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.
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 genomic, metabolomic, and microbiomic data. Our expertise ranges from microarrays to next-generation sequencing platforms for genomics, and includes various platforms for metabolomics and microbiomics.More on Omics
Software & 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.
Paper on drug response prediction accepted08-06-2020
A new article titled 'Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data' has been accepted for publication to Biometrical Journal. An R package that implements the proposed model is available as NIG.Read more
Paper accepted: starnet18-05-2020
The paper "Predictive and interpretable models via the stacked elastic net" by Armin Rauschenberger, Enrico Glaab and Mark van de Wiel has been accepted for publication by Bioinformatics.