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Microarray analysis People
We have large experience in experimental design and analysis of microarray experiments from various platforms. Typically, the different steps in our analysis approach are

  • experimental design: choice of platform, sample size, randomization of samples across multiple conditions
  • quality control (QC): variety of QC plots and quantitative measures to aid in detecting problematic microarrays
  • preprocessing: normalization with a variety of state-of-the-art methods
  • unsupervised analysis: for example, using clustering and principal component analysis
  • differential expression: detection of differentially expressed probes for a variety of experimental designs (multiple groups, time series, inclusion of covariates, regression), emperical Bayes, correction for multiple testing, permutation tests
  • classification: discovery of a predictive signature using a variety of statistical and machine learning models (discriminant analysis, decision trees, logistic regression, nearest centroid, support vector machines, neural networks, etc) in a double cross-validation scheme
    • complete separation of training data used for estimating the parameters of a model and test data for estimating the accuracy of the model
    • multiple estimates of classification accuracy to be able to assess its variance
    • cross-validation on the training data to determine optimal values for hyperparameters of a model and to select features
  • probe annotation: including re-annotation of Affymetrix and Illumina probe sequences
  • geneset analysis: interpretation of a microarray experiments using Gene Ontology, biological pathways, sets of promoters, and other types of genesets

Most of our analyses are performed using R and tools available from Bioconductor.

Platforms included in our standard analysis pipelines are

  • Affymetrix: all types of Affymetrix 3' expression (IVT) arrays
  • Agilent: whole genome microarrays - 1x44k and 4x44k, one-color and two-color
  • Illumina: all types of mRNA BeadArrays (WG-6, Ref8, HT-12) and miRNA BeadArrays

We also have considerable experience with various types of custom-made arrays, SNP arrays (Illumina Infinium BeadChips), and analysis of cross-species (heterologous) experiments.

We set up a yearly four-week course with a mix of lectures and well-structured computer exercises to learn the tricks of the trade in analyzing genome-wide expression data.

Selected publications
Nora Bijl, Milka Sokolovic, Carlos Vrins, Mirjam Langeveld, Perry D. Moerland, Nike Claessen, Roelof Ottenhoff, Cindy van Roomen, Peter Dubbelhuis, Rolf Boot, Jan Aten, Bert Groen, Johannes M.F.G. Aerts, Marco van Eijk (2009). Modulation of Glycosphingolipid Metabolism Significantly Improves Hepatic Insulin Sensitivity and Reverses Hepatic Steatosis in Mice. Hepatology; 50(5):1431-41. PubMed
Miinalainen IJ, Schmitz W, Huotari A, Autio KJ, Soininen R, Ver Loren van Themaat E, Baes M, Herzig KH, Conzelmann E, Hiltunen JK (2009). Mitochondrial 2,4-dienoyl-CoA reductase deficiency in mice results in severe hypoglycemia with stress intolerance and unimpaired ketogenesis. PLoS Genetics, 5(7):e1000453. PubMed

Stephan H. Schirmer, Joost O. Fledderus, Pieter T.G. Bot, Perry D. Moerland, Jan Baan Jr. , Jose P. Henriques, Renee J. van der Schaaf, Marije M. Vis, Anton J.G. Horrevoets, Jan J. Piek, Niels van Royen (2008). Interferon-beta Signaling Is Enhanced in Patients with Insufficient Coronary Collateral Artery Development and Inhibits Arteriogenesis in Mice. Circulation Research;102(10):1286-94. PubMed
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Topic revision: r10 - 21 Mar 2011, AngelaLuijf

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