pcNetCoreRcpp

Computes the gene coefficient matrix using principal component
regression. For each gene K, regresses it against principal components
derived from all other genes via truncated SVD.

internal

A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.

Daniel Osorio

scTenifoldNet

Construct and Compare scGRN from Single-Cell Transcriptomic Data

Yan Zhong

Guanxun Li

Jianhua Huang

James Cai

pcNetCoreRcpp function

<dl><dt>X_std</dt>
<dd>A standardized numeric matrix (samples x genes), centered
and scaled by column.</dd>
<dt>nComp</dt>
<dd>Number of principal components to use.</dd></dl>

Arguments

Core PC Network computation (C++ backend) — pcNetCoreRcpp

<dl>

<dt>X_std</dt>
<dd>A standardized numeric matrix (samples x genes), centered
and scaled by column.</dd>


<dt>nComp</dt>
<dd>Number of principal components to use.</dd>

</dl>

pcNetCoreRcpp: Core PC Network computation (C++ backend)

Description

Usage

Value

Arguments