title: Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack.
abstract: BACKGROUND
  It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information.
  
  
  RESULTS
  Here, we extend the model introduced by Menezes et al. 2009 to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations and yields a test to help separate true associations from noise. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples.
  
  
  CONCLUSIONS
  Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package.
authors: Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1089a, HV Amsterdam, 1081, The Netherlands. r.menezes@vumc.nl.
authors: Renée X
authors: RX
authors: Menezes
authors: Department of Statistics, Shiraz University, Shiraz, Iran. mohammadileila@yahoo.com.
authors: Leila
authors: L
authors: Mohammadi
authors: Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands. jelle.goeman@radboudumc.nl.|Medical Statistics and Bioinformatics, Leiden University Medical Center, Nijmegen, The Netherlands. jelle.goeman@radboudumc.nl.
authors: Jelle J
authors: JJ
authors: Goeman
authors: Department of Pediatric Oncology and Hematology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands. j.m.boer@erasmusmc.nl.|Netherlands Bioinformatics Centre, Nijmegen, The Netherlands. j.m.boer@erasmusmc.nl.
authors: Judith M
authors: JM
authors: Boer
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D003110:Colonic Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019175:DNA Methylation; D005260:Female; D018628:Gene Dosage; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D005838:Genotype; D006801:Humans; D019656:Loss of Heterozygosity; D020641:Polymorphism, Single Nucleotide; D059467:Transcriptome