This site is a development preview. As such the content and styling may not be final and is subject to change before going into production. To see more information about the redesign click here.

SCFA

This is the development version of SCFA; for the stable release version, see SCFA.

SCFA: Subtyping via Consensus Factor Analysis

Bioconductor version: Development (3.18)

Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients.

Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd]

Maintainer: Duc Tran <duct at nevada.unr.edu>

Citation (from within R, enter citation("SCFA")):

Installation

To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("SCFA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("SCFA")
SCFA package manual HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Classification, Clustering, Software, Survival
Version 1.11.0
In Bioconductor since BioC 3.12 (R-4.0) (3 years)
License LGPL
Depends R (>= 4.0)
Imports matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph, Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival
Linking To
Suggests knitr, rmarkdown, BiocStyle
System Requirements
Enhances
URL https://github.com/duct317/SCFA
Bug Reports https://github.com/duct317/SCFA/issues
See More
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package SCFA_1.11.0.tar.gz
Windows Binary SCFA_1.11.0.zip
macOS Binary (x86_64) SCFA_1.11.0.tgz
macOS Binary (arm64) SCFA_1.11.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/SCFA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/SCFA
Bioc Package Browser https://code.bioconductor.org/browse/SCFA/
Package Short Url https://bioconductor.org/packages/SCFA/
Package Downloads Report Download Stats