Distance-correlation based gene set analysis in longitudinal studies

DOI10.1515/SAGMB-2017-0053zbMath1398.92162arXiv1609.02983OpenAlexW2963349610WikidataQ49350774 ScholiaQ49350774MaRDI QIDQ1672816

Xiu Huang, Jiehuan Sun, Jose D. Herazo-Maya, Hongyu Zhao, Naftali Kaminski

Publication date: 11 September 2018

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

Full work available at URL: https://arxiv.org/abs/1609.02983

zbMATH Keywords

distance correlationgene set analysislongitudinal gene expression study

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Genetics and epigenetics (92D10)

Uses Software

• EDGE
• KEGG
• ROAST
• GSA
• CAMERA

Cites Work

• Unnamed Item
• Measuring and testing dependence by correlation of distances
• A multivariate empirical Bayes statistic for replicated microarray time course data
• On testing the significance of sets of genes
• Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions
• Permutation, parametric and bootstrap tests of hypotheses.
• A Multiple Comparison Procedure for Comparing Several Treatments with a Control
• On Gene Ranking Using Replicated Microarray Time Course Data

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