get_callouts_annot

Get gene callout annotations for a loadings heatmap

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_callouts_annot 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>tmp_casted_num</dt>
<dd>matrix The gene by cell type loadings matrix</dd>
<dt>factor_select</dt>
<dd>numeric The factor to investigate</dd>
<dt>sig_thresh</dt>
<dd>numeric Pvalue cutoff for significant genes</dd>
<dt>top_n_per_ctype</dt>
<dd>numeric The number of significant, largest magnitude
genes from each cell type to generate callouts for (default=5)</dd>
<dt>ctypes</dt>
<dd>character The cell types for which to get the top genes to make
callouts for. If NULL then uses all cell types. (default=NULL)</dd></dl>

Arguments

Get gene callout annotations for a loadings heatmap — get_callouts_annot

<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>tmp_casted_num</dt>
<dd>matrix The gene by cell type loadings matrix</dd>


<dt>factor_select</dt>
<dd>numeric The factor to investigate</dd>


<dt>sig_thresh</dt>
<dd>numeric Pvalue cutoff for significant genes</dd>


<dt>top_n_per_ctype</dt>
<dd>numeric The number of significant, largest magnitude
genes from each cell type to generate callouts for (default=5)</dd>


<dt>ctypes</dt>
<dd>character The cell types for which to get the top genes to make
callouts for. If NULL then uses all cell types. (default=NULL)</dd>

</dl>

get_callouts_annot: Get gene callout annotations for a loadings heatmap

Description

Usage

Value

Arguments