A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data

DOI10.1515/sagmb-2015-0070zbMath1343.92162OpenAlexW2292844980WikidataQ31050289 ScholiaQ31050289MaRDI QIDQ306683

Zhixiang Lin, Mingfeng Li, Hongyu Zhao, Nenad Sestan

Publication date: 1 September 2016

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5587217

zbMATH Keywords

Markov random field modelneurodevelopmentRNA-seq and differential expression

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Uses Software

• Bioconductor
• edgeR
• casper
• DEseq
• RSEM
• EBSeq
• DAVID

Cites Work

• EM procedures using mean field-like approximations for Markov model-based image segmentation
• A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data
• Quantifying alternative splicing from paired-end RNA-sequencing data
• A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
• A hidden Markov random field model for genome-wide association studies
• Small-sample estimation of negative binomial dispersion, with applications to SAGE data
• The mean field theory in EM procedures for Markov random fields
• Large-Scale Simultaneous Hypothesis Testing
• Unnamed Item

This page was built for publication: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data