Adaptive Filtering Increases Power to Detect Differentially Expressed Genes

DOI10.1007/978-3-319-69416-0_8zbMath1402.62283OpenAlexW2784170341MaRDI QIDQ4556971

Kun Liang, Zixin Nie

Publication date: 28 November 2018

Published in: New Advances in Statistics and Data Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-69416-0_8

zbMATH Keywords

adaptive filtering

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• Bioconductor
• gcrma
• fdrtool
• someMTP
• SimSeq

Cites Work

• Unnamed Item
• Multiple hypothesis testing in microarray experiments.
• The control of the false discovery rate in multiple testing under dependency.
• FDR Control with Pseudo‐Gatekeeping Based on a Possibly Data Driven Order of the Hypotheses
• Adaptive FDR control under independence and dependence
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• Adaptive and Dynamic Adaptive Procedures for False Discovery Rate Control and Estimation
• A Direct Approach to False Discovery Rates
• A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph
• A Modified False Discovery Rate Multiple-Comparisons Procedure for Discrete Data, Applied to Human Immunodeficiency Virus Genetics

This page was built for publication: Adaptive Filtering Increases Power to Detect Differentially Expressed Genes