compute_LR_interact

Compute and plot the LR interactions for one 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

compute_LR_interact 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>lr_pairs</dt>
<dd>data.frame Data of ligand-receptor pairs. First column should
be ligands and second column should be one or more receptors separated by an
underscore such as receptor1_receptor2 in the case that multiple receptors are
required for signaling.</dd>
<dt>sig_thresh</dt>
<dd>numeric The p-value significance threshold to use for module-
factor associations and ligand-factor associations (default=0.05)</dd>
<dt>percentile_exp_rec</dt>
<dd>numeric The percentile above which the top donors expressing the
ligand all must be expressing the receptor (default=0.75)</dd>
<dt>add_ld_fact_sig</dt>
<dd>logical Set to TRUE to append a heatmap showing significance
of associations between each ligand hit and each factor (default=TRUE)</dd>
<dt>ncores</dt>
<dd>numeric The number of cores to use (default=container$experiment_params$ncores)</dd></dl>

Arguments

Compute and plot the LR interactions for one factor — compute_LR_interact

<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>lr_pairs</dt>
<dd>data.frame Data of ligand-receptor pairs. First column should
be ligands and second column should be one or more receptors separated by an
underscore such as receptor1_receptor2 in the case that multiple receptors are
required for signaling.</dd>


<dt>sig_thresh</dt>
<dd>numeric The p-value significance threshold to use for module-
factor associations and ligand-factor associations (default=0.05)</dd>


<dt>percentile_exp_rec</dt>
<dd>numeric The percentile above which the top donors expressing the
ligand all must be expressing the receptor (default=0.75)</dd>


<dt>add_ld_fact_sig</dt>
<dd>logical Set to TRUE to append a heatmap showing significance
of associations between each ligand hit and each factor (default=TRUE)</dd>


<dt>ncores</dt>
<dd>numeric The number of cores to use (default=container$experiment_params$ncores)</dd>

</dl>

compute_LR_interact: Compute and plot the LR interactions for one factor

Description

Usage

Value

Arguments