normalize_pseudobulk

Normalize the pseudobulked counts matrices. 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

normalize_pseudobulk 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 normalization method to use on the pseudobulked
count data. Set to 'regular' to do standard normalization of dividing by
library size. Set to 'trim' to use edgeR trim-mean normalization, whereby counts
are divided by library size times a normalization factor. (default='trim')</dd>
<dt>scale_factor</dt>
<dd>numeric The number that gets multiplied by fractional counts
during normalization of the pseudobulked data (default=10000)</dd></dl>

Arguments

Normalize the pseudobulked counts matrices. Generally, this should be done through calling the
form_tensor() wrapper function. — normalize_pseudobulk

<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 normalization method to use on the pseudobulked
count data. Set to 'regular' to do standard normalization of dividing by
library size. Set to 'trim' to use edgeR trim-mean normalization, whereby counts
are divided by library size times a normalization factor. (default='trim')</dd>


<dt>scale_factor</dt>
<dd>numeric The number that gets multiplied by fractional counts
during normalization of the pseudobulked data (default=10000)</dd>

</dl>

normalize_pseudobulk: Normalize the pseudobulked counts matrices. Generally, this should be done through calling the form_tensor() wrapper function.

Description

Usage

Value

Arguments