Microarray Analysis Group

People

Maarten van Iterson: statistical tools for design and analysis of microrray experiments (supported by Biorange, the NBIC research programme and the CMSB)
Vacancy: integration of omics data from multiple sources in complex phenotypes and disease (supported by the European FP7 project ENGAGE)
Peter-Bram 't Hoen: superuser and co-developer of microarray analysis tools
Judith Boer: superuser and co-developer of microarray analysis tools

Research

Microarrays are increasingly used in life sciences to investigate molecular processes on a genome-wide scale and to identify biomarkers for molecular diagnostics. We work on different aspects of microarray data analysis, including platform comparison, sample size determination and study design. In addition to expression profiling, we work on the analysis of different types of microarray data, e.g. array-CGH, SNP arrays and methylation arrays. We develop new statistical tools for optimizing study design and for the analysis and integration of microarray experiments and apply them to genomics projects carried out within our institute.

Highlights

We developed a statistical approach to integrate gene expression and genotype (e.g. copy number or SNP) data to identify regions of association and candidate genes for tumorigenesis and complex diseases.
We compared the performance of five different microarray platforms and RNA-Seq with samples from transgenic and wildtype mice. We found good correlation between the different platforms, but also differences related to platform and method: one-color vs two-color and RNA-Seq vs microarrays.
We developed an R package for the estimation of sample size in relation to power, based on pilot data.

Our lectures, courses and course material

To be included on the mailing list for microarray-related lectures and courses organized by the Center for Human and Clinical Genetics, send me an email.

Genomics/Bioinformatics lectures: Tuesdays at lunchtime

including R users group: collection of packages with our experiences and example scripts

Getting started in R: How-To: information for first time R users including several practicals for microarray data analysis
MGC 3-day course "Analysis of microarray gene expression data"

8^th Edition: June 16-19, 2008 at ErasmusMC, Rotterdam

(pre)registration and notification of future editions
Previous courses: lectures and practicals
9^th edition MGC course data

CMSB 3-day course "Microarray analysis using R"

November 2004 course material: lectures and practicals, including introduction to R and Bioconductor.

LUMC microarray analysis infrastructure

Experimental design of microarray analyses including a short introduction to experimental design and designing a microarray experiment
DNA microarray analysis flow chart including publicly available software regularly used at the Center for Human and Clinical Genetics
Software available at the Leiden Genome Technology Center
Rosetta Resolver Server for DNA chip and microarray data storage and analysis (access restricted to registered users from participating institutes)
Microarray-related software tools developed at the Microarray Analysis Group (Marten Boetzer, Maarten van Iterson, Renée X. de Menezes, Judith M. Boer, Peter-Bram 't Hoen):

SIM, Integrated Analysis of gene expression and copynumber data.
SSPA, Power and sample size analysis R package for microarray experiments on the basis of pilot data.
Microarray Retriever: a web-based tool for searching and large scale retrieval of public microarray data.

Microarray-related software tools developed at the Pathology department (Ronald van Eijk, Jan Oosting and Anne-Marie Cleton-Jansen):

Sorting of cDNA microarray data on chromosomal position (Spotfire plug-in)
Tool in R voor cDNA microarray data, selection of valid signals and normalisation in combined high and low intensity scans
exprSetBuilder Excel Addin, to build exprSets to use in R and the Global Test GUI
Global Test GUI, to run Global Test on GO terms and KEGG pathways

Microarray-related software tools developed at the Department of Medical Statistics and Bioinformatics (Jelle Goeman, Paul Eilers):

Globaltest, R/BioC package for testing the association of gene sets with a response
Scattersmooth, for enhancing scatterplots with smoothed densities
Quantsmooth, for quantile smoothing of array-CGH data

Selected publications

van Iterson M, 't Hoen PA, Pedotti P, Hooiveld GJ, den Dunnen JT, van Ommen GJ, Boer JM, Menezes RX. Relative power and sample size analysis on gene expression profiling data. BMC Genomics. 2009 Sep 17;10:439.
Menezes RX, Boetzer M, Sieswerda M, van Ommen GJ, Boer JM. Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics. 2009 Jun 29;10:203.
't Hoen PA, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RH, de Menezes RX, Boer JM, van Ommen GJ, den Dunnen JT. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res. 2008 Dec;36(21):e141.
Ivliev AE, 't Hoen PA, Villerius MP, den Dunnen JT, Brandt BW. Microarray retriever: a web-based tool for searching and large scale retrieval of public microarray data. Nucleic Acids Res. 2008 May 7.
Alagaratnam S, Mertens BJ, Dalebout JC, Deelder AM, van Ommen GJ, den Dunnen JT, 't Hoen PA. Serum protein profiling in mice: identification of Factor XIIIa as a potential biomarker for muscular dystrophy. Proteomics. 2008 Apr;8(8):1552-63.
Pedotti P, 't Hoen PA, Vreugdenhil E, Schenk GJ, Vossen RH, Ariyurek Y, de Hollander M, Kuiper R, van Ommen GJ, den Dunnen JT, Boer JM, de Menezes RX. Can subtle changes in gene expression be consistently detected with different microarray platforms? BMC Genomics. 2008 Mar 10;9:124.
Vinciotti V, Liu X, Turk R, de Meijer EJ, 't Hoen PA. Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data. BMC Bioinformatics. 2006 Apr 3;7:183.
de Graaf JM, de Menezes RX, Boer JM, Kosters WA. Frequent itemsets for genomic profiling. CompLife 2005, Lecture Notes in Computer Science 3695: 104-116.
Eilers PH, de Menezes RX. Quantile smoothing of array CGH data. Bioinformatics. 2005 Apr 1;21(7):1146-53.
Menezes RX, Boer JM, van Houwelingen HC. Microarray data analysis : a hierarchical T-test to handle heteroscedasticity. Appl Bioinformatics. 2004;3(4):229-35.
Cardoso J, Molenaar L, de Menezes RX, Rosenberg C, Morreau H, Moslein G, Fodde R, Boer JM. Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH. Nucleic Acids Res. 2004 Oct 28;32(19):e146.
Turk R, 't Hoen PA, Sterrenburg E, de Menezes RX, de Meijer EJ, Boer JM, van Ommen GJ, den Dunnen JT. Gene expression variation between mouse inbred strains. BMC Genomics. 2004 Aug 18;5(1):57.
't Hoen PA, Turk R, Boer JM, Sterrenburg E, de Menezes RX, van Ommen GJ, den Dunnen JT. Intensity-based analysis of two-colour microarrays enables efficient and flexible hybridization designs. Nucleic Acids Res. 2004 Feb 24;32(4):e41.

If you have any comments or suggestions be sure to let me know ! Judith M. Boer
Last modified: October 30th, 2009 (IF)

Center for Human and Clinical Genetics - LUMC