Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium

• Published in: PloS one Vol. 7; no. 12; p. e50938
• Main Authors: Schurmann, Claudia, Heim, Katharina, Schillert, Arne, Blankenberg, Stefan, Carstensen, Maren, Dörr, Marcus, Endlich, Karlhans, Felix, Stephan B, Gieger, Christian, Grallert, Harald, Herder, Christian, Hoffmann, Wolfgang, Homuth, Georg, Illig, Thomas, Kruppa, Jochen, Meitinger, Thomas, Müller, Christian, Nauck, Matthias, Peters, Annette, Rettig, Rainer, Roden, Michael, Strauch, Konstantin, Völker, Uwe, Völzke, Henry, Wahl, Simone, Wallaschofski, Henri, Wild, Philipp S, Zeller, Tanja, Teumer, Alexander, Prokisch, Holger, Ziegler, Andreas
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.12.2012; Public Library of Science (PLoS)
• Subjects: Amplification; Analysis; Binding sites; Bioinformatics; Biology; Blood; Blood banks; Blood donation; Cancer; Consortia; Data analysis; Data processing; Diabetes; DNA microarrays; DNA probes; Engineering; Environmental health; Epidemiology; Gene Expression; Gene Expression Profiling - methods; Gene frequency; Genes; Genetic transformation; Genetics; Genomes; Genomics; Germany; Humans; Hybridization; Internal medicine; Laboratories; Leukocytes; Medical prognosis; Medicine; Molecular biology; Monocytes; Oligonucleotide Array Sequence Analysis - methods; Polymorphism, Single Nucleotide; Population; Preprocessing; Reliability analysis; Reliability aspects; Reproducibility of Results; Ribonucleic acid; RNA; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Studies; Tissues; Transformation; White blood cells; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0050938

Microarray profiling of gene expression is widely applied in molecular biology and functional genomics. Experimental and technical variations make meta-analysis of different studies challenging. In a total of 3358 samples, all from German population-based cohorts, we investigated the effect of data preprocessing and the variability due to sample processing in whole blood cell and blood monocyte gene expression data, measured on the Illumina HumanHT-12 v3 BeadChip array.Gene expression signal intensities were similar after applying the log(2) or the variance-stabilizing transformation. In all cohorts, the first principal component (PC) explained more than 95% of the total variation. Technical factors substantially influenced signal intensity values, especially the Illumina chip assignment (33-48% of the variance), the RNA amplification batch (12-24%), the RNA isolation batch (16%), and the sample storage time, in particular the time between blood donation and RNA isolation for the whole blood cell samples (2-3%), and the time between RNA isolation and amplification for the monocyte samples (2%). White blood cell composition parameters were the strongest biological factors influencing the expression signal intensities in the whole blood cell samples (3%), followed by sex (1-2%) in both sample types. Known single nucleotide polymorphisms (SNPs) were located in 38% of the analyzed probe sequences and 4% of them included common SNPs (minor allele frequency >5%). Out of the tested SNPs, 1.4% significantly modified the probe-specific expression signals (Bonferroni corrected p-value<0.05), but in almost half of these events the signal intensities were even increased despite the occurrence of the mismatch. Thus, the vast majority of SNPs within probes had no significant effect on hybridization efficiency.In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses.