shuffle_fibers

Shuffle elements within the selected fibers

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

shuffle_fibers 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>s_fibers</dt>
<dd>list Gene and cell type indices for the randomly selected fibers</dd></dl>

Arguments

Shuffle elements within the selected fibers — shuffle_fibers

<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>s_fibers</dt>
<dd>list Gene and cell type indices for the randomly selected fibers</dd>

</dl>

shuffle_fibers: Shuffle elements within the selected fibers

Description

Usage

Value

Arguments