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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Single Cell Mapper</dc:title>
  <dc:title>R package scMappR version 1.0.12</dc:title>
  <dc: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. &lt;doi:10.1093/nargab/lqab011&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.0.0)</dc:relation>
  <dc:relation>Imports: ggplot2, pheatmap, graphics, Seurat, GSVA, stats, utils,
downloader, pcaMethods, grDevices, gProfileR, limSolve,
gprofiler2, pbapply, ADAPTS, reshape,</dc:relation>
  <dc:relation>Suggests: testthat, knitr, rmarkdown</dc:relation>
  <dc:creator>Dustin Sokolowski &lt;djsokolowski95@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>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]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2025-06-25</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=scMappR</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.scMappR</dc:identifier>
</oai_dc:dc>
