get_module_enr

Identify gene sets that are enriched within specified gene co-regulatory modules.
Uses a hypergeometric test for over-representation. Used in plot_multi_module_enr().

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

get_module_enr 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>ctype</dt>
<dd>character The name of cell type for the cell type module to test</dd>
<dt>mod_select</dt>
<dd>numeric The module number for the cell type module to test</dd>
<dt>db_use</dt>
<dd>character The database of gene sets to use. Database
options include "GO", "Reactome", "KEGG", "BioCarta", "Hallmark", "TF", and
"immuno". More than one database can be used. (default="GO")</dd>
<dt>adjust_pval</dt>
<dd>logical Set to TRUE to apply FDR correction (default=TRUE)</dd></dl>

Arguments

Identify gene sets that are enriched within specified gene co-regulatory modules.
Uses a hypergeometric test for over-representation. Used in plot_multi_module_enr(). — get_module_enr

<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>ctype</dt>
<dd>character The name of cell type for the cell type module to test</dd>


<dt>mod_select</dt>
<dd>numeric The module number for the cell type module to test</dd>


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


<dt>adjust_pval</dt>
<dd>logical Set to TRUE to apply FDR correction (default=TRUE)</dd>

</dl>

Identify gene sets that are enriched within specified gene co-regulatory modules.
Uses a hypergeometric test for over-representation. Used in plot_multi_module_enr().

get_module_enr: Identify gene sets that are enriched within specified gene co-regulatory modules. Uses a hypergeometric test for over-representation. Used in plot_multi_module_enr().

Description

Usage

Value

Arguments