tucker_ica_helper

Helper function for running the decomposition. Use the run_tucker_ica()
wrapper function instead.

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

tucker_ica_helper function

<dl><dt>tensor_data</dt>
<dd>list The tensor data including donor, gene, and cell type labels
as well as the tensor array itself</dd>
<dt>ranks</dt>
<dd>numeric The number of donor and gene factors respectively,
to decompose to using Tucker decomposition.</dd>
<dt>tucker_type</dt>
<dd>character The 'regular' type is the only one currently implemented</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.</dd>
<dt>projection_container</dt>
<dd>environment A project container to store projection
data in. Currently only implemented for 'hybrid' and 'ica_dsc' rotations. (default=NULL)</dd></dl>

Arguments

Helper function for running the decomposition. Use the run_tucker_ica()
wrapper function instead. — tucker_ica_helper

<dl>

<dt>tensor_data</dt>
<dd>list The tensor data including donor, gene, and cell type labels
as well as the tensor array itself</dd>


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


<dt>tucker_type</dt>
<dd>character The 'regular' type is the only one currently implemented</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.</dd>


<dt>projection_container</dt>
<dd>environment A project container to store projection
data in. Currently only implemented for 'hybrid' and 'ica_dsc' rotations. (default=NULL)</dd>

</dl>

tucker_ica_helper: Helper function for running the decomposition. Use the run_tucker_ica() wrapper function instead.

Description

Usage

Value

Arguments