extract_genes_cell

Extracting cell-type markers from a signature matrix.

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

extract_genes_cell function

<dl><dt>geneHeat</dt>
<dd>The heatmap of ranks from your scRNA-seq dataset with your genes subsetted.</dd>
<dt>cellTypes</dt>
<dd>The cell-types that you're interested in extracting. They need to be colnames (not case sensitive).</dd>
<dt>val</dt>
<dd>How associated a gene is with a particular cell type to include in your list - default is slightly associated.</dd>
<dt>isMax</dt>
<dd>If you are taking the single best CT marker (T/F) -- TRUE not recommended.</dd>
<dt>isPvalue</dt>
<dd>If the signature matrix is raw p-value (T/F) -- TRUE not recommended.</dd></dl>

Arguments

Extract Markers — extract_genes_cell

<dl>

<dt>geneHeat</dt>
<dd>The heatmap of ranks from your scRNA-seq dataset with your genes subsetted.</dd>


<dt>cellTypes</dt>
<dd>The cell-types that you're interested in extracting. They need to be colnames (not case sensitive).</dd>


<dt>val</dt>
<dd>How associated a gene is with a particular cell type to include in your list - default is slightly associated.</dd>


<dt>isMax</dt>
<dd>If you are taking the single best CT marker (T/F) -- TRUE not recommended.</dd>


<dt>isPvalue</dt>
<dd>If the signature matrix is raw p-value (T/F) -- TRUE not recommended.</dd>

</dl>

Extract Markers

extract_genes_cell: Extract Markers

Description

Usage

Value

Arguments

Details

Examples