A test for comparing two groups of samples when analyzing multiple omics profiles

BMC Bioinformatics

Nimisha Chaturvedi³  Jelle J Goeman²  Judith M Boer³  Wessel N van Wieringen¹  Renée X de Menezes³ 

[1] Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands

[2] Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands

[3] Netherlands Bioinformatics Center, Nijmegen, The Netherlands

关键词: Penalized regression;    Joint analysis;    Group effect;   

Others  :  1087556
DOI  :  10.1186/1471-2105-15-236

 received in 2014-02-28, accepted in 2014-06-28,  发布年份 2014

【 摘 要 】

Background

A number of statistical models has been proposed for studying the association between gene expression and copy number data in integrated analysis. The next step is to compare association patterns between different groups of samples.

Results

We propose a method, named dSIM, to find differences in association between copy number and gene expression, when comparing two groups of samples. Firstly, we use ridge regression to correct for the baseline associations between copy number and gene expression. Secondly, the global test is applied to the corrected data in order to find differences in association patterns between two groups of samples. We show that dSIM detects differences even in small genomic regions in a simulation study. We also apply dSIM to two publicly available breast cancer datasets and identify chromosome arms where copy number led gene expression regulation differs between positive and negative estrogen receptor samples. In spite of differing genomic coverage, some selected arms are identified in both datasets.

Conclusion

We developed a flexible and robust method for studying association differences between two groups of samples while integrating genomic data from different platforms. dSIM can be used with most types of microarray/sequencing data, including methylation and microRNA expression. The method is implemented in R and will be made part of the BioConductor package SIM.

【 授权许可】

   
2014 Chaturvedi et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Van Wieringen WN, Berkhof J, Van de Wiel MA: A random coefficients model for regional co-expression associated with dna copy number. Stat Appl Genet Mol Biol 2010, 9(1):25-128.
• [2]Horlings HM, Lai C, Nuyten DSA, Halfwerk H, Kristel P, van Beers E, Joosse SA, Klijn C, Nederlof PM, Reinders MJT, Wessels LFA, van de Vijver MJ: Integration of dna copy number alterations and prognostic gene expression signatures in breast cancer patients. Clin Cancer Res 2010, 16(2):651-663.
• [3]Lee H, Kong SW, Park PJ: Integrative analysis reveals the direct and indirect interactions between dna copy number aberrations and gene expression changes. Bioinformatics 2008, 24(7):889-896.
• [4]Hyman E, Kauraniemi P, Hautaniemi S, Wolf M, Mousses S, Rozenblum E, Ringnér M, Sauter G, Monni O, Elkahloun A, Kallioniemi O-P, Kallioniemi A: Impact of dna amplification on gene expression patterns in breast cancer. Cancer Res 2002, 62(21):6240-6245.
• [5]Pollack JR, Sørlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Børresen-Dale A-L, Brown PO: Microarray analysis reveals a major direct role of dna copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci 2002, 99(20):12963-12968.
• [6]van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, ‘t Hoen PAC, Menezes RX, Boer JM: Integrated analysis of microrna and mrna expression: adding biological significance to microrna target predictions. Nucleic Acids Res 2013, 41(11):1-10.
• [7]Menezes R, Boetzer M, Sieswerda M, van Ommen G-J, Boer J: Integrated analysis of dna copy number and gene expression microarray data using gene sets. BMC Bioinformatics 2009, 10(1):203.
• [8]Hoerl AE, Kennard RW: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12(1):55-67.
• [9]Goeman JJ, Van De Geer SA, Van Houwelingen HC: Testing against a high dimensional alternative. J Roy Stat Soc B Stat Meth 2006, 68(3):477-493.
• [10]Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004, 20(1):93-99.
• [11]Meinshausen N: False discovery control for multiple tests of association under general dependence. Scand J Stat 2006, 33(2):227-237.
• [12]Goeman JJ, Solari A: Tutorial in biostatistics: multiple hypothesis testing in genomics. Stat Med 2012, 00:1-20.
• [13]Bentzon N, Düring M, Rasmussen BB, Mouridsen H, Kroman N: Prognostic effect of estrogen receptor status across age in primary breast cancer. Int J Cancer 2008, 122(5):1089-1094.
• [14]Sotiriou C, Neo S-Y, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci 2003, 100(18):10393-10398.
• [15]Gruvberger S, Ringnér M, Chen Y, Panavally S, Saal LH, Borg K, Fernö M, Peterson C, Meltzer PS: Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 2001, 61(16):5979-5984.
• [16]Holm K, Staaf J, Jonsson G, Vallon-Christersson J, Gunnarsson H, Arason A, Magnusson L, Barkardottir R, Hegardt C, Ringner M, Borg A: Characterisation of amplification patterns and target genes at chromosome 11q13 in ccnd1-amplified sporadic and familial breast tumours. Breast Cancer Res Treat 2012, 133(2):583-594.
• [17]Takahata C, Miyoshi Y, Irahara N, Taguchi T, Tamaki Y, Noguchi S: Demonstration of adiponectin receptors 1 and 2 mrna expression in human breast cancer cells. Cancer Lett 2007, 250(2):229-236.
• [18]Delort L, Lequeux C, Dubois V, Dubouloz A, Billard H, Mojallal A, Damour O, Vasson M-P, Caldefie-Chézet F: Reciprocal interactions between breast tumor and its adipose microenvironment based on a 3d adipose equivalent model. PLoS ONE 2013, 8(6):66284.
• [19]Dieudonne M-N, Bussiere M, Santos ED, Leneveu M-C, Giudicelli Y, Pecquery R: Adiponectin mediates antiproliferative and apoptotic responses in human mcf7 breast cancer cells. Biochem Biophys Res Commun 2006, 345(1):271-279.
• [20]Miyoshi Y, Funahashi T, Kihara S, Taguchi T, Tamaki Y, Matsuzawa Y, Noguchi S: Association of serum adiponectin levels with breast cancer risk. Clin Cancer Res 2003, 9(15):5699-5704.
• [21]Barb D, Williams CJ, Neuwirth AK, Mantzoros CS: Adiponectin in relation to malignancies: a review of existing basic research and clinical evidence. Am J Clin Nutr 2007, 86(3):858-866.
• [22]Otvos L, Haspinger E, La Russa F, Maspero F, Graziano P, Kovalszky I, Lovas S, Nama K, Hoffmann R, Knappe D, Cassone M, Wade J, Surmacz E: Design and development of a peptide-based adiponectin receptor agonist for cancer treatment. BMC Biotechnol 2011, 11(1):90.
• [23]Ezeh UI, Turek PJ, Reijo RA, Clark AT: Human embryonic stem cell genes oct4, nanog, stellar, and gdf3 are expressed in both seminoma and breast carcinoma. Cancer 2005, 104(10):2255-2265.
• [24]Li Q, Ling Y, Yu L: Gdf3 inhibits the growth of breast cancer cells and promotes the apoptosis induced by taxol. J Cancer Res Clin Oncol 2012, 138(6):1073-1079.
• [25]Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, Weinberg RA: An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet 2008, 40(5):499-507.
• [26]Martini PGV, Katzenellenbogen BS: Modulation of estrogen receptor activity by selective coregulators. J Steroid Biochem Mol Biol 2003, 85(2–5):117-122.
• [27]Artmann S, Jung K, Bleckmann A, Beißbarth T: Detection of simultaneous group effects in microrna expression and related target gene sets. PLoS ONE 2012, 7(6):38365.
• [28]Meinshausen N, Maathuis MH, Buhlmann P: Asymptotic optimality of the westfall-young permutation procedure for multiple testing under dependence. Ann Stat 2011, 39(6):3369-3391.
• [29]Dudoit S, Shaffer JP, Boldrick JC: Multiple hypothesis testing in microarray experiments. Stat Sci 2003, 18(1):71-103.
• [30]Westfall P, Young S: Resampling-based multiple testing: examples and methods for p-value adjustment. In Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction . Edited by Cox DR, Hambly B, Holmes S, Meng XL. New York: Cambridge University Press; 2010.