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: 2026-06-23 07:56:26 UTC; root
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>
Config/pak/sysreqs: cmake libglpk-dev make libmagick++-dev gsfonts
        libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev python3
        zlib1g-dev
Repository: https://dsokolo.r-universe.dev
Date/Publication: 2025-06-25 17:10:02 UTC
RemoteUrl: https://github.com/cran/scMappR
RemoteRef: HEAD
RemoteSha: bb8ac09ba4f5500f4f29bffc40fa11bcd06444a9