get_ctype_vargenes

Partition main gene by cell matrix into per cell type matrices with significantly
variable genes only. Generally, this should be done through calling the
form_tensor() wrapper function.

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_ctype_vargenes 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>method</dt>
<dd>character The method used to select significantly variable
genes across donors within a cell type. Can be either "anova" to use basic
anova with cells grouped by donor or "norm_var" to get the top overdispersed
genes by normalized variance. Set to "norm_var_pvals" to use normalized variance
p-values as calculated in pagoda2.</dd>
<dt>thresh</dt>
<dd>numeric A pvalue threshold to use for gene significance when
method is set to "anova" or "empir". For the method "norm_var" thresh is the
number of top overdispersed genes from each cell type to include.</dd>
<dt>ncores</dt>
<dd>numeric The number of cores to use (default=container$experiment_params$ncores)</dd>
<dt>seed</dt>
<dd>numeric Seed passed to set.seed() (default=container$experiment_params$rand_seed)</dd></dl>

Arguments

Partition main gene by cell matrix into per cell type matrices with significantly
variable genes only. Generally, this should be done through calling the
form_tensor() wrapper function. — get_ctype_vargenes

<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>method</dt>
<dd>character The method used to select significantly variable
genes across donors within a cell type. Can be either "anova" to use basic
anova with cells grouped by donor or "norm_var" to get the top overdispersed
genes by normalized variance. Set to "norm_var_pvals" to use normalized variance
p-values as calculated in pagoda2.</dd>


<dt>thresh</dt>
<dd>numeric A pvalue threshold to use for gene significance when
method is set to "anova" or "empir". For the method "norm_var" thresh is the
number of top overdispersed genes from each cell type to include.</dd>


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


<dt>seed</dt>
<dd>numeric Seed passed to set.seed() (default=container$experiment_params$rand_seed)</dd>

</dl>

Partition main gene by cell matrix into per cell type matrices with significantly
variable genes only. Generally, this should be done through calling the
form_tensor() wrapper function.

get_ctype_vargenes: Partition main gene by cell matrix into per cell type matrices with significantly variable genes only. Generally, this should be done through calling the form_tensor() wrapper function.

Description

Usage

Value

Arguments