plot_donor_sig_genes

Generate a gene by donor heatmap showing scaled expression of top loading genes
for a given factor

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

plot_donor_sig_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 query</dd>
<dt>top_n_per_ctype</dt>
<dd>numeric Vector of the number of top genes from each cell type
to plot</dd>
<dt>ctypes_use</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>
<dt>show_donor_labels</dt>
<dd>logical Set to TRUE to display donor labels (default=FALSE)</dd>
<dt>additional_meta</dt>
<dd>character Another meta variable to plot (default=NULL)</dd>
<dt>add_genes</dt>
<dd>character Additional genes to plot for all ctypes (default=NULL)</dd></dl>

Arguments

Generate a gene by donor heatmap showing scaled expression of top loading genes
for a given factor — plot_donor_sig_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 query</dd>


<dt>top_n_per_ctype</dt>
<dd>numeric Vector of the number of top genes from each cell type
to plot</dd>


<dt>ctypes_use</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>


<dt>show_donor_labels</dt>
<dd>logical Set to TRUE to display donor labels (default=FALSE)</dd>


<dt>additional_meta</dt>
<dd>character Another meta variable to plot (default=NULL)</dd>


<dt>add_genes</dt>
<dd>character Additional genes to plot for all ctypes (default=NULL)</dd>

</dl>

plot_donor_sig_genes: Generate a gene by donor heatmap showing scaled expression of top loading genes for a given factor

Description

Usage

Value

Arguments

Examples