scMappR1.0.11 package

Single Cell Mapper

cellmarker_enrich

Fisher's Exact Cell-Type Identification.

coEnrich

Identify co-expressed cell-types

compare_deconvolution_methods

compare_deconvolution_methods

cwFoldChange_evaluate

Measure cell-type specificity of cell-weighted Fold-changes

DeconRNAseq_CRAN

DeconRNASeq CRAN compatible

deconvolute_and_contextualize

Generate cell weighted Fold-Changes (cwFold-changes)

extract_genes_cell

Extract Markers

generes_to_heatmap

Generate signature matrix

get_gene_symbol

Internal -- get gene symbol from Panglao.db assigned gene-names (symbo...

get_signature_matrices

Get signature matrices.

gProfiler_cellWeighted_Foldchange

Pathway enrichment for cwFold-changes

gsva_cellIdentify

Cell-type naming with GSVA

heatmap_generation

Generate Heatmap

human_mouse_ct_marker_enrich

Consensus cell-type naming (Fisher's Exact)

make_TF_barplot

Plot g:profileR Barplot (TF)

pathway_enrich_internal

Internal - Pathway enrichment for cellWeighted_Foldchanges and bulk ge...

plotBP

Plot gProfileR Barplot

process_dgTMatrix_lists

Count Matrix To Signature Matrix

process_from_count

Count Matrix To Seurat Object

scMappR_and_pathway_analysis

Generate cellWeighted_Foldchanges, visualize, and enrich.

seurat_to_generes

Identify all cell-type markers

single_gene_preferences

Single cell-type gene preferences

tissue_by_celltype_enrichment

tissue_by_celltype_enrichment

tissue_scMappR_custom

Gene List Visualization and Enrichment with Custom Signature Matrix

tissue_scMappR_internal

Gene List Visualization and Enrichment (Internal)

tochr

To Character.

toNum

To Numeric.

topgenes_extract

Extract Top Markers

two_method_pathway_enrichment

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>.

  • Maintainer: Dustin Sokolowski
  • License: GPL-3
  • Last published: 2023-06-30