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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
  • 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), empirical 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 from the MSigDB collection

Most of our microarray 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 one-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
S Bergonzi, MC Albani, E Ver Loren van Themaat, K Nordström, R Wang, K Schneeberger, PD Moerland, G Coupland (2013). Mechanisms of age-dependent response to winter temperature in perennial flowering of Arabis alpina. Science, 340: 1094-97. PubMed
C Helbig, R Gentek, RA Backer, Y de Souza, IA Derks, E Eldering, K Wagner, D Jankovic, T Gridley, PD Moerland, RA Flavell, D Amsen (2012). Notch controls the magnitude of CD4+ T cell responses by promoting cellular longevity. PNAS, 109(23):9041-46. PubMed
KML Hertoghs, PD Moerland, A van Stijn, EBM Remmerswaal, SL Yong, PJEJ van de Berg, SM van Ham, IJM ten Berge and RAW van Lier (2010). Molecular profiling of cytomegalovirus-induced human CD8+ T cell differentiation. Journal of Clinical Investigation, 120(11):4077-90. PubMed

This topic: BioLab > WebHome > SupportBioLab > MicroarrayBioLab
Topic revision: 27 May 2020, UnknownUser
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