An empirical Bayes change-point model for transcriptome time-course data

DOI10.1214/20-AOAS1403zbMath1475.62268OpenAlexW3137005095MaRDI QIDQ2233191

Yanyan Li

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-aoas1403

zbMATH Keywords

empirical Bayestime seriestranscriptomechange-point model

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Uses Software

• Bioconductor
• gcrma
• edgeR
• affy
• limma
• Voom
• TensorFlow
• tensorflow
• Adam
• gprege
• DESeq2

Cites Work

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• A Bayesian Analysis for Change Point Problems
• Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Exploration, normalization, and summaries of high density oligonucleotide array probe level data