Rscript anno-celltype.R <input> <outdir> <project> [options]


scRNA-seq Celltype Annotation using SingleR, scCATCH, CellMarker

positional arguments:
  input            input seurat rds file
  outdir           output result folder
  project          project name

flags:
  -h, --help       show this help message and exit

optional arguments:
  -m, --method     SingleR, scCATCH, CellMarker, SelfMarker [default: SingleR]
  -s, --species    Human or Mouse [default: Human]
  -r, --reference  set when using SingleR, 
                   HumanPrimaryCellAtlasData(microarray datasets derived from human primary cells and also contains pathogenic conditions)、
                   BlueprintEncodeData (quick intepretation for immune cell and not contain finer monocyte and dendritic cell subtypes)、
                   DatabaseImmuneCellExpressionData(CD4+ T cell subsets)、 
                   NovershternHematopoieticData (greatest resolution for myeloid and progenitor cells)、 
                   MonacoImmuneData (best coverages for a typical PBMC sample) 

                   MouseRNAseqData (mouse, best suited to bulk tissue samples from brain, blood, or heart)、 
                   ImmGen(mouse immune cells with exhaustive coverage)、
                  [default: combined]

  -l, --level      set when using SingleR, 
                   main (broad), fine (fine-grained), ont (standard in Cell Ontology)
                   [default: main]
  -t, --tissue     tissue name, set when using scCATCH or CellMarker
  --minpct         set when using scCATCH, 
                   Include the gene detected in at least this many cells in each cluster 
                   [default: 0.25]
  --logfc          set when using scCATCH, 
                   Include the gene with at least this fold change of average gene expression compared to every other clusters 
                   [default: 0.25]
  -p, --pvalue     set when using scCATCH, 
                   Include the significantly highly expressed gene from wilcox test compared to every other clusters
                   [default: 0.05]
  --plot           features to plot on umap 
                   [default: (nFeature_RNA,percent.mt,percent.rb)]
  --markerfile     set when using SelfMarker, local marker xlsx files, including cell_name,marker_genes

 # SingleR
 Rscript anno-celltype.R examples/two-samples.rds ~/tests/annotation test --method SingleR --species Human --reference MonacoImmuneData --level main

 # scCATCH
 Rscript anno-celltype.R examples/two-samples.rds ~/tests/annotation test --method scCATCH --species Human --tissue Blood --minpct 0.25 --logfc 0.25 --pvalue 
 0.05
 
 # CellMarker
 Rscript anno-celltype.R examples/two-samples.rds ~/tests/annotation test --method CellMarker --species Human --tissue Blood

 # SelfMarker
 Rscript anno-celltype.R examples/two-samples.rds ~/tests/annotation test --method SelfMarker --species Human --tissue Blood --markerfile examples/self_markers.xlsx

annotation
├── test.celltype.SingleR.dataset=MonacoImmuneData.barcodes.detail.tsv       # barcode annotation statistics
├── test.celltype.SingleR.dataset=MonacoImmuneData.clusters.detail.tsv       # cluster annotation statistics
├── test.celltype.SingleR.dataset=MonacoImmuneData.pdf                       # annotation figures
├── test.celltype.SingleR.dataset=MonacoImmuneData.rds                       # annotation results