get_num_batch_ranks

Plot factor-batch associations for increasing number of donor factors

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_num_batch_ranks 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>donor_ranks_test</dt>
<dd>numeric The number of donor rank values to test</dd>
<dt>gene_ranks</dt>
<dd>numeric The number of gene ranks to use throughout</dd>
<dt>batch_var</dt>
<dd>character The name of the batch meta variable</dd>
<dt>thresh</dt>
<dd>numeric The threshold r-squared cutoff for considering a
factor to be a batch factor. Can be a vector of multiple values to get plots
at varying thresholds. (default=0.5)</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 optimize loadings via our hybrid
method (see paper for details). Set to 'ica_dsc' to perform ICA rotation
on resulting donor factor matrix. Set to 'ica_lds' to optimize loadings by the
ICA rotation. (default='hybrid')</dd></dl>

Arguments

Plot factor-batch associations for increasing number of donor factors — get_num_batch_ranks

<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>donor_ranks_test</dt>
<dd>numeric The number of donor rank values to test</dd>


<dt>gene_ranks</dt>
<dd>numeric The number of gene ranks to use throughout</dd>


<dt>batch_var</dt>
<dd>character The name of the batch meta variable</dd>


<dt>thresh</dt>
<dd>numeric The threshold r-squared cutoff for considering a
factor to be a batch factor. Can be a vector of multiple values to get plots
at varying thresholds. (default=0.5)</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 optimize loadings via our hybrid
method (see paper for details). Set to 'ica_dsc' to perform ICA rotation
on resulting donor factor matrix. Set to 'ica_lds' to optimize loadings by the
ICA rotation. (default='hybrid')</dd>

</dl>

get_num_batch_ranks: Plot factor-batch associations for increasing number of donor factors

Description

Usage

Value

Arguments

Examples