├── .gitignore
├── README.md
├── docs
├── CNAME
├── P1_W01.introduction.md
├── P1_W02.learning-r-and-bioconductor.md
├── P1_W03.beyond-r-basics.md
├── P1_W04.data-infrastructure.md
├── P2_W01.overview.md
├── P2_W01.overview_files
│ └── figure-html
│ │ └── quick-start-umap-1.png
├── P2_W02.quality-control.md
├── P2_W02.quality-control_files
│ └── figure-html
│ │ ├── ambientpvalhist-1.png
│ │ ├── discardplot416b-1.png
│ │ ├── discardplotpbmc-1.png
│ │ ├── qc-dist-416b-1.png
│ │ ├── qc-mito-416b-1.png
│ │ ├── qc-mito-pbmc-1.png
│ │ ├── qc-mito-spike-zeisel-1.png
│ │ ├── qc-mito-zeisel-1.png
│ │ ├── qc-mito2-416b-1.png
│ │ ├── qc-plot-pancreas-1.png
│ │ └── rankplot-1.png
├── P2_W03.normalization.md
├── P2_W03.normalization_files
│ └── figure-html
│ │ ├── cellbench-lognorm-downsample-1.png
│ │ ├── cellbench-lognorm-fail-1.png
│ │ ├── deconv-zeisel-1.png
│ │ ├── histlib-1.png
│ │ ├── norm-effect-malat-1.png
│ │ └── norm-spike-t-1.png
├── P2_W04.feature-selection.md
├── P2_W04.feature-selection_files
│ └── figure-html
│ │ ├── blocked-fit-1.png
│ │ ├── cv2-pbmc-1.png
│ │ ├── spike-416b-1.png
│ │ ├── tech-pbmc-1.png
│ │ └── trend-plot-pbmc-1.png
├── P2_W05.reduced-dimensions.md
├── P2_W05.reduced-dimensions_files
│ └── figure-html
│ │ ├── cluster-pc-choice-1.png
│ │ ├── elbow-1.png
│ │ ├── heat-nmf-zeisel-1.png
│ │ ├── tsne-brain-1.png
│ │ ├── tsne-perplexity-1.png
│ │ ├── umap-brain-1.png
│ │ ├── zeisel-pca-1.png
│ │ └── zeisel-pca-multi-1.png
├── P2_W06.clustering.md
├── P2_W06.clustering_files
│ └── figure-html
│ │ ├── bootstrap-matrix-1.png
│ │ ├── ccr7-dist-memory-1.png
│ │ ├── cd48-memory-expression-1.png
│ │ ├── cluster-graph-1.png
│ │ ├── cluster-mod-1.png
│ │ ├── dend-416b-1.png
│ │ ├── dend-cluster-1.png
│ │ ├── kmeans-gap-1.png
│ │ ├── kmeans-tree-1.png
│ │ ├── pbmc-force-1.png
│ │ ├── silhouette416b-1.png
│ │ ├── tsne-416b-1.png
│ │ ├── tsne-clust-graph-1.png
│ │ ├── tsne-clust-kmeans-1.png
│ │ ├── tsne-clust-kmeans-best-1.png
│ │ ├── tsne-kmeans-graph-pbmc-1.png
│ │ └── walktrap-v-others-1.png
├── P2_W07.marker-detection.md
├── P2_W07.marker-detection_files
│ └── figure-html
│ │ ├── comparative-markers-tw-1.png
│ │ ├── heat-basic-pbmc-1.png
│ │ ├── heat-focused-pbmc-1.png
│ │ ├── heat-wmw-pbmc-1.png
│ │ ├── pval-dist-1.png
│ │ ├── viol-de-binom-1.png
│ │ └── viol-gcg-lawlor-1.png
├── P2_W08.cell-annotation.md
├── P2_W08.cell-annotation_files
│ └── figure-html
│ │ ├── aucell-boxplots-1.png
│ │ ├── aucell-muraro-heat-1.png
│ │ ├── lipid-synth-violin-1.png
│ │ ├── singler-cluster-1.png
│ │ ├── singler-comp-pancreas-1.png
│ │ ├── singler-dist-pbmc-1.png
│ │ ├── singler-heat-pbmc-1.png
│ │ ├── thrsp-violin-1.png
│ │ └── violin-milk-1.png
├── P2_W09.data-integration.md
├── P2_W09.data-integration_files
│ └── figure-html
│ │ ├── coassign-after-mnn-1.png
│ │ ├── heat-after-mnn-1.png
│ │ ├── pbmc-marker-blocked-1.png
│ │ ├── tsne-pbmc-corrected-1.png
│ │ ├── tsne-pbmc-corrected-markers-1.png
│ │ ├── tsne-pbmc-rescaled-1.png
│ │ └── tsne-pbmc-uncorrected-1.png
├── P2_W10.sample-comparisons.md
├── P2_W10.sample-comparisons_files
│ └── figure-html
│ │ ├── abplotbcv-1.png
│ │ ├── abplotql-1.png
│ │ ├── bcvplot-1.png
│ │ ├── exprs-unique-de-allantois-1.png
│ │ ├── heat-cluster-label-1.png
│ │ ├── qlplot-1.png
│ │ └── tsne-initial-1.png
├── P2_W11.doublet-detection.md
├── P2_W11.doublet-detection_files
│ └── figure-html
│ │ ├── densclust-1.png
│ │ ├── denstsne-1.png
│ │ ├── doublet-prop-hash-dist-1.png
│ │ ├── hash-barcode-rank-1.png
│ │ ├── heatclust-1.png
│ │ ├── hto-1to2-hist-1.png
│ │ ├── hto-2to3-hash-1.png
│ │ ├── markerexprs-1.png
│ │ └── tsne-hash-1.png
├── P2_W12.cell-cycle.md
├── P2_W12.cell-cycle_files
│ └── figure-html
│ │ ├── dist-lef1-1.png
│ │ ├── heat-cyclin-1.png
│ │ └── phaseplot416b-1.png
├── P2_W13.trajectory.md
├── P2_W13.trajectory_files
│ └── figure-html
│ │ ├── traj-princurve-clustered-nest-1.png
│ │ ├── traj-princurve-clustered-nest-approx-1.png
│ │ ├── traj-princurve-pca-nest-1.png
│ │ ├── traj-princurve-umap-nest-1.png
│ │ ├── tscan-nest-mst-1.png
│ │ ├── tscan-nest-pseudo-1.png
│ │ └── tscan-nest-tsne-1.png
├── P2_W14.protein-abundance.md
├── P2_W14.protein-abundance_files
│ └── figure-html
│ │ ├── combined-umap-1.png
│ │ ├── comp-bias-norm-1.png
│ │ ├── control-bias-norm-1.png
│ │ ├── detected-ab-hist-1.png
│ │ ├── gzmh-cd8-t-1.png
│ │ ├── heat-pd-1-1.png
│ │ ├── heat-tags-1.png
│ │ ├── subcluster-stats-1.png
│ │ ├── subcluster-tag-dist-1.png
│ │ ├── tsne-naive-1.png
│ │ └── tsne-tags-1.png
├── P2_W15.repertoire-seq.md
├── P2_W15.repertoire-seq_files
│ └── figure-html
│ │ ├── expanded-t-clusters-1.png
│ │ ├── tcr-prop-cluster-all-1.png
│ │ ├── tcr-prop-cluster-multi-1.png
│ │ ├── tcr-prop-cluster-prod-1.png
│ │ └── unnamed-chunk-10-1.png
├── P2_W16.interactive.md
├── P2_W17.big-data.md
├── P2_W18.interoperability.md
├── P3_W01.lun-416b.md
├── P3_W01.lun-416b_files
│ └── figure-html
│ │ ├── unref-416b-markers-1.png
│ │ ├── unref-416b-norm-1.png
│ │ ├── unref-416b-qc-comp-1.png
│ │ ├── unref-416b-qc-dist-1.png
│ │ ├── unref-416b-silhouette-1.png
│ │ ├── unref-416b-tsne-1.png
│ │ └── unref-416b-variance-1.png
├── P3_W02.zeisel-brain.md
├── P3_W02.zeisel-brain_files
│ └── figure-html
│ │ ├── unref-zeisel-heat-cell-1.png
│ │ ├── unref-zeisel-heat-lfc-1.png
│ │ ├── unref-zeisel-norm-1.png
│ │ ├── unref-zeisel-qc-comp-1.png
│ │ ├── unref-zeisel-qc-dist-1.png
│ │ ├── unref-zeisel-tsne-1.png
│ │ └── unref-zeisel-var-1.png
├── P3_W03.tenx-unfiltered-pbmc4k.md
├── P3_W03.tenx-unfiltered-pbmc4k_files
│ └── figure-html
│ │ ├── unnamed-chunk-10-1.png
│ │ ├── unref-unfiltered-pbmc-mito-1.png
│ │ ├── unref-unfiltered-pbmc-norm-1.png
│ │ ├── unref-unfiltered-pbmc-qc-1.png
│ │ ├── unref-unfiltered-pbmc-tsne-1.png
│ │ └── unref-unfiltered-pbmc-var-1.png
├── P3_W04.tenx-filtered-pbmc3k-4k-8k.md
├── P3_W04.tenx-filtered-pbmc3k-4k-8k_files
│ └── figure-html
│ │ ├── unref-filtered-pbmc-merged-tsne-1.png
│ │ ├── unref-filtered-pbmc-tsne-1.png
│ │ ├── unref-filtered-pbmc-variance-1.png
│ │ └── unref-pbmc-filtered-var-1.png
├── P3_W05.tenx-repertoire-pbmc8k.md
├── P3_W05.tenx-repertoire-pbmc8k_files
│ └── figure-html
│ │ ├── unref-clustmod-pbmc-adt-1.png
│ │ ├── unref-norm-pbmc-adt-1.png
│ │ ├── unref-pbmc-adt-qc-1.png
│ │ ├── unref-pbmc-adt-qc-mito-1.png
│ │ └── unref-tsne-pbmc-adt-1.png
├── P3_W06.grun-pancreas.md
├── P3_W06.grun-pancreas_files
│ └── figure-html
│ │ ├── unref-416b-variance-1.png
│ │ ├── unref-grun-norm-1.png
│ │ ├── unref-grun-qc-dist-1.png
│ │ └── unref-grun-tsne-1.png
├── P3_W07.muraro-pancreas.md
├── P3_W07.muraro-pancreas_files
│ └── figure-html
│ │ ├── unref-muraro-norm-1.png
│ │ ├── unref-muraro-qc-dist-1.png
│ │ ├── unref-muraro-tsne-1.png
│ │ ├── unref-muraro-variance-1.png
│ │ └── unref-seger-heat-1.png
├── P3_W08.lawlor-pancreas.md
├── P3_W08.lawlor-pancreas_files
│ └── figure-html
│ │ ├── unnamed-chunk-4-1.png
│ │ ├── unref-grun-tsne-1.png
│ │ ├── unref-lawlor-norm-1.png
│ │ ├── unref-lawlor-qc-comp-1.png
│ │ └── unref-lawlor-qc-dist-1.png
├── P3_W09.segerstolpe-pancreas.md
├── P3_W09.segerstolpe-pancreas_files
│ └── figure-html
│ │ ├── unref-grun-tsne-1.png
│ │ ├── unref-seger-heat-1-1.png
│ │ ├── unref-seger-heat-2-1.png
│ │ ├── unref-seger-norm-1.png
│ │ ├── unref-seger-qc-dist-1.png
│ │ └── unref-seger-variance-1.png
├── P3_W10.merged-pancreas.md
├── P3_W10.merged-pancreas_files
│ └── figure-html
│ │ ├── tsne-pancreas-mnn-1.png
│ │ ├── tsne-pancreas-mnn-donor-1.png
│ │ ├── tsne-pancreas-mnn-donor-all-1.png
│ │ ├── tsne-pancreas-mnn-many-1.png
│ │ └── tsne-pancreas-rescaled-1.png
├── P3_W11.grun-hsc.md
├── P3_W11.grun-hsc_files
│ └── figure-html
│ │ ├── unref-heat-hgrun-markers-1.png
│ │ ├── unref-hgrun-norm-1.png
│ │ ├── unref-hgrun-qc-dist-1.png
│ │ ├── unref-hgrun-tsne-1.png
│ │ └── unref-hgrun-var-1.png
├── P3_W12.nestorowa-hsc.md
├── P3_W12.nestorowa-hsc_files
│ └── figure-html
│ │ ├── unref-assignments-nest-1.png
│ │ ├── unref-heat-nest-markers-1.png
│ │ ├── unref-nest-facs-1.png
│ │ ├── unref-nest-norm-1.png
│ │ ├── unref-nest-qc-dist-1.png
│ │ ├── unref-nest-tsne-1.png
│ │ └── unref-nest-var-1.png
├── P3_W13.pijuan-embryo.md
├── P3_W13.pijuan-embryo_files
│ └── figure-html
│ │ ├── unref-pijuan-tsne-1.png
│ │ ├── unref-pijuan-var-1.png
│ │ ├── unref-pijuan-var-2.png
│ │ └── unref-pijuan-var-3.png
├── P3_W14.bach-mammary.md
├── P3_W14.bach-mammary_files
│ └── figure-html
│ │ ├── unref-bach-norm-1.png
│ │ ├── unref-bach-qc-comp-1.png
│ │ ├── unref-bach-qc-dist-1.png
│ │ ├── unref-bach-tsne-1.png
│ │ └── unref-bach-var-1.png
├── P3_W15.hca-bone-marrow.md
├── P3_W15.hca-bone-marrow_files
│ └── figure-html
│ │ ├── unref-hca-bone-ab-1.png
│ │ ├── unref-hca-bone-dotplot-1.png
│ │ ├── unref-hca-bone-hclust-1.png
│ │ ├── unref-hca-bone-mito-1.png
│ │ ├── unref-hca-bone-qc-1.png
│ │ ├── unref-hca-bone-umap-1.png
│ │ └── unref-hca-bone-var-1.png
├── P4_W01.about-the-contributors.md
├── P4_W02.bibliography.md
├── bach-mouse-mammary-gland-10x-genomics.html
├── beyond-r-basics.html
├── bibliography.html
├── cell-cycle-assignment.html
├── cell-type-annotation.html
├── clustering.html
├── contributors.html
├── data-infrastructure.html
├── dealing-with-big-data.html
├── dimensionality-reduction.html
├── doublet-detection.html
├── feature-selection.html
├── filtered-human-pbmcs-10x-genomics.html
├── grun-human-pancreas-cel-seq2.html
├── hca-human-bone-marrow-10x-genomics.html
├── human-pbmc-10x-dataset-surface-proteins.html
├── index.html
├── index.md
├── integrating-datasets.html
├── integrating-with-protein-abundance.html
├── interactive-sharing.html
├── interoperability.html
├── introduction.html
├── lawlor-human-pancreas-smarter.html
├── learning-r-and-more.html
├── libs
│ ├── gitbook-2.6.7
│ │ ├── css
│ │ │ ├── fontawesome
│ │ │ │ └── fontawesome-webfont.ttf
│ │ │ ├── plugin-bookdown.css
│ │ │ ├── plugin-clipboard.css
│ │ │ ├── plugin-fontsettings.css
│ │ │ ├── plugin-highlight.css
│ │ │ ├── plugin-search.css
│ │ │ ├── plugin-table.css
│ │ │ └── style.css
│ │ └── js
│ │ │ ├── app.min.js
│ │ │ ├── clipboard.min.js
│ │ │ ├── jquery.highlight.js
│ │ │ ├── lunr.js
│ │ │ ├── plugin-bookdown.js
│ │ │ ├── plugin-clipboard.js
│ │ │ ├── plugin-fontsettings.js
│ │ │ ├── plugin-search.js
│ │ │ └── plugin-sharing.js
│ └── jquery-2.2.3
│ │ └── jquery.min.js
├── lun-416b-cell-line-smart-seq2.html
├── marker-detection.html
├── merged-hcsc.html
├── merged-pancreas.html
├── multi-sample-comparisons.html
├── muraro-human-pancreas-cel-seq.html
├── nestorowa-mouse-hsc-smart-seq2.html
├── normalization.html
├── overview.html
├── pijuan-sala-chimeric-mouse-embryo-10x-genomics.html
├── quality-control.html
├── repertoire-seq.html
├── search_index.json
├── segerstolpe-human-pancreas-smart-seq2.html
├── style.css
├── trajectory-analysis.html
├── unfiltered-human-pbmcs-10x-genomics.html
└── zeisel-mouse-brain-strt-seq.html
└── images
├── ._SingleCellExperiment.png
├── SCE-dependency-graph.png
├── SingleCellExperiment.png
├── Workflow.png
├── cover.png
└── iSEE-dataflow.png
/.gitignore:
--------------------------------------------------------------------------------
1 | ## OS junk
2 | .DS_STORE
3 |
4 | ## Book specific
5 | .Rproj.user
6 | _bookdown_files
7 | _bookdown_files/*
8 | *_cache
9 | ## *_files
10 | *.rdb
11 | *.rdx
12 | *.rds
13 | raw_data
14 | workflows
15 | *.Rmd
16 |
17 | # History files
18 | .Rhistory
19 | .Rapp.history
20 |
21 | # Session Data files
22 | .RData
23 | *.rdb
24 | *.rdx
25 | *.rds
26 | *.RData
27 |
28 | # User-specific files
29 | .Ruserdata
30 |
31 | # Example code in package build process
32 | *-Ex.R
33 |
34 | # Output files from R CMD build
35 | /*.tar.gz
36 |
37 | # Output files from R CMD check
38 | /*.Rcheck/
39 |
40 | # RStudio files
41 | .Rproj.user/
42 |
43 | # produced vignettes
44 | vignettes/*.html
45 | vignettes/*.pdf
46 |
47 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
48 | .httr-oauth
49 |
50 | # knitr and R markdown default cache directories
51 | /*_cache/
52 | /cache/
53 |
54 | # Temporary files created by R markdown
55 | *.utf8.md
56 | *.knit.md
57 |
58 | # docs
59 | inst/doc
60 |
61 | # content in OSCABase
62 | OSCABase/
63 |
64 | ## (interactive) build stuff
65 | logs/
66 | OSCAlogs
67 |
68 | ## In case of build
69 | slurm*
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Redirecting...
2 |
3 | The canonical location for the OSCA book is now at https://bioconductor.org/books/release/OSCA/.
4 | This repository only exists to host a redirection to the new location for the book.
5 |
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/docs/CNAME:
--------------------------------------------------------------------------------
1 | osca.bioconductor.org
2 |
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/docs/P1_W01.introduction.md:
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1 | # (PART) Preamble {-}
2 |
3 |
4 | ```
5 | ##
18 | ##
19 | ##
39 | ```
40 |
41 | # Introduction
42 |
43 | [Bioconductor](https://bioconductor.org) is an open source, open development software project to provide tools for the analysis and comprehension of high-throughput genomic data. It is based primarily on the [R](http://www.r-project.org/) programming language.
44 |
45 | ## What you will learn
46 |
47 | The goal of this book is to provide a solid foundation in the usage of Bioconductor tools for single-cell RNA-seq analysis by walking through various steps of typical workflows using example datasets. We strive to tackle key concepts covered in the manuscript, __"Orchestrating Single-Cell Analysis with Bioconductor"__, with each workflow covering these in varying detail, as well as essential preliminaries that are important for following along with the workflows on your own.
48 |
49 | ### Preliminaries
50 |
51 | For those unfamiliar with R (and those looking to learn more), we recommend reading the [_Learning R and More_](#learning-r-and-more) chapter, which first and foremost covers how to get started with R. We point to many great online resources for learning R, as well as related tools that are nice to know for bioinformatic analysis. For advanced users, we also point to some extra resources that go beyond the basics. While we provide an extensive list of learning resources for the interested audience in this chapter, we only ask for [some familiarity with R](#getting-started-with-r) before going to the next section.
52 |
53 | We then briefly cover getting started with [_Using R and Bioconductor_](#using-r-and-bioconductor). Bioconductor, being its own repository, has a unique set of tools, documentation, resources, and practices that benefit from some extra explanation.
54 |
55 | [_Data Infrastructure_](#data-infrastructure) merits a separate chapter. The reason for this is that common data containers are an essential part of Bioconductor workflows because they enable interoperability across packages, allowing for "plug and play" usage of cutting-edge tools. Specifically, here we cover the _SingleCellExperiment_ class in depth, as it has become the working standard for Bioconductor based single-cell analysis packages.
56 |
57 | Finally, before diving into the various workflows, armed with knowledge about the _SingleCellExperiment_ class, we briefly discuss the datasets that will be used throughout the book in [_About the Data_](#about-the-data).
58 |
59 |
60 | ### Workflows
61 |
62 | All workflows begin with data import and subsequent _quality control and normalization_, going from a raw (count) expression matrix to a clean one. This includes adjusting for experimental factors and possibly even latent factors. Using the clean expression matrix, _feature selection_ strategies can be applied to select the features (genes) driving heterogeneity. Furthermore, these features can then be used to perform _dimensionality reduction_, which enables downstream analysis that would not otherwise be possible and visualization in 2 or 3 dimensions.
63 |
64 | From there, the workflows largely focus on differing downstream analyses. _Clustering_ details how to segment a scRNA-seq dataset, and _differential expression_ provides a means to determine what drives the differences between different groups of cells. _Integrating datasets_ walks through merging scRNA-seq datasets, an area of need as the number of scRNA-seq datasets continues to grow and comparisons between datasets must be done. Finally, we touch upon how to work with _large scale data_, specifically where it becomes impractical or impossible to work with data solely in-memory.
65 |
66 | As an added bonus, we dedicate a chapter to _interactive visualization_, which focuses on using the _iSEE_ package to enable active exploration of a single cell experiment's data.
67 |
68 |
69 | ## What you won't learn
70 |
71 | The field of bioinformatic analysis is large and filled with many potential trajectories depending on the biological system being studied and technology being deployed. Here, we only briefly survey some of the many tools available for the analysis of scRNA-seq, focusing on Bioconductor packages. It is impossible to thoroughly review the plethora of tools available through R and Bioconductor for biological analysis in one book, but we hope to provide the means for further exploration on your own.
72 |
73 | Thus, it goes without saying that you may not learn the optimal workflow for your own data from our examples - while we strive to provide high quality templates, they should be treated as just that - a template from which to extend upon for your own analyses.
74 |
75 |
76 | ## Who we wrote this for
77 |
78 | We've written this book with the interested experimental biologist in mind, and do our best to make few assumptions on previous programming or statistical experience. Likewise, we also welcome more seasoned bioinformaticians who are looking for a starting point from which to dive into single-cell RNA-seq analysis. As such, we welcome any and all feedback for improving this book to help increase accessibility and refine technical details.
79 |
80 |
81 | ## Why we wrote this
82 |
83 | This book was conceived in the fall of 2018, as single-cell RNA-seq analysis continued its rise in prominence in the field of biology. With its rapid growth, and the ongoing developments within Bioconductor tailored specifically for scRNA-seq, it became apparent that an update to the [Orchestrating high-throughput genomic analysis with Bioconductor](https://www.nature.com/articles/nmeth.3252) paper was necessary for the age of single-cell studies.
84 |
85 | We strive to highlight the fantastic software by people who call Bioconductor home for their tools, and in the process hope to showcase the Bioconductor community at large in continually pushing forward the field of biological analysis.
86 |
87 | ## Acknowledgements
88 |
89 | We would like to thank all Bioconductor contributors for their efforts in creating the definitive leading-edge repository of software for biological analysis. It is truly extraordinary to chart the growth of Bioconductor over the years. We are thankful for the wonderful community of scientists and developers alike that together make the Bioconductor community special.
90 |
91 | We would first and foremost like to thank the Bioconductor core team and the emerging targets subcommittee for commissioning this work, Stephanie Hicks and Raphael Gottardo for their continuous mentorship, and all our contributors to the companion manuscript of this book.
92 |
93 | We'd also like to thank Garret Grolemund and Hadley Wickham for their book, [R for Data Science](https://r4ds.had.co.nz/index.html), from which we drew stylistic and teaching inspiration. We also thank Levi Waldron and Aaron Lun for advice on the code-related aspects of managing the online version of this book.
94 |
95 |
96 |
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1 | # Beyond R Basics
2 |
3 | Here we briefly outline resources for taking your R programming to the next level, including resources for learning about package development. We also outline some companions to R that are good to know not only for package development, but also for running your own bioinformatic pipelines, enabling you to use a broader array of tools to go from raw data to preprocessed data before working in R.
4 |
5 |
6 | ## Becoming an R Expert {#becoming-an-r-expert}
7 |
8 | A deeper dive into the finer details of the R programming language is provided by the book [Advanced R](https://adv-r.hadley.nz/). While targeted at more experienced R users and programmers, this book represents a comprehensive compendium of more advanced concepts, and touches on some of the paradigms used extensively by developers throughout Bioconductor, specifically [programming with S4](https://adv-r.hadley.nz/s4.html).
9 |
10 | Eventually, you'll reach the point where you have your own collection of functions and datasets, and where you will be writing your own packages. Luckily, there's a guide for just that, with the book [R Packages](http://r-pkgs.had.co.nz/). Packages are great even if just for personal use, and of course, with some polishing, can eventually become available on CRAN or Bioconductor. Furthermore, they are also a great way of putting together code associated with a manuscript, promoting reproducible, accessible computing practices, something we all strive for in our work.
11 |
12 | For many of the little details that are oft forgotten learning about R, the aptly named [What They Forgot to Teach You About R](https://whattheyforgot.org/) is a great read for learning about the little things such as file naming, maintaining an R installation, and reproducible analysis habits.
13 |
14 | Finally, we save the most intriguing resource for last - another book for those on the road to becoming an R expert is [R Inferno](https://www.burns-stat.com/pages/Tutor/R_inferno.pdf), which dives into many of the unique quirks of R. _Warning: this book goes *very* deep into the painstaking details of R._
15 |
16 |
17 |
18 | ## Becoming an R/Bioconductor Developer {#becoming-a-bioconductor-developer}
19 |
20 | While [learning to use Bioconductor](#bioconductor-documentation) tools is a very welcoming experience, unfortunately there is no central resource for navigating the plethora of gotchas and paradigms associated with developing for Bioconductor. Based on conversations with folks involved in developing for Bioconductor, much of this knowledge is hard won and fairly spread out. This however is beginning to change with more recent efforts led by the Bioconductor team, and while this book represents an earnest effort towards addressing the user perspective, it is currently out of scope to include a deep dive about the developer side.
21 |
22 | For those looking to get started with developing packages for Bioconductor, it is important to first become acquainted with developing standalone R packages. To this end, the [R Packages](https://r-pkgs.org/) book provides a deep dive into the details of constructing your own package, as well as details regarding submission of a package to CRAN. For programming practices,
23 |
24 | With that, some resources that are worth looking into to get started are the [BiocWorkshops](https://github.com/Bioconductor/BiocWorkshops) repository under the [Bioconductor Github](https://github.com/Bioconductor/) provides a book composed of workshops that have been hosted by Bioconductor team members and contributors. These workshops center around learning, using, and developing for Bioconductor. A host of topics are also available via the [Learn](http://bioconductor.org/help/course-materials/) module on the Bioconductor website. Finally, the Bioconductor [developers portal](https://www.bioconductor.org/developers/) contains a bevy of individual resources and guides for experienced R developers.
25 |
26 |
27 | ## Nice Companions for R {#nice-companions-for-r}
28 |
29 | While not essential for our purposes, many bioinformatic tools for processing raw sequencing data require knowledge beyond just R to install, run, and import their results into R for further analysis. The most important of which are basic knowledge of the Shell/Bash utilities, for working with bioinformatic pipelines and troubleshooting (R package) installation issues.
30 |
31 | Additionally, for working with packages or software that are still in development and not hosted on an official repository like CRAN or Bioconductor, knowledge of Git - a version control system - and the popular GitHub hosting service is helpful. This enables you to not only work with other people's code, but also better manage your own code to keep track of changes.
32 |
33 | ### Shell/Bash
34 |
35 | Datacamp and other interactive online resources such as [Codecademy](https://www.codecademy.com/catalog/subject/all) are great places to learn some of these extra skills. We highly recommend learning [Shell/Bash](https://www.datacamp.com/courses/tech:shell), as it is the starting point for most bioinformatic processing pipelines.
36 |
37 | ### Git
38 |
39 | We would recommend learning [Git](https://www.datacamp.com/courses/tech:git) next, a system for code versioning control which underlies the popular [GitHub](https://Github.com) hosting service, where many of the most popular open source tools are hosted. Learning Git is essential not only for keeping track of your own code, but also for using, managing, and contributing to open source software projects.
40 |
41 | For a more R centric look at using Git (and Github), we highly recommend checking out [Happy Git and Github for the useR](https://happygitwithr.com/).
42 |
43 | ### Other Languages
44 |
45 | A frequent question that comes up is "What else should I learn besides R?" Firstly, we believe that [honing your R skills](#getting-started-with-r) is first and foremost, and beyond just R, learning Shell/Bash and Git covered in the [_Nice Companions for R_](#nice-companions-for-r) section are already a great start. For those just getting started, these skills should become comfortable in practice before moving on.
46 |
47 | However, there are indeed benefits to going beyond just R. At a basic level, learning other programming languages helps broaden one's perspective - similar to learning multiple spoken or written languages, learning about other programming languages (even if only in a cursory manner) helps one identify broader patterns that may be applicable across languages.
48 |
49 | At an applied level, work within and outside of R has made it ever more friendly now than ever before with multi-lingual setups and teams, enabling the use of the best tool for the job at hand. For example, [Python](https://www.python.org/) is another popular language used in both data science and a broader array of applications as well. R now supports a native Python interface via the [_reticulate_](https://github.com/rstudio/reticulate) package, enabling access to tools developed originally in Python such as the popular [TensorFlow](https://tensorflow.rstudio.com/) framework for machine learning applications. [C++](http://www.cplusplus.com/) is frequently used natively in R as well via [Rcpp](http://www.rcpp.org/) in packages to massively accelerate computations. Finally, multiple languages are supported in code documents and reports through [R Markdown](https://rmarkdown.rstudio.com/lesson-5.html).
50 |
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1 | ---
2 | output:
3 | html_document
4 | bibliography: ../ref.bib
5 | ---
6 |
7 | # Interoperability
8 |
9 |
22 |
23 |
43 |
44 | ## Motivation
45 |
46 | The Bioconductor single-cell ecosystem is but one of many popular frameworks for scRNA-seq data analysis.
47 | *[Seurat](https://CRAN.R-project.org/package=Seurat)* is very widely used for analysis of droplet-based datasets while _scanpy_ provides an option for users who prefer working in Python.
48 | In many scenarios, these frameworks provide useful functionality that we might want to use from a Bioconductor-centric analysis (or vice versa).
49 | For example, Python has well-established machine learning libraries while R has a large catalogue of statistical tools, and it would be much better to use this functionality directly than to attempt to transplant it into a new framework.
50 | However, effective re-use requires some consideration towards interoperability during development of the relevant software tools.
51 |
52 | In an ideal world, everyone would agree on a common data structure that could be seamlessly and faithfully exchanged between frameworks.
53 | In the real world, though, each framework uses a different structure for various pragmatic or historical reasons.
54 | (This [obligatory _xkcd_](https://xkcd.com/927/) sums up the situation.)
55 | Most single cell-related Bioconductor packages use the `SingleCellExperiment` class, as previously discussed; *[Seurat](https://CRAN.R-project.org/package=Seurat)* defines its own `SeuratObject` class; and _scanpy_ has its `AnnData` class.
56 | This inevitably introduces some friction if we are forced to convert from one structure to another in order to use another framework's methods.
57 |
58 | In the absence of coordination of data structures, the next best solution is for each framework to provide methods that can operate on its most basic data object.
59 | Depending on the method, this might be the count matrix, the normalized expression matrix, a matrix of PCs or a graph object.
60 | If such methods are available, we can simply extract the relevant component from our `SingleCellExperiment` and call an external method directly without having to assemble that framework's structure.
61 | Indeed, it is for this purpose that almost all *[scran](https://bioconductor.org/packages/3.11/scran)* functions and many *[scater](https://bioconductor.org/packages/3.11/scater)* functions are capable of accepting matrix objects or equivalents (e.g., sparse matrices) in addition to `SingleCellExperiment`s.
62 |
63 | In this chapter, we will provide some examples of using functionality from frameworks outside of the `SingleCellExperiment` ecosystem in a single-cell analysis.
64 | We will focus on *[Seurat](https://CRAN.R-project.org/package=Seurat)* and _scanpy_ as these are the two of the most popular analysis frameworks in the field.
65 | However, the principles of interoperability are generally applicable and are worth keeping in mind when developing or evaluating any type of analysis software.
66 |
67 | ## Interchanging with _Seurat_
68 |
69 |
118 |
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1 | # (PART) Appendix {-}
2 |
3 | # Contributors
4 |
5 | ### *Aaron Lun, PhD* {-}
6 |
7 | When one thinks of single-cell bioinformatics, one thinks of several titans who bestride the field.
8 | Unfortunately, they weren't available, so we had to make do with Aaron instead.
9 | He likes long walks on the beach (as long as there's Wifi) and travelling (but only in business class).
10 | His friends say that he is "absolutely insane" and "needs to get a life", or they would if they weren't mostly imaginary.
11 | His GitHub account is his Facebook and his Slack is his Twitter.
12 | He maintains more Bioconductor packages than he has phone numbers on his cell.
13 | He has a recurring event on his Google Calendar to fold his laundry.
14 | He is... the most boring man in the world.
15 | ("I don't often cry when I watch anime, but when I do, my tears taste like Dos Equis.")
16 | He currently works as a Scientist at Genentech after a stint as a research associate in John Marioni's group at the CRUK Cambridge Institute, which was preceded by a PhD with Gordon Smyth at the Walter and Eliza Hall Institute for Medical Research.
17 |
18 | ### *Robert Amezquita, PhD* {-}
19 |
20 | Robert Amezquita is a Postdoctoral Fellow in the Immunotherapy Integrated Research Center (IIRC) at Fred Hutch under the mentorship of Raphael Gottardo. His current research focuses on utilizing computational approaches leveraging transcriptional and epigenomic profiling at single-cell resolution to understand how novel anti-cancer therapeutics - ranging from small molecule therapies to biologics such as CAR-T cells - affect immune response dynamics. Extending from his graduate work at Yale's Dept. of Immunobiology, Robert's research aims to better understand the process of immune cell differentiation under the duress of cancer as a means to inform future immunotherapies. To accomplish this, Robert works collaboratively across the Fred Hutch IIRC with experimental collaborators, extensively utilizing R and Bioconductor for data analysis.
21 |
22 |
23 | ### *Stephanie Hicks, PhD* {-}
24 |
25 | Stephanie Hicks is an Assistant Professor in the [Department of Biostatistics](https://www.jhsph.edu/departments/biostatistics/) at [Johns Hopkins Bloomberg School of Public Health](https://www.jhsph.edu). Her research interests focus around developing statistical methods, tools and software for the analysis of genomics data. Specifically, her research addresses statistical challenges in epigenomics, functional genomics and single-cell genomics such as the pre-processing, normalization, analysis of noisy high-throughput data leading to an improved quantification and understanding of biological variability. She actively contributes software packages to [Bioconductor](https://bioconductor.org) and is involved in teaching courses for data science and the analysis of genomics data. She is also a faculty member of the [Johns Hopkins Data Science Lab](https://jhudatascience.org), co-host of [The Corresponding Author](https://twitter.com/CorrespondAuth) podcast and co-founder of [R-Ladies Baltimore](https://rladies-baltimore.github.io). For more information, please see http://www.stephaniehicks.com
26 |
27 |
28 | ### *Raphael Gottardo, PhD* {-}
29 |
30 | Raphael Gottardo is the Scientific Director of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch, J. Ordin Edson Foundation Endowed Chair, and Full Member within the Vaccine and Infectious Disease and Public Health Sciences Division. A pioneer in developing and applying statistical methods and software tools to distill actionable insights from large and complex biological data sets.In partnership with scientists and clinicians, he works to understand such diseases as cancer, HIV, malaria, and tuberculosis and inform the development of vaccines and treatments. He is a leader in forming interdisciplinary collaborations across the Hutch, as well as nationally and internationally, to address important research questions, particularly in the areas of vaccine research, human immunology, and immunotherapy. As director of the Translational Data Science Integrated Research Center, he fosters interaction between the Hutch’s experimental and clinical researchers and their computational and quantitative science colleagues with the goal of transforming patient care through data-driven research. Dr. Gottardo partners closely with the cancer immunotherapy program at Fred Hutch to improve treatments. For example, his team is harnessing cutting-edge computational methods to determine how cancers evade immunotherapy. He has made significant contributions to vaccine research and is the principal investigator of the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery.
31 |
32 | ### Other notable entities {-}
33 |
34 | Kevin Rue-Albrecht (University of Oxford, United Kingdom), for contributing the interactive data analysis chapter.
35 |
36 | Charlotte Soneson (Friedrich Miescher Institute, Switzerland), for many formatting and typographical fixes.
37 |
38 | Al J Abadi (University of Melbourne, Australia), for bringing the log-normalization corner case to our attention.
39 |
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1 | ---
2 | title: "Orchestrating Single-Cell Analysis with Bioconductor"
3 | date: "2020-07-02"
4 | site: bookdown::bookdown_site
5 | documentclass: book
6 | bibliography: [ref.bib, packages.bib]
7 | biblio-style: apalike
8 | link-citations: yes
9 | favicon: "favicon.ico"
10 | description: "Online companion to 'Orchestrating Single-Cell Analysis with Bioconductor' manuscript by the Bioconductor team."
11 | ---
12 |
13 | # Welcome {-}
14 |
15 |
16 |
17 | This is the website for __"Orchestrating Single-Cell Analysis with Bioconductor"__, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). This book will teach you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data. Additionally, it serves as an online companion for the manuscript __"Orchestrating Single-Cell Analysis with Bioconductor"__.
18 |
19 | While we focus here on scRNA-seq data, a newer technology that profiles transcriptomes at the single-cell level, many of the tools, conventions, and analysis strategies utilized throughout this book are broadly applicable to other types of assays. By learning the grammar of Bioconductor workflows, we hope to provide you a starting point for the exploration of your own data, whether it be scRNA-seq or otherwise.
20 |
21 | This book is organized into three parts. In the _Preamble_, we introduce the book and dive into resources for learning R and Bioconductor (both at a beginner and developer level). Part I ends with a tutorial for a key data infrastructure, the _SingleCellExperiment_ class, that is used throughout Bioconductor for single-cell analysis and in the subsequent section.
22 |
23 | The second part, _Focus Topics_, begins with an overview of the framework for analysis of scRNA-seq data, with deeper dives into specific topics are presented in each subsequent chapter.
24 |
25 | The third part, _Workflows_, provides primarily code detailing the analysis of various datasets throughout the book.
26 |
27 | Finally, the _Appendix_ highlights our contributors.
28 |
29 | If you would like to cite this work, please use the reference [__"Orchestrating Single-Cell Analysis with Bioconductor"__](https://www.nature.com/articles/s41592-019-0654-x).
30 |
31 | ---
32 |
33 | The book is written in [RMarkdown](https://rmarkdown.rstudio.com) with [bookdown](https://bookdown.org). OSCA is a collaborative effort, supported by various folks from the Bioconductor team who have contributed workflows, fixes, and improvements.
34 |
35 | This website is __free to use__, and is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivs 3.0](http://creativecommons.org/licenses/by-nc-nd/3.0/us/) License.
36 |
37 |
38 |
39 |
40 | _Version 0.0.1.9999_
41 | _Built on 2020-07-02_
42 |
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