datasets

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>.

Dustin Sokolowski

scMappR

Single Cell Mapper

Mariela Faykoo-Martinez

Lauren Erdman

Houyun Hou

Cadia Chan

Helen Zhu

Melissa Holmes

Anna Goldenberg

Michael Wilson

gmt function

A list with 5 character vectors, each containing genes.<dl>
 <dt>Astrocytes_panglao</dt>
<dd>astrocyte markers identified by panglao</dd> <dt>Schwann_panglao</dt>
<dd>Schwann markers identified by panglao</dd> <dt>Bergmann glia_panglao</dt>
<dd>Bergmann glia markers identified by panglao</dd> <dt>Kupffer_panglao</dt>
<dd>Kupffer markers identified by panglao</dd> <dt>Oligodendrocyte progenitor_panglao</dt>
<dd>Oligodendrocyte progenitor markers identified by panglao</dd>
</dl>

Format

gmt_example — gmt

A list with 5 character vectors, each containing genes.<dl>
 <dt>Astrocytes_panglao</dt>
<dd>astrocyte markers identified by panglao</dd>

 <dt>Schwann_panglao</dt>
<dd>Schwann markers identified by panglao</dd>

 <dt>Bergmann glia_panglao</dt>
<dd>Bergmann glia markers identified by panglao</dd>

 <dt>Kupffer_panglao</dt>
<dd>Kupffer markers identified by panglao</dd>

 <dt>Oligodendrocyte progenitor_panglao</dt>
<dd>Oligodendrocyte progenitor markers identified by panglao</dd>


</dl>

gmt_example

gmt: gmt_example

Description

Usage

Arguments

Format

Details

Examples