Identification of Significant Features in DNA Microarray Data

Overview

Journal Wiley Interdiscip Rev Comput Stat

Date 2013 Nov 19

PMID 24244802

Citations 2

Authors

Eric Bair

Affiliations

Soon will be listed here.

Abstract

DNA microarrays are a relatively new technology that can simultaneously measure the expression level of thousands of genes. They have become an important tool for a wide variety of biological experiments. One of the most common goals of DNA microarray experiments is to identify genes associated with biological processes of interest. Conventional statistical tests often produce poor results when applied to microarray data owing to small sample sizes, noisy data, and correlation among the expression levels of the genes. Thus, novel statistical methods are needed to identify significant genes in DNA microarray experiments. This article discusses the challenges inherent in DNA microarray analysis and describes a series of statistical techniques that can be used to overcome these challenges. The problem of multiple hypothesis testing and its relation to microarray studies are also considered, along with several possible solutions.

Citing Articles

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets.

Seah C, Kasim S, Fudzee M, Law Tze Ping J, Mohamad M, Saedudin R Saudi J Biol Sci. 2018; 24(8):1828-1841.

PMID: 29551932 PMC: 5851940. DOI: 10.1016/j.sjbs.2017.11.024.

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

Vasiliu D, Clamons S, McDonough M, Rabe B, Saha M PLoS One. 2015; 10(3):e0118198.

PMID: 25738861 PMC: 4349782. DOI: 10.1371/journal.pone.0118198.

References

Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz S, Tainsky M . Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res. 2003; 31(13):3775-81. PMC: 169030. DOI: 10.1093/nar/gkg624. View

Pawitan Y, Calza S, Ploner A . Estimation of false discovery proportion under general dependence. Bioinformatics. 2006; 22(24):3025-31. DOI: 10.1093/bioinformatics/btl527. View

Maugis C, Celeux G, Martin-Magniette M . Variable selection for clustering with Gaussian mixture models. Biometrics. 2009; 65(3):701-9. DOI: 10.1111/j.1541-0420.2008.01160.x. View

Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M . Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002; 18 Suppl 1:S96-104. DOI: 10.1093/bioinformatics/18.suppl_1.s96. View

ARFIN S, Long A, Ito E, Tolleri L, Riehle M, Paegle E . Global gene expression profiling in Escherichia coli K12. The effects of integration host factor. J Biol Chem. 2000; 275(38):29672-84. DOI: 10.1074/jbc.M002247200. View

Tibshirani R, Hastie T, Narasimhan B, Chu G . Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A. 2002; 99(10):6567-72. PMC: 124443. DOI: 10.1073/pnas.082099299. View

Allison D, Cui X, Page G, Sabripour M . Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 2005; 7(1):55-65. DOI: 10.1038/nrg1749. View

Leek J, Scharpf R, Corrada Bravo H, Simcha D, Langmead B, Johnson W . Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010; 11(10):733-9. PMC: 3880143. DOI: 10.1038/nrg2825. View

Koestler D, Marsit C, Christensen B, Karagas M, Bueno R, Sugarbaker D . Semi-supervised recursively partitioned mixture models for identifying cancer subtypes. Bioinformatics. 2010; 26(20):2578-85. PMC: 2951086. DOI: 10.1093/bioinformatics/btq470. View

10.

Hsiao A, Worrall D, Olefsky J, Subramaniam S . Variance-modeled posterior inference of microarray data: detecting gene-expression changes in 3T3-L1 adipocytes. Bioinformatics. 2004; 20(17):3108-27. DOI: 10.1093/bioinformatics/bth371. View

11.

Nam D, Kim J, Kim S, Kim S . GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 2010; 38(Web Server issue):W749-54. PMC: 2896081. DOI: 10.1093/nar/gkq428. View

12.

Stingo F, Vannucci M . Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data. Bioinformatics. 2010; 27(4):495-501. PMC: 3105481. DOI: 10.1093/bioinformatics/btq690. View

13.

Witten D, Tibshirani R . TESTING SIGNIFICANCE OF FEATURES BY LASSOED PRINCIPAL COMPONENTS. Ann Appl Stat. 2009; 2(3):986-1012. PMC: 2743444. DOI: 10.1214/08-AOAS182SUPP. View

14.

Guo J . Simultaneous variable selection and class fusion for high-dimensional linear discriminant analysis. Biostatistics. 2010; 11(4):599-608. DOI: 10.1093/biostatistics/kxq023. View

15.

Bullard J, Purdom E, Hansen K, Dudoit S . Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010; 11:94. PMC: 2838869. DOI: 10.1186/1471-2105-11-94. View

16.

Leek J, Monsen E, Dabney A, Storey J . EDGE: extraction and analysis of differential gene expression. Bioinformatics. 2005; 22(4):507-8. DOI: 10.1093/bioinformatics/btk005. View

17.

Tanaka T, Jaradat S, Lim M, Kargul G, Wang X, Grahovac M . Genome-wide expression profiling of mid-gestation placenta and embryo using a 15,000 mouse developmental cDNA microarray. Proc Natl Acad Sci U S A. 2000; 97(16):9127-32. PMC: 16833. DOI: 10.1073/pnas.97.16.9127. View

18.

Doniger S, Salomonis N, Dahlquist K, Vranizan K, Lawlor S, Conklin B . MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 2003; 4(1):R7. PMC: 151291. DOI: 10.1186/gb-2003-4-1-r7. View

19.

Taniguchi M, Miura K, Iwao H, Yamanaka S . Quantitative assessment of DNA microarrays--comparison with Northern blot analyses. Genomics. 2001; 71(1):34-9. DOI: 10.1006/geno.2000.6427. View

20.

Berriz G, King O, Bryant B, Sander C, Roth F . Characterizing gene sets with FuncAssociate. Bioinformatics. 2003; 19(18):2502-4. DOI: 10.1093/bioinformatics/btg363. View