Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery

DOI10.1214/20-AOAS1407zbMath1475.62259OpenAlexW3138502941MaRDI QIDQ2233190

David K. Lim, Naim U. Rashid, Joseph G. Ibrahim

Publication date: 14 October 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/20-aoas1407

zbMATH Keywords

clusteringconfoundersRNA-sequences

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20) Statistical ranking and selection procedures (62F07)

Related Items (2)

A sparse negative binomial classifier with covariate adjustment for RNA-seq data ⋮ Conditional mean embedding and optimal feature selection via positive definite kernels

Uses Software

• mclust
• Bioconductor
• edgeR
• NMF
• glmnet
• DEseq
• svaseq
• NbClust
• RSEM
• Salmon
• TCGAbiolinks
• zinbwave
• iClusterPlus
• NB.MClust
• FSCseq
• DESeq2

Cites Work

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• 10.1162/153244303321897735
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Convergence of a block coordinate descent method for nondifferentiable minimization

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