Differential Expression From Microarray and RNA-seq Experiments

TY - CHAP

T1 - Differential Expression From Microarray and RNA-seq Experiments

AU - Delord, Marc

PY - 2019

Y1 - 2019

N2 - The genome project has provided new insights in molecular biology research and paved the way for transcriptomics and large scale differential gene expression analysis. Major methods used for quantifying the genome includes hybridization-based approaches using microarrays and the sequencing approach (RNA-seq), which is the reference method. Both methods result in large datasets in which the number of measured mRNA transcripts far exceeds the sample size. Differential expression analysis is therefore a challenging task, which is best addressed as a single high-dimensional inference problem rather than as multiple single inference procedures considered as independent from one another. In this article we introduce the main principles of high-throughput gene expression data measurement using microarrays and RNA-seq, some preprocessing and normalization steps, and the principle of differential expression analysis in the framework of empirical Bayes procedure for expression data arising from microarrays and RNA-seq.

AB - The genome project has provided new insights in molecular biology research and paved the way for transcriptomics and large scale differential gene expression analysis. Major methods used for quantifying the genome includes hybridization-based approaches using microarrays and the sequencing approach (RNA-seq), which is the reference method. Both methods result in large datasets in which the number of measured mRNA transcripts far exceeds the sample size. Differential expression analysis is therefore a challenging task, which is best addressed as a single high-dimensional inference problem rather than as multiple single inference procedures considered as independent from one another. In this article we introduce the main principles of high-throughput gene expression data measurement using microarrays and RNA-seq, some preprocessing and normalization steps, and the principle of differential expression analysis in the framework of empirical Bayes procedure for expression data arising from microarrays and RNA-seq.

U2 - 10.1016/B978-0-12-809633-8.20211-2

DO - 10.1016/B978-0-12-809633-8.20211-2

M3 - Chapter

SN - 9780128114322

SP - 372

EP - 387

BT - Encyclopedia of Bioinformatics and Computational Biology

PB - Elsevier

ER -