count_word

count_word. From older version of simplifyEnrichment package.

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

count_word function

<dl><dt>term</dt>
<dd>A vector of description texts.</dd>
<dt>exclude_words</dt>
<dd>The words that should be excluded.</dd></dl>

Arguments

count_word. From older version of simplifyEnrichment package. — count_word

<dl>

<dt>term</dt>
<dd>A vector of description texts.</dd>


<dt>exclude_words</dt>
<dd>The words that should be excluded.</dd>

</dl>

count_word: count_word. From older version of simplifyEnrichment package.

Description

Usage

Value

Arguments