Making sense of Omics

Statistics for Omics

Welcome to the site of the Statistics for Omics unit! Our aim is to link omics to clinical response by novel, problem-specific statistical methods.

As part of the Department of Epidemiology and Biostatistics of the VU University Medical Center, our unit is involved in consultancy, research and teaching. More information about who we are, our work and how to reach us can be found in these pages.

Statistics for Omics


Networks                                                                                                                                                                              Our research generates methods to learn molecular network from omics data. In particular, identifying (parts of these) networks to be differential between disease stages is a first step towards network medicine.

Integrated analysis of omics datasets 
Our research also involves methods to unravel associations between different types of molecular profiles. These make use of many molecular features at the same time, and are ideal to be used in genome-wide studies.

Clinical prediction using co-data 
Omics data is 2 x Big data: a) number of features ánd b) sources of auxiliary data: co-data. We develop methods that jointly use co-data and the main data, rendering better predictions and markers for several applications.

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Omics data refers to the high-throughput quantification of some pool of molecular molecules. Often, these data have more features than observations. Our group provides statistical support for the processing and analysis of a wide variety of omics data, such as genomicmetabolomic, and microbiomic data. Our expertise ranges from microarrays to next-generation sequencing platforms for genomics, and includes various platforms for metabolomics and microbiomics.

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Software & Support

Research Support
Omics data analysis support is core business for our group. We supply tailored solutions for a variety of omics data analysis questions in the VUmc, covering study design, preprocessing and downstream analysis. Our focus is cancer genomics, but our support extends towards others diseases, like Alzheimer.

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Software is the tool for disseminating our research. We have contributed >15 R packages (>30,000 downloads per year) to well-known public repositories like CRAN, Bioconductor and Github.

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Latest News



We are proud to announce that the proposal "Co-data random forest learning for rare tumors" has been granted by the Hanarth Foundation for the call "Artificial Intelligence for Oncology".  


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Integration symposium


The next symposium on methods for (Integrated) Analysis of complex and multi-omics data will be held on September 30th 2019 at the Amsterdam UMC, location VUmc, room De IJssel (hospital building, ZH 2  E14)

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4 accepted papers


Armin Rauschenberger, et al. (2019). Sparse regression with paired covariates. Adv Data Analys Class

Carel Peeters, et al. (2019). The spectral condition number plot for regularization parameter evaluation. Comp Statist

Jurre Veerman, et al. (2019). Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear models. Comm Statist Sim Comp 

Soufiane Mourragui, et al. (2019). PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinformatics 

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