108 lines
3.7 KiB
Markdown
108 lines
3.7 KiB
Markdown
# FastCAR
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FastCAR is an R package to remove ambient RNA from cells in droplet based single cell RNA sequencing data.
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## Getting Started
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These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
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### Prerequisites
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What things you need to install the software and how to install them
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```
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Give examples
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```
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### Installing
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FastCAR can be install from git with the following command.
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```
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devtools::install_git("https://git.web.rug.nl/P278949/FastCAR")
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```
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Running FastCAR is quite simple.
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First load the library and dependencies.
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```
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library(Matrix)
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library(Seurat)
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library(qlcMatrix)
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library(FastCAR)
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```
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Specify the locations of the expression matrices
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```
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cellExpressionFolder = c("Cellranger_output/sample1/filtered_feature_bc_matrix/")
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fullMatrixFolder = c("Cellranger_output/sample1/raw_feature_bc_matrix/")
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```
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Set a location for storing the corrected cell/gene matrix
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```
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correctedMatrixFolder = c("Cellranger_output/sample1/corrected_feature_bc_matrix")
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```
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Load both the cell matrix and the full matrix
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```
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cellMatrix = read.cell.matrix(cellExpressionFolder)
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fullMatrix = read.full.matrix(fullMatrixFolder)
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```
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The following functions give an idea of the effect that different settings have on the ambient RNA profile.
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These are optional as they do take a few minutes and the default settings work fine
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Plotting the number of empty droplets, the number of genes identified in the ambient RNA, and the number of genes that will be corrected for at different UMI cutoffs,
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```
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ambProfile = describe.ambient.RNA.sequence(fullCellMatrix = fullMatrix,
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start = 10,
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stop = 500,
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by = 10,
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contaminationChanceCutoff = 0.05)
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plot.ambient.profile(ambProfile)
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```
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Set the empty droplet cutoff and the contamination chance cutoff
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The empty droplet cutoff is the number of UMIs a droplet can contain at the most to be considered empty.
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100 works fine but we tested this method in only one tissue. For other tissues this might not be the.
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Increasing this number also increases the highest possible value of expression of a given gene.
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As the correction will remove this value from every cell it is adviced not to set this too high and thereby overcorrect the expression in lowly expressing cells.
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The contamination chance cutoff is the allowed probability of a gene contaminating a cell.
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As we developed FastCAR specifically for differential expression analyses between groups we suggest setting this such that not enough cells could be contaminated to affect this.
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In a cluster of a thousand cells divided into two groups there would be 2-3 cells per group with ambient RNA contamination of any given gene.
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Such low cell numbers are disregarded for differential expression analyses.
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```
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emptyDropletCutoff = 100
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contaminationChanceCutoff = 0.05
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```
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Determine the ambient RNA profile and remove the ambient RNA from each cell
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```
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ambientProfile = determine.background.to.remove(fullMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff)
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cellMatrix = remove.background(cellMatrix, ambientProfile)
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```
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Finally write the corrected cell/gene matrix to a file, this matrix can be used in Seurat the same way as any other cell/gene matrix.
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```
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write.corrected.matrix(cellMatrix, correctedMatrixFolder, ambientProfile)
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```
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## Authors
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* **Marijn Berg** - m.berg@umcg.nl
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## License
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This project is licensed under the GPL-3 License - see the [LICENSE.md](LICENSE.md) file for details
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