Literature DB >> 19530550

Microarray data analysis for differential expression: a tutorial.

Erick Suárez1, Ana Burguete, Geoffrey J Mclachlan.   

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.

Mesh:

Year:  2009        PMID: 19530550

Source DB:  PubMed          Journal:  P R Health Sci J        ISSN: 0738-0658            Impact factor:   0.705


  5 in total

Review 1.  Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium.

Authors:  Signe Altmäe; Francisco J Esteban; Anneli Stavreus-Evers; Carlos Simón; Linda Giudice; Bruce A Lessey; Jose A Horcajadas; Nick S Macklon; Thomas D'Hooghe; Cristina Campoy; Bart C Fauser; Lois A Salamonsen; Andres Salumets
Journal:  Hum Reprod Update       Date:  2013-09-29       Impact factor: 15.610

2.  How to get the most from microarray data: advice from reverse genomics.

Authors:  Ivan P Gorlov; Ji-Yeon Yang; Jinyoung Byun; Christopher Logothetis; Olga Y Gorlova; Kim-Anh Do; Christopher Amos
Journal:  BMC Genomics       Date:  2014-03-21       Impact factor: 3.969

3.  A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.

Authors:  Daniel Vasiliu; Samuel Clamons; Molly McDonough; Brian Rabe; Margaret Saha
Journal:  PLoS One       Date:  2015-03-04       Impact factor: 3.240

Review 4.  Quantifying Fetal Reprogramming for Biomarker Development in the Era of High-Throughput Sequencing.

Authors:  Fu-Sheng Chou; Krystel Newton; Pei-Shan Wang
Journal:  Genes (Basel)       Date:  2021-02-25       Impact factor: 4.096

5.  STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data.

Authors:  Daniel Jupiter; Hailin Chen; Vincent VanBuren
Journal:  BMC Bioinformatics       Date:  2009-10-14       Impact factor: 3.169

  5 in total

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