Package: scMappR
Title: Single Cell Mapper
Version: 1.0.12
Authors@R: 
    c(person(given = "Dustin", family = "Sokolowski", role = c("aut", "cre"), email = "djsokolowski95@gmail.com"), person(given = "Mariela", family = "Faykoo-Martinez", role = "aut", email = "mfaykoomartinez@gmail.com"), person(given = "Lauren", family = "Erdman", role = "aut", email = "lauren.erdman@sickkids.ca"), person(given = "Houyun", family = "Hou", role = "aut", email = "huayunhou@gmail.com"),  person(given = "Cadia", family = "Chan", role = "aut", email = "cadia.chan@mail.utoronto.ca"), person(given = "Helen", family = "Zhu", role = "aut", email = "helen.m.zhu@gmail.com"), person(given = "Melissa", family = "Holmes", role = "aut", email = "melissa.holmes@utoronto.ca"), person(given = "Anna", family = "Goldenberg", role = "aut", email = "nyulik@gmail.com"), person(given = "Michael", family = "Wilson", role = "aut", email = "michael.wilson@sickkids.ca"))
Description: 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>.
Depends: R (>= 4.0.0)
Imports: ggplot2, pheatmap, graphics, Seurat, GSVA, stats, utils,
        downloader, pcaMethods, grDevices, gProfileR, limSolve,
        gprofiler2, pbapply, ADAPTS, reshape,
License: GPL-3
URL:
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Packaged: 2025-06-25 15:02:56 UTC; dsokolowski
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Author: Dustin Sokolowski [aut, cre],
  Mariela Faykoo-Martinez [aut],
  Lauren Erdman [aut],
  Houyun Hou [aut],
  Cadia Chan [aut],
  Helen Zhu [aut],
  Melissa Holmes [aut],
  Anna Goldenberg [aut],
  Michael Wilson [aut]
Maintainer: Dustin Sokolowski <djsokolowski95@gmail.com>
Repository: CRAN
Date/Publication: 2025-06-25 17:10:02 UTC
Built: R 4.6.0; ; 2026-04-26 21:28:45 UTC; unix
