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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
Microarray data analysis for differential expression: a tutorial
Erick Suárez1, Ana Burguete, Geoffrey J Mclachlan
1Department of Biostatistics and Epidemiology, School of Public Health, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico. erick.suarez@upr.edu
Abstract:
DNA microarray is a technology that simultaneously evaluates quantitative measurements for the expression of thousands of genes. DNA microarrays have been used to assess gene expression between groups of cells of different organs or different populations. In order to understand the role and function of the genes, one needs the complete information about their mRNA transcripts and proteins. Unfortunately, exploring the protein functions is very difficult, due to their unique 3-dimentional complicated structure. To overcome this difficulty, one may concentrate on the mRNA molecules produced by the gene expression. In this paper, we describe some of the methods for preprocessing data for gene expression and for pairwise comparison from genomic experiments. Previous studies to assess the efficiency of different methods for pairwise comparisons have found little agreement in the lists of significant genes. Finally, we describe the procedures to control false discovery rates, sample size approach for these experiments, and available software for microarray data analysis. This paper is written for those professionals who are new in microarray data analysis for differential expression and want to have an overview of the specific steps or the different approaches for this sort of analysis.

