Analyzing Gene Pathways from Microarrays to Sequencing Platforms

Jeffrey Miecznikowski (), Dan Wang (), Xing Ren (), Jianmin Wang () and Song Liu ()
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Jeffrey Miecznikowski: Department of Biostatistics, SUNY University at Buffalo
Dan Wang: Roswell Park Comprehensive Cancer Center
Xing Ren: Department of Biostatistics, SUNY University at Buffalo
Jianmin Wang: Roswell Park Comprehensive Cancer Center
Song Liu: Roswell Park Comprehensive Cancer Center

A chapter in Statistical Modeling for Biological Systems, 2020, pp 289-296 from Springer

Abstract: Abstract Genetic microarrays have been the primary technology for quantitative transcriptome analysis since the mid-1990s. Via statistical testing methodology developed for microarray data, researchers can study genes and gene pathways involved in a disease. Recently a new technology known as RNA-seq has been developed to quantitatively study the transcriptome. This new technology can also study genes and gene pathways, although the statistical methodology used for microarrays must be adapted to this new platform. In this manuscript, we discuss methods of gene pathway analysis in microarrays and next generation sequencing and their advantages over standard “gene by gene” testing schemes.

Keywords: Pathway analysis; Microarrays; RNA-Seq; GSEA; GSA; Multiple testing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-34675-1_16

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