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SCFA: Subtyping via Consensus Factor Analysis

Bioconductor version: Release (3.19)

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>

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


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SCFA package manual HTML R Script
Reference Manual PDF


biocViews Classification, Clustering, Software, Survival
Version 1.14.0
In Bioconductor since BioC 3.12 (R-4.0) (3.5 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
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Source Package SCFA_1.14.0.tar.gz
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