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Open RStudio (you can type it in the Windows search bar)
Create a new R script: ‘File’ → “New File” → “R script”
Save this script where your samples folders are (‘File’ → ‘Save’). These should be on your H or W drive. Save the script file as ‘scrnaseq.R’
In the following sections you will be copying and running the R code into your scrnaseq.R script.
Cell Ranger (and nfcore/scrnaseq) generates a default folder and file output structure. There will be a main folder that contains all the sample subfolders (NOTE: this is where you must save your R script). Each sample folder will have an ‘outs’ subfolder. This ‘outs’ folder contains a ‘filtered_feature_bc_matrix’ folder, which contains the files that Seurat uses in its analysis.
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You can manually set your working directory in RStudio by selecting ‘Session' -> 'Set working directory' -> 'Choose directory'. Choose the same directory as you saved your scrnaseq.R script, previous section. This will output the setwd(...) command with your working directory into the console window (bottom left panel). Copy this command to replace the default setwd(...) line in your R script.
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3a. Select a sample to work with and import the data into R
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#TO DO: Make sure the folder structure provided follows the Cell ranger output format.
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├── filtered_feature_bc_matrix
│ ├── barcodes.tsv.gz
│ ├── features.tsv.gz
│ └── matrix.mtx.gz |
Clarify how the files will be output from the upstream Nextflow pipeline and update text accordingly to guide to the output folder.
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mat <- Read10X(data.dir = "H:/nfcore-scrnaseq_R_analysis/test_data/Choroid/filtered_feature_bc_matrix" |
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In this section we’ll choose one of our samples to analyse, then import that sample’s scRNA-Seq dataset into R. You can re-run this, and following sections, for each sample dataset that you have. Just replace the sample name in sample <- "xxxxxx" below.
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