Single Cell Mapper
Fisher's Exact Cell-Type Identification.
Identify co-expressed cell-types
compare_deconvolution_methods
Measure cell-type specificity of cell-weighted Fold-changes
DeconRNASeq CRAN compatible
Generate cell weighted Fold-Changes (cwFold-changes)
Extract Markers
Generate signature matrix
Internal -- get gene symbol from Panglao.db assigned gene-names (symbo...
Get signature matrices.
Pathway enrichment for cwFold-changes
Cell-type naming with GSVA
Generate Heatmap
Consensus cell-type naming (Fisher's Exact)
Plot g:profileR Barplot (TF)
Internal - Pathway enrichment for cellWeighted_Foldchanges and bulk ge...
Plot gProfileR Barplot
Count Matrix To Signature Matrix
Count Matrix To Seurat Object
Generate cellWeighted_Foldchanges, visualize, and enrich.
Identify all cell-type markers
Single cell-type gene preferences
tissue_by_celltype_enrichment
Gene List Visualization and Enrichment with Custom Signature Matrix
Gene List Visualization and Enrichment (Internal)
To Character.
To Numeric.
Extract Top Markers
two_method_pathway_enrichment
The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.