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genesorteR: Feature Ranking for Single Cell Data

genesorteR is an R package for single cell data analysis. It calculates a specificity score to rank all genes in each cell cluster. It can then use this ranking to find sets of marker genes or to find highly variable or differentially expressed genes. genesorteR is applicable to scRNA-Seq data as well as other sparse single cell data like scATAC-Seq

genesorteR is relatively quick, just seconds for 100k cells, few minutes for millions of cells. Read more in genesorteR's pre-print.

If you have questions or need help running genesorteR please email us at this email, we will be happy to help you. For bugs or feature requests, please post here.

genesorteR was developed at the RWTH Aachen University Hospital.

What genesorteR Can Do

  • Rank genes (scRNA-Seq) or open chromatin regions (snATAC-Seq) by "specificity" in cell clusters
  • Find small sets of marker genes and differentially expressed / highly variable genes or accessible regions
  • Cluster genes/open chromatin regions and make heatmap summaries of single cell data

Cited by...

Install genesorteR

#install devtools package from CRAN
install.packages("devtools") 

#install genesorteR from the Github repository
devtools::install_github("mahmoudibrahim/genesorteR") 

genesorteR Documentation

Here is the PDF manual for genesorteR.

You can of course also access the documentation of each function like so ?sortGenes.

Wiki (Tutorials & FAQs)

more Wiki pages coming soon...

Quick Tutorial

library(genesorteR)

data(kidneyTabulaMuris) #three cell types from kidney (Tabula Muris data)

#get specificity scores for each cell type
sg = sortGenes(kidneyTabulaMuris$exp, kidneyTabulaMuris$cellType)

head(sg$specScore) #specificity scores for each gene in each cluster

#define a small set of markers
mm = getMarkers(sg, quant = 0.99)

#cluster genes and make a heatmap
pp = plotMarkerHeat(sg$inputMat, sg$inputClass, mm$markers, clusterGenes=TRUE, outs = TRUE)

pp$gene_class_info #gene clusters

Check this post for more info. Also check out the examples in each function's documentation. Vignettes coming soon.

Note that genesorteR does not currently accept expression matrices with negative entries.

Fits with Seurat?

Yes it does!

#if "seuratObject" is the Seurat object that contains your data, I think this should work:
gs = sortGenes(seuratObject@assays$RNA@data, Idents(seuratObject))

Hint: some real sorting here (click the picture!)...

IMAGE ALT TEXT HERE

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Feature Ranking in (possibly large) Clustered Single Cell Data

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