run_jackstraw

Run jackstraw to get genes that are significantly associated with donor scores
for factors extracted by Tucker decomposition

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

run_jackstraw 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>ranks</dt>
<dd>numeric The number of donor ranks and gene ranks to decompose to
using Tucker decomposition</dd>
<dt>n_fibers</dt>
<dd>numeric The number of fibers the randomly shuffle in each iteration
(default=100)</dd>
<dt>n_iter</dt>
<dd>numeric The number of shuffling iterations to complete (default=500)</dd>
<dt>tucker_type</dt>
<dd>character Set to 'regular' to run regular tucker or to 'sparse' to run tucker
with sparsity constraints (default='regular')</dd>
<dt>rotation_type</dt>
<dd>character Set to 'hybrid' to perform hybrid rotation on resulting donor factor
matrix and loadings. Otherwise set to 'ica_lds' to perform ica rotation on loadings or
ica_dsc to perform ica on donor scores. (default='hybrid')</dd>
<dt>seed</dt>
<dd>numeric Seed passed to set.seed() (default=container$experiment_params$rand_seed)</dd>
<dt>ncores</dt>
<dd>numeric The number of cores to use (default=container$experiment_params$ncores)</dd></dl>

Arguments

Run jackstraw to get genes that are significantly associated with donor scores
for factors extracted by Tucker decomposition — run_jackstraw

<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>ranks</dt>
<dd>numeric The number of donor ranks and gene ranks to decompose to
using Tucker decomposition</dd>


<dt>n_fibers</dt>
<dd>numeric The number of fibers the randomly shuffle in each iteration
(default=100)</dd>


<dt>n_iter</dt>
<dd>numeric The number of shuffling iterations to complete (default=500)</dd>


<dt>tucker_type</dt>
<dd>character Set to 'regular' to run regular tucker or to 'sparse' to run tucker
with sparsity constraints (default='regular')</dd>


<dt>rotation_type</dt>
<dd>character Set to 'hybrid' to perform hybrid rotation on resulting donor factor
matrix and loadings. Otherwise set to 'ica_lds' to perform ica rotation on loadings or
ica_dsc to perform ica on donor scores. (default='hybrid')</dd>


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


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

</dl>

run_jackstraw: Run jackstraw to get genes that are significantly associated with donor scores for factors extracted by Tucker decomposition

Description

Usage

Value

Arguments

Examples