpaper-docs/) and is based on materials from the [OSCA tutorial at the ISMB 2023](https://bioconductor.github.io/ISMB.OSCA/).
+Tutorials for working with these tools are available elsewhere and are not covered
+in this tutorial. A demonstration of how to interoperate with `Seurat` and packages
+from the `scverse` is given in [Session 5](https://ccb-hms.github.io/osca-workbench/large_data.html)
+of this tutorial.
+
+Other Carpentries-style tutorials for single-cell analysis with a different scope include:
+
+- a [community-developed lesson](https://carpentries-incubator.github.io/scrna-seq-analysis/)
+ that makes use of command-line utilities and `scanpy` for basic preprocessing steps, 
+- and a [tutorial proposal](https://github.com/carpentries-incubator/proposals/issues/178)
+based on `Seurat`. 
+
+## Source
 
-As individual vignettes are converted into lessons, they can be added to `config.yaml` to be rendered and shown in the final Github Pages lesson.  
+This lesson uses [The Carpentries Workbench](https://carpentries.github.io/sandpaper-docs/)
+and is based on materials from the [OSCA tutorial at the ISMB 2023](https://bioconductor.github.io/ISMB.OSCA/).
 
+## Citation
 
+Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F,
+Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith ML, Huber W,
+Morgan M, Gottardo R, Hicks SC. Orchestrating single-cell analysis with
+Bioconductor. *Nature Methods*, 2020.
+doi: [10.1038/s41592-019-0654-x](https://doi.org/10.1038/s41592-019-0654-x)

index.md

@@ -8,32 +8,39 @@ in individual cells has become routine. Consequently, a plethora of tools for
 the analysis of single-cell data has been developed, making it hard to understand
 the critical steps in the analysis workflow and the best methods for each objective of one’s study.
 
-This tutorial aims to provide a solid foundation in using [Bioconductor](https://bioconductor.org)
+This [Carpentries-style](https://carpentries.org/) tutorial aims to provide a
+solid foundation in using [Bioconductor](https://bioconductor.org)
 tools for single-cell RNA-seq (scRNA-seq) analysis by walking through various steps of
 typical workflows using example datasets.
 
 This tutorial is based on the the online book "Orchestrating Single-Cell
 Analysis with Bioconductor" ([OSCA](https://bioconductor.org/books/release/OSCA/)),
-started in 2018 and continuously updated by many contributors from the Bioconductor community.
+[published in 2020](https://doi.org/10.1038%2Fs41592-019-0654-x), 
+and continuously updated by many contributors from the Bioconductor community.
 Like the book, this tutorial strives to be of interest to the experimental biologists
 wanting to analyze their data and to the bioinformaticians approaching single-cell data.
 
-This is a new lesson built with [The Carpentries Workbench][workbench]. 
-
-
-[workbench]: https://carpentries.github.io/sandpaper-docs
-
 ::::::::::::::::::::::::::::::::::::::::::  prereq
 
 ## Prerequisites
 
 - Familiarity with R/Bioconductor, such as the
 [Introduction to data analysis with R and Bioconductor](https://carpentries-incubator.github.io/bioc-intro/)
 lesson.
+- Familiarity with multivariate analysis and dimensionality reduction, such as
+[Chapter 7](https://www.huber.embl.de/msmb/07-chap.html) of the book
+*Modern Statistics for Modern Biology* by Holmes and Huber.
 - Familiarity with the biology of gene expression and scRNA-seq, such as the review article 
 [A practical guide to single-cell RNA-sequencing](https://doi.org/10.1186/s13073-017-0467-4) by Haque et.al.
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
+If you use materials of this lesson in published research, please cite:
+
+Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F,
+Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith ML, Huber W,
+Morgan M, Gottardo R, Hicks SC. Orchestrating single-cell analysis with
+Bioconductor. *Nature Methods*, 2020.
+doi: [10.1038/s41592-019-0654-x](https://doi.org/10.1038/s41592-019-0654-x)
 

learners/setup.md

@@ -141,7 +141,7 @@ BiocManager::install(c("AUCell", "batchelor", "BiocNeighbors",
                        "scuttle", "Seurat", "SeuratData", 
                        "SingleCellExperiment", "SingleR",
                        "TENxBrainData", "zellkonverter"),
-                     Ncpus = 4)
+                       Ncpus = 4)
 ```
 
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