run_hypergeometric_gsea

Compute enriched gene sets among significant genes in a cell type for
a factor using hypergeometric test

Single-cell Interpretable Tensor Decomposition (scITD) employs the
Tucker tensor decomposition to extract multicell-type gene expression patterns
that vary across donors/individuals. This tool is geared for use with single-cell
RNA-sequencing datasets consisting of many source donors. The method has a wide
range of potential applications, including the study of inter-individual variation
at the population-level, patient sub-grouping/stratification, and the analysis
of sample-level batch effects. Each "multicellular process" that is extracted
consists of (A) a multi cell type gene loadings matrix and (B) a
corresponding donor scores vector indicating the level at which the corresponding
loadings matrix is expressed in each donor. Additional methods are implemented
to aid in selecting an appropriate number of factors and to evaluate stability
of the decomposition. Additional tools are provided for downstream analysis,
including integration of gene set enrichment analysis and ligand-receptor analysis.
Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.

Jonathan Mitchel

scITD

Single-Cell Interpretable Tensor Decomposition

Evan Biederstedt

Peter Kharchenko

run_hypergeometric_gsea function

<dl><dt>container</dt>
<dd>environment Project container that stores sub-containers
for each cell type as well as results and plots from all analyses</dd>
<dt>factor_select</dt>
<dd>numeric The factor of interest</dd>
<dt>ctype</dt>
<dd>character The cell type of interest</dd>
<dt>up_down</dt>
<dd>character Either "up" to compute enrichment among the significant
positive loading genes or "down" to compute enrichment among the significant
negative loading genes.</dd>
<dt>thresh</dt>
<dd>numeric Pvalue significance threshold. Used as cutoff for calling
genes as significant to use for enrichment tests. (default=0.05)</dd>
<dt>min_gs_size</dt>
<dd>numeric Minimum gene set size (default=15)</dd>
<dt>max_gs_size</dt>
<dd>numeric Maximum gene set size (default=500)</dd>
<dt>db_use</dt>
<dd>character The database of gene sets to use. Database
options include "GO", "Reactome", "KEGG", and "BioCarta". More than
one database can be used. (default="GO")</dd></dl>

Arguments

Compute enriched gene sets among significant genes in a cell type for
a factor using hypergeometric test — run_hypergeometric_gsea

<dl>

<dt>container</dt>
<dd>environment Project container that stores sub-containers
for each cell type as well as results and plots from all analyses</dd>


<dt>factor_select</dt>
<dd>numeric The factor of interest</dd>


<dt>ctype</dt>
<dd>character The cell type of interest</dd>


<dt>up_down</dt>
<dd>character Either "up" to compute enrichment among the significant
positive loading genes or "down" to compute enrichment among the significant
negative loading genes.</dd>


<dt>thresh</dt>
<dd>numeric Pvalue significance threshold. Used as cutoff for calling
genes as significant to use for enrichment tests. (default=0.05)</dd>


<dt>min_gs_size</dt>
<dd>numeric Minimum gene set size (default=15)</dd>


<dt>max_gs_size</dt>
<dd>numeric Maximum gene set size (default=500)</dd>


<dt>db_use</dt>
<dd>character The database of gene sets to use. Database
options include "GO", "Reactome", "KEGG", and "BioCarta". More than
one database can be used. (default="GO")</dd>

</dl>

run_hypergeometric_gsea: Compute enriched gene sets among significant genes in a cell type for a factor using hypergeometric test

Description

Usage

Value

Arguments