Analyzing Illumina Gene Expression Microarray Data Obtained From Human Whole Blood Cell and Blood Monocyte Samples
Microarray profiling of gene expression is widely applied to studies in molecular biology and functional genomics. Experimental and technical variations make not only the statistical analysis of single studies but also meta-analyses of different studies very challenging. Here, we describe the analytical steps required to substantially reduce the variations of gene expression data without affecting true effect sizes. A software pipeline has been established using gene expression data from a total of 3358 whole blood cell and blood monocyte samples, all from three German population-based cohorts, measured on the Illumina HumanHT-12 v3 BeadChip array. In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses of different studies.
Key wordsGene expression analysis Transcriptomics Human whole blood cells Human monocytes Microarray ILLUMINA HumanHT-12 BeadChip
This work was funded by the European Commission’s Seventh Framework Programme (FP7/2007-2013, HEALTH-F2-2011, grant agreement No. 277984, TIRCON), the BMBF (German Ministry of Education and Research) grants 03IS2061A, 03ZIK012, 01GS0834, 01GS0833, 01GS0831, 01KU0908A, 01KU0908B, 0315536F, and the National Genome Research Network NGFNplus Atherogenomics, the Federal State of Mecklenburg-West Pomerania, the Caché Campus program of the InterSystems GmbH, the Helmholtz Zentrum München (German Research Center for Environmental Health), the State of Bavaria, the German Center for Diabetes Research (DZD e.V.), the State of North-Rhine-Westphalia, the Leibniz Association (WGL Pakt für Forschung und Innovation), the government of Rheinland-Pfalz (“Stiftung Rheinland Pfalz für Innovation”, contract AZ 961–386261/733), the Johannes Gutenberg-University of Mainz, and its contract with Boehringer Ingelheim and PHILIPS Medical Systems, the Agence Nationale de la Recherche, France (contract ANR 09 GENO 106 01), the European Union (HEALTH-2011-278913), and the DZHK (German Centre for Cardiovascular Research).
- 16.R Development Core Team (2006) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org. ISBN 3-900051-07-0
- 17.Smith ML, Baggerly KA, Bengtsson H et al. (2013) illuminaio: an open source idat parsing tool for Illumina microarrays. F1000Research 2Google Scholar
- 19.Du P, Kibbe WA, Lin SM (2007) nuID: a universal naming scheme of oligonucleotides for Illumina, Affymetrix, and other microarrays. Biol Direct 2: 16Google Scholar