Currently most methods take manual strategies to annotate cell types after clustering the single-cell RNA-seq data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from single-cell RNA-seq data, based on a score annotation model combining differentially expressed genes and confidence levels of cell markers in databases. Evaluation on real scRNA-seq datasets that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.
SCSA is maintained by Yinghao Cao [yhcao@ibms.pumc.edu.cn].
Any suggestion is welcome.
- CellMarker database v2 was integrated, the number of marker evidence increased from 48257 to 91969. User can use this version with cmd '-d whole_v2.db' instead.
git clone https://github.com/bioinfo-ibms-pumc/SCSA.git
pip install pandas numpy scipy openpyxl
SCSA.py [-h] -i INPUT [-o OUTPUT] [-d DB] [-s SOURCE] [-c CLUSTER]
[-M MARKERDB] [-f FOLDCHANGE] [-p PVALUE] [-w WEIGHT]
[-g SPECIES] [-k TISSUE] [-m OUTFMT] [-T CELLTYPE]
[-t TARGET] [-E] [-N] [-b] [-l]
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Input file for marker annotation(Only
CSV format supported).
-o OUTPUT, --output OUTPUT
Output file for marker annotation.
-d DB, --db DB Database for annotation. (whole.db)
-s SOURCE, --source SOURCE
Source of marker genes. (cellranger,[seurat],[scanpy],
[scran])
-c CLUSTER, --cluster CLUSTER
Only deal with one cluster of marker genes.
(all,[1],[1,2,3],[...])
-M MARKERDB, --MarkerDB MARKERDB
User-defined marker database in table format with two
columns.First column as Cellname, Second refers to
Genename.
-f FOLDCHANGE, --foldchange FOLDCHANGE
Fold change threshold for marker filtering. (2.0)
-p PVALUE, --pvalue PVALUE
P-value threshold for marker filtering. (0.05)
-w WEIGHT, --weight WEIGHT
Weight threshold for marker filtering from cellranger
v1.0 results. (100)
-g SPECIES, --species SPECIES
Species for annotation. Only used for cellmarker
database. ('Human',['Mouse'])
-k TISSUE, --tissue TISSUE
Tissue for annotation. Only used for cellmarker
database. Multiple tissues should be seperated
by commas.Run '-l' option to see all tissues.
In linux platform:('All',['Bone marrow'],['Bone marrow,Brain,Blood'][...])
In windows platform:("All",["Bone marrow"],["Bone marrow,Brain,Blood"][...])
-m OUTFMT, --outfmt OUTFMT
Output file format for marker annotation. (ms-
excel,[txt])
-T CELLTYPE, --celltype CELLTYPE
Cell type for annotation. (normal,[cancer])
-t TARGET, --target TARGET
Target to annotation class in Database.
(cellmarker,[cancersea])
-E, --Gensymbol Using gene symbol ID instead of ensembl ID in input
file for calculation.
-N, --norefdb Only using user-defined marker database for
annotation.
-b, --noprint Do not print any detail results.
-l, --list_tissue List tissue names in database.
- To annotate a human scRNA-seq sets generated by CellRanger, use the following code
python3 SCSA.py -d whole.db -i cellranger_pbmc_3k.csv -k All -g Human -p 0.01 -f 1.5 -m txt -o sc.txt
- To annotate a human scRNA-seq sets generated by 'FindAllMarkers' function of Seurat(Butler, A., et al. Nature Biotechnology. 2018) with ensemblIDs, use the following code
python3 SCSA.py -d whole.db -s seurat -i seurat_GSE72056.csv -k All -E -g Human -p 0.01 -f 1.5
- To annotate a human scRNA-seq sets generated by Scanpy, use the following code
##### scanpy_pbmc_3k.csv was genearted by following command from anndata object:
### result = adata.uns['rank_genes_groups']
### groups = result['names'].dtype.names
### dat = pd.DataFrame({group + '_' + key[:1]: result[key][group] for group in groups for key in ['names', 'logfoldchanges','scores','pvals']})
### dat.to_csv("scanpy_pbmc_3k.csv")
python3 SCSA.py -d whole.db -i scanpy_pbmc_3k.csv -s scanpy -E -f1.5 -p 0.01 -o result -m txt
- To annotate a human scRNA-seq sets generated by Scran, use the following code
###### scran_pbmc_3k.csv was generated by following command from sce object(due to its pairwise comparisons, we use the mean LFC instead):
### markers <- findMarkers(sce, sce$cluster, pval.type="all")
### res <- data.frame()
### for (i in names(markers)){
### predata <- subset(markers[[i]],select=c(p.value,FDR))
### meandata <- as.matrix(apply(subset(markers[[i]],select=-c(p.value,FDR)),1,mean))
### if (length(res) == 0){
### colnames(meandata) <- paste("LFC",i,sep="_")
### colnames(predata) <- paste(names(predata),i,sep="_")
### res <- cbind(predata,meandata)
### }else{
### predata <- predata[rownames(res),]
### meandata <- as.matrix(meandata[rownames(res),])
### colnames(meandata) <- paste("LFC",i,sep="_")
### colnames(predata) <- paste(names(predata),i,sep="_")
### res <- cbind(res,predata,meandata)
### }
### }
### write.csv(res,file="~/software/SCSA/new_scran_pbmc_3k.csv",quote=FALSE)
python SCSA.py -d whole.db -s scran -i scran_pbmc_3k.csv -k All -g Human -p 0.05 -f 1.1 -b
- To annotate a human scRNA-seq sets generated by 'FindAllMarkers' function of Seurat(Butler, A., et al. Nature Biotechnology. 2018) with both user-defined database and CellMarker database, use the following code
python3 SCSA.py -d whole.db -i seurat_GSE72056.csv -s seurat -E -f1.5 -p 0.01 -o result -m txt -M user.table
- To annotate a human scRNA-seq sets generated by CellRanger only with user-defined database without any detail print, use the following code
python3 SCSA.py -d whole.db -i cellranger_pbmc_3k.csv -f1.5 -p 0.01 -m txt -M user.table -N -b
- To annotate cluster1 of mouse scRNA-seq sets and To annotate cluster1 of mouse scRNA-seq sets generated by CellRanger, use the following code
python3 SCSA.py -d whole.db -s seurat -i seurat_mouse.csv -k All -E -g Mouse -p 0.01 -f 1 -m txt -o testout -c 1
- To list tissue names in the SCSA annotation database, use the following code
python3 SCSA.py -i none -d whole.db -l
The output information from stdout consists of five parts: "#Cluster","Type","Celltype","Score","Times"
“#Cluster” : The cluster id from input file.
“Type” : A subjective symbol for the prediction results.
“Good” means one of the following conditions:
1.Only one celltype found
2.The score of the first predicted celltype is more than twice as much
as the second predicted celltype.
3.The score of the second predicted celltype is a minus.
“?” means the score of the first predicted celltype is less than twice as much
as the second predicted celltype.
“E” means no celltype found.
“Celltype”: The predicted celltype name.
“Score” : The predicted score for a celltype normalized by Z-score method.
“nan” will be assigned if only one celltype found.
“Times” : The score of the first predicted celltype / The score of the second predicted celltype
If you use SCSA for your research, please kindly cite the following paper:
Cao Y, Wang X and Peng G (2020) SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data. Front. Genet. 11:490. doi: https://doi.org/10.3389/fgene.2020.00490