get_leading_edge_genes

Get the leading edge genes from GSEA results

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_leading_edge_genes 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 to get results for</dd>
<dt>gsets</dt>
<dd>character A vector of gene set names to get leading edge genes for.</dd>
<dt>num_genes_per</dt>
<dd>numeric The maximum number of leading edge genes to get for
each gene set (default=5)</dd></dl>

Arguments

Get the leading edge genes from GSEA results — get_leading_edge_genes

<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 to get results for</dd>


<dt>gsets</dt>
<dd>character A vector of gene set names to get leading edge genes for.</dd>


<dt>num_genes_per</dt>
<dd>numeric The maximum number of leading edge genes to get for
each gene set (default=5)</dd>

</dl>

get_leading_edge_genes: Get the leading edge genes from GSEA results

Description

Usage

Value

Arguments