bash
docker run -it ghcr.io/js2264/scrnaseqworkshop:devel RSingle-cell RNA-seq workshop ed. 2024
Package: scRNAseqWorkshop
Authors: Jacques Serizay [aut, cre]
Compiled: 2024-01-23
Package version: 0.98.0
R version: R version 4.3.2 (2023-10-31)
BioC version: 3.18
License: MIT
Welcome
This is the landing page of the workshop entitled Single-cell RNA-seq analysis with R/Bioconductor.
Overview
This course will introduce biologists and bioinformaticians to the field of single-cell RNA sequencing. We will cover a range of software and analysis workflows that extend over the spectrum from the best practices in the filtering scRNA-seq data
to the downstream analysis of cell clusters and temporal ordering. This course will help the attendees gain accurate insights in pre-processing, analysis and interpretation of scRNA-seq data.
We will start by introducing general concepts about single-cell RNA-sequencing. From there, we will then continue to describe the main analysis steps to go from raw sequencing data to processed and usable data. We will present classical analysis workflows, their output and the possible paths to investigate for downstream analysis.
Throughout this workshop, we will put an emphasis on R/Bioconductor ecosystem and the different packages which will be used to analyse datasets and learn new approaches.
Format
The course is structured in modules over five days.
During the first 1/3 of the day, formal lectures will cover the key concepts required to understand the principles of scRNA-seq analysis (~2h).
Following these lectures, practical examples will be shown to illustrate how to translate the acquired knowledge into functional R code (~1h). At this stage, trainees will get acquainted with state-of-the-art packages for scRNAseq analysis as well as the best practices in bioinformatics.
During the second half of the day (3h), trainees will work by themselves, following guided exercises to improve their understanding of scRNAseq analysis workflow. Hints and solution are provided for each exercise. The exercises will mainly focus on specific concepts introducted earlier that day. However, analytical steps studied throughout the previous days will also be integrated so that towards the end of the week, trainees are fully able to perform fundamental scRNAseq analyses from beginning to end.
Office hours will take place during the last hour of the exercises. An instructor will be available to answer individual questions related to daily exercises. A Slack channel will also be available so that Q&A are available for everybody.
Learning outcomes
At the end of this course, you should be able to:
- Understand the pros/cons of different single-cell RNA-seq methods
- Process and QC of scRNA-seq data
- Normalize scRNA-seq data
- Correct for batch effects
- Visualise the data and applying dimensionality reduction
- Perform cell clustering and annotation
- Perform differential gene expression analysis
- Infer cell trajectory and pseudotime, and perform temporal differential expression
Instructor
Program
Day 1
[1h] Lecture 1 - General introduction to single-cell RNA-seq experimental design [Pptx]
[1h] Lecture 2 - scRNAseq: from raw sequencing files to count matrix [Pptx]
[1h] Demonstration 1 - From fastq to count matrix [HTML | qmd (save to open in RStudio)]
[3h] Homework - From bcl to count matrix [HTML | qmd (save to open in RStudio)]
Day 2
[1h] Lecture 3 - Filtering cells in droplet-based scRNAseq data [Pptx]
[1h] Lecture 4 - Normalizing scRNAseq data [Pptx]
[BONUS] R/Bioconductor essentials:
GRangesand*Experimentclasses [Pptx][1h] Demonstration 2 - Leveraging R/Bioconductor for single-cell analyses [HTML | qmd (save to open in RStudio)]
[3h] Homework - scRNAseq analysis with R/Bioconductor (1/3) [HTML | qmd (save to open in RStudio)]
Day 3
[2h] Lecture 5 - Clustering cells in scRNAseq [Pptx]
[1h] Demonstration 3 - Dimensional reduction visualization and clustering [HTML | qmd (save to open in RStudio)]
[3h] Homework - scRNAseq analysis with R/Bioconductor (2/3) [HTML | qmd (save to open in RStudio)]
Day 4
[1h] Lecture 6 - Cell type annotations [Pptx]
[1h] Lecture 7 - Batch correction [PDF]
[1h] Demonstration 4 - Annotation transfer with
scmap[HTML | qmd (save to open in RStudio)][3h] Homework - scRNAseq analysis with R/Bioconductor (3/3) [HTML | qmd (save to open in RStudio)]
Day 5
[2h] Lecture 8 - Trajectory inference and RNA velocity [Pptx]
[1h] Demonstration 5 - Trajectory inference in multiciliated cells [HTML | qmd (save to open in RStudio)]
[3h] Homework - Advanced scRNAseq topics: trajectory inference and RNA velocity [HTML | qmd (save to open in RStudio)]
Docker image
A Docker image built from this repository is available here:
👉 ghcr.io/js2264/scrnaseqworkshop 🐳
Get started now 🎉
You can get access to all the packages used in this book in < 1 minute, using this command in a terminal:
RStudio Server
An RStudio Server instance can be initiated from the Docker image as follows:
bash
docker run \
--volume <local_folder>:<destination_folder> \
-e PASSWORD=OHCA \
-p 8787:8787 \
ghcr.io/js2264/scrnaseqworkshop:develThe initiated RStudio Server instance will be available at https://localhost:8787.
Course material
The workshop content is available at this adress: https://github.com/js2264/scRNAseq-workshop.
You can clone it locally with git:
git clone https://github.com/js2264/scRNAseq-workshop.gitTo download it without the command-line tool git, go to the GitHub repo page, click on the green Code button, then Download ZIP. Beware, the download may take a significant time based on your internet connection (several hundreds MB).
Session info
Click to expand 👇
sessioninfo::session_info(
installed.packages()[,"Package"],
include_base = TRUE
)
## ─ Session info ────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.2 (2023-10-31)
## os Ubuntu 22.04.3 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2024-01-23
## pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown)
##
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## pracma 2.4.4 2023-11-10 [2] RSPM (R 4.3.0)
## praise 1.0.0 2015-08-11 [2] RSPM (R 4.3.0)
## preprocessCore 1.64.0 2023-10-24 [2] Bioconductor
## prettyunits 1.2.0 2023-09-24 [2] RSPM (R 4.3.0)
## princurve 2.1.6 2021-01-18 [2] RSPM (R 4.3.0)
## processx 3.8.3 2023-12-10 [2] RSPM (R 4.3.0)
## profvis 0.3.8 2023-05-02 [2] RSPM (R 4.3.0)
## progress 1.2.3 2023-12-06 [2] RSPM (R 4.3.0)
## progressr 0.14.0 2023-08-10 [2] RSPM (R 4.3.0)
## promises 1.2.1 2023-08-10 [2] RSPM (R 4.3.0)
## ProtGenerics 1.34.0 2023-10-24 [2] Bioconductor
## proxy 0.4-27 2022-06-09 [2] RSPM (R 4.3.0)
## ps 1.7.6 2024-01-18 [2] RSPM (R 4.3.0)
## purrr 1.0.2 2023-08-10 [2] RSPM (R 4.3.0)
## R.methodsS3 1.8.2 2022-06-13 [2] RSPM (R 4.3.0)
## R.oo 1.25.0 2022-06-12 [2] RSPM (R 4.3.0)
## R.utils 2.12.3 2023-11-18 [2] RSPM (R 4.3.0)
## R6 2.5.1 2021-08-19 [2] RSPM (R 4.3.0)
## ragg 1.2.7 2023-12-11 [2] RSPM (R 4.3.0)
## randomForest 4.7-1.1 2022-05-23 [2] RSPM (R 4.3.0)
## RANN 2.6.1 2019-01-08 [2] RSPM (R 4.3.0)
## rappdirs 0.3.3 2021-01-31 [2] RSPM (R 4.3.0)
## rcmdcheck 1.4.0 2021-09-27 [2] RSPM (R 4.3.0)
## RColorBrewer 1.1-3 2022-04-03 [2] RSPM (R 4.3.0)
## Rcpp 1.0.12 2024-01-09 [2] RSPM (R 4.3.0)
## RcppAnnoy 0.0.21 2023-07-02 [2] RSPM (R 4.3.0)
## RcppArmadillo 0.12.6.6.1 2023-12-04 [2] RSPM (R 4.3.0)
## RcppEigen 0.3.3.9.4 2023-11-02 [2] RSPM (R 4.3.0)
## RcppHNSW 0.5.0 2023-09-19 [2] RSPM (R 4.3.0)
## RcppML 0.3.7 2021-09-21 [2] RSPM (R 4.3.0)
## RcppProgress 0.4.2 2020-02-06 [2] RSPM (R 4.3.0)
## RcppTOML 0.2.2 2023-01-29 [2] RSPM (R 4.3.0)
## RCurl 1.98-1.14 2024-01-09 [2] RSPM (R 4.3.0)
## readr 2.1.5 2024-01-10 [2] RSPM (R 4.3.0)
## readxl 1.4.3 2023-07-06 [2] RSPM (R 4.3.0)
## registry 0.5-1 2019-03-05 [2] RSPM (R 4.3.0)
## rematch 2.0.0 2023-08-30 [2] RSPM (R 4.3.0)
## rematch2 2.1.2 2020-05-01 [2] RSPM (R 4.3.0)
## remotes 2.4.2.1 2023-07-18 [2] RSPM (R 4.3.0)
## reprex 2.1.0 2024-01-11 [2] RSPM (R 4.3.0)
## reshape2 1.4.4 2020-04-09 [2] RSPM (R 4.3.0)
## ResidualMatrix 1.12.0 2023-10-24 [2] Bioconductor
## restfulr 0.0.15 2022-06-16 [2] RSPM (R 4.3.2)
## reticulate 1.34.0 2023-10-12 [2] RSPM (R 4.3.0)
## rgl 1.2.8 2023-11-29 [2] RSPM (R 4.3.0)
## rhdf5 2.46.1 2023-11-29 [2] Bioconductor 3.18 (R 4.3.2)
## rhdf5filters 1.14.1 2023-11-06 [2] Bioconductor
## Rhdf5lib 1.24.1 2023-12-11 [2] Bioconductor 3.18 (R 4.3.2)
## Rhtslib 2.4.1 2024-01-18 [2] Bioconductor 3.18 (R 4.3.2)
## rjson 0.2.21 2022-01-09 [2] RSPM (R 4.3.0)
## rlang 1.1.3 2024-01-10 [2] RSPM (R 4.3.0)
## rmarkdown 2.25 2023-09-18 [2] RSPM (R 4.3.0)
## rncl 0.8.7 2023-01-08 [2] RSPM (R 4.3.0)
## RNeXML 2.4.11 2023-02-01 [2] RSPM (R 4.3.0)
## rngtools 1.5.2 2021-09-20 [2] RSPM (R 4.3.0)
## ROCR 1.0-11 2020-05-02 [2] RSPM (R 4.3.0)
## roxygen2 7.3.0 2024-01-11 [2] RSPM (R 4.3.0)
## rpart 4.1.21 2023-10-09 [3] CRAN (R 4.3.2)
## rprojroot 2.0.4 2023-11-05 [2] RSPM (R 4.3.0)
## Rsamtools 2.18.0 2023-10-24 [2] Bioconductor
## RSpectra 0.16-1 2022-04-24 [2] RSPM (R 4.3.0)
## RSQLite 2.3.5 2024-01-21 [2] RSPM (R 4.3.0)
## rstudioapi 0.15.0 2023-07-07 [2] RSPM (R 4.3.0)
## rsvd 1.0.5 2021-04-16 [2] RSPM (R 4.3.0)
## rtracklayer 1.62.0 2023-10-24 [2] Bioconductor
## Rtsne 0.17 2023-12-07 [2] RSPM (R 4.3.0)
## rversions 2.1.2 2022-08-31 [2] RSPM (R 4.3.0)
## rvest 1.0.3 2022-08-19 [2] RSPM (R 4.3.0)
## S4Arrays 1.2.0 2023-10-24 [2] Bioconductor
## S4Vectors 0.40.2 2023-11-23 [2] Bioconductor 3.18 (R 4.3.2)
## sass 0.4.8 2023-12-06 [2] RSPM (R 4.3.0)
## ScaledMatrix 1.10.0 2023-10-24 [2] Bioconductor
## scales 1.3.0 2023-11-28 [2] RSPM (R 4.3.0)
## scater 1.30.1 2023-11-16 [2] Bioconductor
## scattermore 1.2 2023-06-12 [2] RSPM (R 4.3.0)
## scDblFinder 1.16.0 2023-10-24 [2] Bioconductor
## scmap 1.24.0 2023-10-24 [2] Bioconductor
## scran 1.30.2 2024-01-22 [2] Bioconductor 3.18 (R 4.3.2)
## scRNAseq 2.16.0 2023-10-26 [2] Bioconductor
## scRNAseqWorkshop 0.98.0 2024-01-23 [1] local
## sctransform 0.4.1 2023-10-19 [2] RSPM (R 4.3.0)
## scuttle 1.12.0 2023-10-24 [2] Bioconductor
## segmented 2.0-1 2023-12-19 [2] RSPM (R 4.3.0)
## selectr 0.4-2 2019-11-20 [2] RSPM (R 4.3.0)
## sessioninfo 1.2.2 2021-12-06 [2] RSPM (R 4.3.0)
## Seurat 5.0.1 2023-11-17 [2] RSPM (R 4.3.0)
## SeuratObject 5.0.1 2023-11-17 [2] RSPM (R 4.3.0)
## shapefiles 0.7.2 2022-08-25 [2] RSPM (R 4.3.0)
## shiny 1.8.0 2023-11-17 [2] RSPM (R 4.3.0)
## SingleCellExperiment 1.24.0 2023-10-24 [2] Bioconductor
## SingleR 2.4.1 2024-01-07 [2] Bioconductor 3.18 (R 4.3.2)
## sitmo 2.0.2 2021-10-13 [2] RSPM (R 4.3.0)
## slingshot 2.10.0 2023-10-24 [2] Bioconductor
## snow 0.4-4 2021-10-27 [2] RSPM (R 4.3.0)
## softImpute 1.4-1 2021-05-09 [2] RSPM (R 4.3.0)
## sourcetools 0.1.7-1 2023-02-01 [2] RSPM (R 4.3.0)
## sp 2.1-2 2023-11-26 [2] RSPM (R 4.3.0)
## spam 2.10-0 2023-10-23 [2] RSPM (R 4.3.0)
## SparseArray 1.2.3 2023-12-25 [2] Bioconductor 3.18 (R 4.3.2)
## sparseMatrixStats 1.14.0 2023-10-24 [2] Bioconductor
## spatial 7.3-17 2023-07-20 [3] CRAN (R 4.3.2)
## spatstat.data 3.0-4 2024-01-15 [2] RSPM (R 4.3.0)
## spatstat.explore 3.2-5 2023-10-22 [2] RSPM (R 4.3.0)
## spatstat.geom 3.2-7 2023-10-20 [2] RSPM (R 4.3.0)
## spatstat.random 3.2-2 2023-11-29 [2] RSPM (R 4.3.0)
## spatstat.sparse 3.0-3 2023-10-24 [2] RSPM (R 4.3.0)
## spatstat.utils 3.0-4 2023-10-24 [2] RSPM (R 4.3.0)
## splines 4.3.2 2023-10-31 [3] local
## statmod 1.5.0 2023-01-06 [2] RSPM (R 4.3.0)
## stats * 4.3.2 2023-10-31 [3] local
## stats4 4.3.2 2023-10-31 [3] local
## stringi 1.8.3 2023-12-11 [2] RSPM (R 4.3.0)
## stringr 1.5.1 2023-11-14 [2] RSPM (R 4.3.0)
## SummarizedExperiment 1.32.0 2023-10-24 [2] Bioconductor
## survival 3.5-7 2023-08-14 [3] CRAN (R 4.3.2)
## sys 3.4.2 2023-05-23 [2] RSPM (R 4.3.0)
## systemfonts 1.0.5 2023-10-09 [2] RSPM (R 4.3.0)
## tcltk 4.3.2 2023-10-31 [3] local
## tensor 1.5 2012-05-05 [2] RSPM (R 4.3.0)
## TENxPBMCData 1.20.0 2023-10-26 [2] Bioconductor
## testthat 3.2.1 2023-12-02 [2] RSPM (R 4.3.0)
## textshaping 0.3.7 2023-10-09 [2] RSPM (R 4.3.0)
## tibble 3.2.1 2023-03-20 [2] RSPM (R 4.3.0)
## tidyr 1.3.0 2023-01-24 [2] RSPM (R 4.3.0)
## tidyselect 1.2.0 2022-10-10 [2] RSPM (R 4.3.0)
## tidySingleCellExperiment 1.12.0 2023-10-24 [2] Bioconductor
## tidyverse 2.0.0 2023-02-22 [2] RSPM (R 4.3.0)
## timechange 0.3.0 2024-01-18 [2] RSPM (R 4.3.0)
## tinytex 0.49 2023-11-22 [2] RSPM (R 4.3.0)
## tools 4.3.2 2023-10-31 [3] local
## tradeSeq 1.16.0 2023-10-24 [2] Bioconductor
## TrajectoryUtils 1.10.0 2023-10-24 [2] Bioconductor
## ttservice 0.4.0 2023-11-01 [2] RSPM (R 4.3.0)
## tzdb 0.4.0 2023-05-12 [2] RSPM (R 4.3.0)
## umap 0.2.10.0 2023-02-01 [2] RSPM (R 4.3.0)
## urlchecker 1.0.1 2021-11-30 [2] RSPM (R 4.3.0)
## usethis 2.2.2 2023-07-06 [2] RSPM (R 4.3.0)
## utf8 1.2.4 2023-10-22 [2] RSPM (R 4.3.0)
## utils * 4.3.2 2023-10-31 [3] local
## uuid 1.2-0 2024-01-14 [2] RSPM (R 4.3.0)
## uwot 0.1.16 2023-06-29 [2] RSPM (R 4.3.0)
## vctrs 0.6.5 2023-12-01 [2] RSPM (R 4.3.0)
## velociraptor 1.12.0 2023-10-24 [2] Bioconductor
## vipor 0.4.7 2023-12-18 [2] RSPM (R 4.3.0)
## viridis 0.6.4 2023-07-22 [2] RSPM (R 4.3.0)
## viridisLite 0.4.2 2023-05-02 [2] RSPM (R 4.3.0)
## vroom 1.6.5 2023-12-05 [2] RSPM (R 4.3.0)
## waldo 0.5.2 2023-11-02 [2] RSPM (R 4.3.0)
## whisker 0.4.1 2022-12-05 [2] RSPM (R 4.3.0)
## withr 3.0.0 2024-01-16 [2] RSPM (R 4.3.0)
## xfun 0.41 2023-11-01 [2] RSPM (R 4.3.0)
## xgboost 1.7.6.1 2023-12-06 [2] RSPM (R 4.3.0)
## XML 3.99-0.16 2023-11-29 [2] RSPM (R 4.3.0)
## xml2 1.3.6 2023-12-04 [2] RSPM (R 4.3.0)
## xopen 1.0.0 2018-09-17 [2] RSPM (R 4.3.0)
## xtable 1.8-4 2019-04-21 [2] RSPM (R 4.3.0)
## XVector 0.42.0 2023-10-24 [2] Bioconductor
## yaml 2.3.8 2023-12-11 [2] RSPM (R 4.3.0)
## zeallot 0.1.0 2018-01-28 [2] RSPM (R 4.3.0)
## zellkonverter 1.12.1 2023-11-14 [2] Bioconductor
## zinbwave 1.24.0 2023-10-24 [2] Bioconductor
## zip 2.3.0 2023-04-17 [2] RSPM (R 4.3.0)
## zlibbioc 1.48.0 2023-10-24 [2] Bioconductor
## zoo 1.8-12 2023-04-13 [2] RSPM (R 4.3.0)
##
## [1] /tmp/RtmpSvAeu0/Rinst1b656332c177
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library
##
## ─ Python configuration ────────────────────────────────────────────────────
## Python is not available
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