https://github.com/helenalc/bc2_2019-workshop_multi-scrna-seq
https://github.com/helenalc/bc2_2019-workshop_multi-scrna-seq
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/helenalc/bc2_2019-workshop_multi-scrna-seq
- Owner: HelenaLC
- Created: 2019-08-30T07:14:10.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-09-09T06:59:53.000Z (almost 7 years ago)
- Last Synced: 2025-08-12T09:31:38.065Z (10 months ago)
- Language: HTML
- Size: 3.12 MB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# BC2_2019-workshop_multi-scRNA-seq
### Software installation
For this tutorial, you will need R 3.6, which you can download from [https://stat.ethz.ch/CRAN/](https://stat.ethz.ch/CRAN/).
We will, however, need to access the *devel* versions of various Bioconductor packages and with R 3.6, you can follow the code below (submitted to the terminal) to get a set of packages that should be compatible. First, tell the `BiocManager` package that *devel* should be used:
```
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(version = "devel") # will get prompted
```
For the steps below, you should be able to cut-and-paste the commands into an active session (and wait for packages and dependencies to be installed):
```
BiocManager::install("remotes")
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")
BiocManager::install("HelenaLC/muscat")
pkgs <- c("M3C","Seurat","UpSetR","cowplot",
"msigdbr","org.Mm.eg.db","pheatmap",
"scds","scran","topGO","ExperimentHub")
BiocManager::install(pkgs)
BiocManager::install("plger/scDblFinder")
```
### Data downloads
For the pratical part of the workshop, we will be analyzing a replicated two-condition dataset of brain cortex tissue from mice treated with lipopolysaccharide (LPS), which is available [HERE](http://imlspenticton.uzh.ch/teaching/BC2_2019-workshop_multi-scRNA-seq).
* **1-SCE_reduced.rds**: `SingleCellExperiment` (SCE) subset of the LPS dataset from Crowell *et al.*[1](#f1)
* **2-SO_integrated.rds**: `SeuratObject` obtained after preprocessing & integration
* **3-SCE_clustered.rds**: final clustered & annotated SCE for downstream analyses
[1]:
Crowell HL, Soneson C\*, Germain P-L\*,
Calini D, Collin L, Raposo C, Malhotra D & Robinson MD:
On the discovery of population-specific state transitions from
multi-sample multi-condition single-cell RNA sequencing data.
*bioRxiv* **713412** (July, 2019). doi: [10.1101/713412](https://doi.org/10.1101/713412)