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scTenifoldNet
View on CRAN: Click
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Download and install scTenifoldNet package within the R console
Install from CRAN:
install.packages("scTenifoldNet")
Install from Github:
library("remotes")
install_github("cran/scTenifoldNet")
Install by package version:
library("remotes")
install_version("scTenifoldNet", "1.3")
Attach the package and use:
library("scTenifoldNet")
Maintained by
Daniel Osorio
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All associated links for this package
First Published: 2019-12-15
Latest Update: 2021-10-29
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. See for more details.
How to cite:
Daniel Osorio (2019). scTenifoldNet: Construct and Compare scGRN from Single-Cell Transcriptomic Data. R package version 1.3, https://cran.r-project.org/web/packages/scTenifoldNet. Accessed 07 Nov. 2024.
Previous versions and publish date:
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Complete documentation for scTenifoldNet
Functions, R codes and Examples using
the scTenifoldNet R package
Some associated functions: cpDecomposition . cpmNormalization . dRegulation . makeNetworks . manifoldAlignment . pcNet . scQC . scTenifoldNet . tensorDecomposition .
Some associated R codes: asTensor.R . cpDecomposition.R . cpmNormalization.R . dModularity.R . dRegulation.R . makeNetworks.R . manifoldAlignment.R . pcNet.R . scQC.R . scTenifoldNet.R . tensorDecomposition.R . Full scTenifoldNet package functions and examples
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