In FerrenaAlexander/FerrenaSCRNAseq: Package for QC, processing and analysis of scRNAseq data

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FerrenaSCRNAseq

The goal of FerrenaSCRNAseq is to perform QC, processing, and analysis of scRNAseq data.

Installation

You can install from GitHub with:

# install.packages("devtools")
devtools::install_github("FerrenaAlexander/FerrenaSCRNAseq")

Usage

This package works with Seurat objects.

The idea is to automatically filter out poor quality cells, defined as cells with high mito content, low UMI, low "complexity" (lower genes than expected given nUMI)

I suggest you pre-process with Seurat's SingleCellTransform (SCT) pipeline and run clustering on un-filtered data. This allows cell-type specific QC filtering. Sometimes, mito conent, number of UMI or number of genes is actually a function of cell type. So global cutoffs suffer from lack of both specificity and sensitivity, by throwing out cells that are actually good, and keeping cells that are actually bad. So adjusting for cell type is pretty important.

It works by multivariable linear models which allow for "outlier diagnostics".

After filtering, you should re-process the data.

See ?FerrenaSCRNAseq::automatedfiltering()

### read in and pre-process
#read in object
rawh5 = '/path/to/h5/file/from/cellranger'
samp = 'sample_label'
sobj <- CreateSeuratObject(   Read10X_h5(rawh5), min.cells= 3, project = samp)

#mito content, add to metadata
mito.features <- grep(pattern = "^mt-", x = rownames(x = sobj), value = TRUE, ignore.case = T)
sobj[["percent.mito"]] <- Seurat::PercentageFeatureSet(sobj, features = mito.features)


#normalize and cluster
suppressWarnings(sobj <- Seurat::SCTransform(sobj, verbose = T))

sobj <- Seurat::RunPCA(object = sobj, verbose = F)

sobj <- Seurat::FindNeighbors(object = sobj, dims = 1:20, verbose = F)
sobj <- Seurat::FindClusters(object = sobj, resolution = 0.1, verbose = F, algorithm = 4)

sobj <- RunUMAP(sobj, dims = 1:20)


### run auto filter ###

# sobj is a suerat object
# clusters refers to a column in the [e-mail] - here, we use the clsutering computed above.
# iterative mito filter (cell-wise) is not as good as identifying and removing the mito cluster.
# see ?FerrenaSCRNAseq::automatedfiltering

reportlist <- FerrenaSCRNAseq::automatedfiltering(sobj, clusters = 'SCT_snn_res.0.1',
iterativefilter.mito = F)



#add autofilter results to metadata
autofilterres <- reportlist[[1]]
sobj$filteredout <- autofilterres$filteredout
sobj$filterreason <- autofilterres$filterreason


#make an output dir for the report
outputdir = '.'
dir.create( paste0(outputdir) )
dir.create( paste0(outputdir, '/qc') )

#plot autofilter results
pdf(  paste0(outputdir, '/qc/autofilter.pdf'), 7,7)
print( DimPlot(sobj, label = T) )


print( FeaturePlot(sobj, c('nCount_RNA', 'nFeature_RNA', 'percent.mito'), order = T) + 
DimPlot(sobj, group.by = 'filteredout')
)

print(reportlist)
dev.off()

#save autofilter output
saveRDS( reportlist, paste0(outputdir, '/qc/reportlist-autofilter.rds') )

#filter
goodcells <- autofilterres[autofilterres$filteredout == 'No', 'barcodes']
sobj <- sobj[,goodcells]

#reprocess
#normalize and cluster
suppressWarnings(sobj <- Seurat::SCTransform(sobj, verbose = T))

sobj <- Seurat::RunPCA(object = sobj, verbose = F)

sobj <- Seurat::FindNeighbors(object = sobj, dims = 1:20, verbose = F)
sobj <- Seurat::FindClusters(object = sobj, resolution = 0.1, verbose = F, algorithm = 4)

sobj <- RunUMAP(sobj, dims = 1:20)

DoubletFinder Wrapper for automated doublet calling

I use DoubletFinder a lot, so I added a wrapper of DoubletFinder

• DoubletFinder paper: https://www.sciencedirect.com/science/article/pii/S2405471219300730

• DoubletFinder github https://github.com/chris-mcginnis-ucsf/DoubletFinder

It assumes processing with SCT. It also uses an estimated doublet rate from 10X genomics,

see ?FerrenaSCRNAseq::dratedf and (https://kb.10xgenomics.com/hc/en-us/articles/360001378811-What-is-the-maximum-number-of-cells-that-can-be-profiled-)

This table is accurate as of 2022 Feb 09.

#use doublet filtering
dfdf <- FerrenaSCRNAseq::doubletfinderwrapper(sobj, clusters = 'SCT_snn_res.0.1')

FerrenaAlexander/FerrenaSCRNAseq documentation built on March 10, 2023, 9:31 a.m.