Abstract
To perform integrative analysis on multiple genomic data sources, we propose to use Fisher’s combined probability test for consolidated inference. The method combines the individual p-values from different data sources and constructs a chi-square test statistics for the overall significance. This method is valid to combine results across independent data sources. We further improve the method to accommodate the scenario that the data sources are dependent or the data samples are too small to obtain valid p-values through exact distributions. The proposed method is convenient to use in practice and is robust to distributional assumptions and small sample sizes.
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References
Reif D, White B, Moore J (2004) Integrated analysis of genetic, genomic and proteomic data. Expert Rev Proteomics 1:67–75
Hamid J, Hu P, Roslin M et al (2009) Data integration in genetics and genomics: methods and challenges. Human Genomics and Proteomics 2009, Article ID 869093
Lanckriet G, De Bie T, Cristianini N et al (2004) A statistical framework for genomic data fusion. Bioinformatics 20:2626–2635
Daemen A, Gevaert O, De Bie T et al (2008) Integrating microarray and proteomics data to predict the response on cetuximab in patients with rectal cancer. Pac Symp Biocomput 13:166–177
Buness A, Ruschhaupt M, Kuner R et al (2009) Classification across gene expression microarray studies. BMC Bioinformatics 10:453
Tian Q, Stepaniants S, Mao M et al (2004) Integrated genomic and proteomic analyses of gene expression in mammalian cells. Mol Cell Proteomics 3:960–969
Bussey K, Chin K, Lababidi S et al (2006) Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol Cancer Ther 5:853–867
Adourian A, Jennings E, Balasubramanian R et al (2003) Correlation network analysis for data integration and biomarker selection. Royal Soc Chem 4:249–259
Ma Y, Ding Z, Qian Y et al (2009) An integrative genomic and proteomic approach to chemosensitivity prediction. Int J Oncol 34:107–115
Aerts S, Lambrechts D, Maity S et al (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24:537–544
Rhodes D, Yu J, Shanker K et al (2004) Large-scale meta analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 25:9309–9314
Wu S, Xu Y, Feng Z et al (2012) Multiple-platform data integration method with application to combined analysis of microarray and proteomic data. BMC Bioinformatics 13:320
Fisher RA (1925) Statistical methods for research workers. Oliver and Boyd, Edinburgh
Brierley N, Tippetts T, Cawley P (2014) Data fusion for automated non-destructive inspection. Proc R Soc A 470, Issue 2167
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Series B 57:289–300
Gao X (2006) Construction of null statistics in permutation based multiple testing for multi-factorial microarray experiments. Bioinformatics 22:1486–1494
Jayapal K, Philp R, Kok Y et al (2008) Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor. PLoS One 7:e2097
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Gao, X. (2016). Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data. In: Jung, K. (eds) Statistical Analysis in Proteomics. Methods in Molecular Biology, vol 1362. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3106-4_13
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DOI: https://doi.org/10.1007/978-1-4939-3106-4_13
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3105-7
Online ISBN: 978-1-4939-3106-4
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