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scTenifoldNet (version 1.2.2)

Construct and Compare scGRN from Single-Cell Transcriptomic Data

Description

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.

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Install

install.packages('scTenifoldNet')

Monthly Downloads

2,142

Version

1.2.2

License

GPL (>= 2)

Issues

5

Pull Requests

0

Stars

38

Forks

5

Maintainer

Daniel Osorio

Last Published

May 13th, 2020

Functions in scTenifoldNet (1.2.2)

scTenifoldNet

scTenifoldNet
makeNetworks

Computes gene regulatory networks for subsamples of cells based on principal component regression.
cpmNormalization

Performs counts per million (CPM) data normalization
pcNet

Computes a gene regulatory network based on principal component regression
cpDecomposition

Canonical Polyadic Decomposition
scQC

Performs single-cell data quality control
dRegulation

Evaluates gene differential regulation based on manifold alignment distances.
tensorDecomposition

Performs CANDECOMP/PARAFAC (CP) Tensor Decomposition.
manifoldAlignment

Performs non-linear manifold alignment of two gene regulatory networks.