PBMC_example

Toy example of data where cell-weighted fold-changes and related downsteam analyses can be completed.

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

PBMC_example function

A list containing three data frames, normalized count data, a signature matrix, and a list of differentially expressed genes.<dl>
 <dt>bulk_normalized</dt>
<dd>A 3231 x 9 matrix where rows are genes, columns are samples, and the matrix is filled with CPM normalized counts.</dd> <dt>odds_ratio_in</dt>
<dd>A 2336 x 7 matrix where rows are genes, columns are cell-types and matrix is filled with the odds-ratio that a gene is in each cell-type.</dd> <dt>bulk_DE_cors</dt>
<dd>A 59 x 3 matrix of sex-specific genes found between male and female PBMC samples (female biased = upregulated). row and rownames are genes, columns are gene name, FDR adjusted p-value, and log2 fold-change. DEGs were computed with DESeq2 and genes with a log2FC &gt; 1 were kept.</dd>
</dl>

Format

PBMC_scMappR — PBMC_example

A list containing three data frames, normalized count data, a signature matrix, and a list of differentially expressed genes.<dl>
 <dt>bulk_normalized</dt>
<dd>A 3231 x 9 matrix where rows are genes, columns are samples, and the matrix is filled with CPM normalized counts.</dd>

 <dt>odds_ratio_in</dt>
<dd>A 2336 x 7 matrix where rows are genes, columns are cell-types and matrix is filled with the odds-ratio that a gene is in each cell-type.</dd>

 <dt>bulk_DE_cors</dt>
<dd>A 59 x 3 matrix of sex-specific genes found between male and female PBMC samples (female biased = upregulated). row and rownames are genes, columns are gene name, FDR adjusted p-value, and log2 fold-change. DEGs were computed with DESeq2 and genes with a log2FC &gt; 1 were kept.</dd>


</dl>

PBMC_scMappR

PBMC_example: PBMC_scMappR

Description

Usage

Arguments

Format

Details

Examples