Group Leader and Senior ScientistEdit
The Huber group studies biological systems by developing statistical and bioinformatic methods for the analysis of new data types and large systematic datasets: single-cell profiling, multi-omics, high-throughput drug- or CRISPR-based perturbation assays, and quantitative imaging. Our projects range from applied data analysis for biological discovery to theoretical method development. Our biological systems of interest from fundamental models of tissue biology to blood cancers. We maintain an extensive network of collaborations. These include the Molecular Medicine Partnership Unit (MMPU) ‘Systems Medicine of Cancer Drugs’, the ERC Synergy project DECODE, the ELLIS unit Heidelberg, and our contributions to the Bioconductor project.
We develop computational methods needed to master big data sets, and we address scientific questions in fundamental biology and precision medicine. We employ statistics and machine learning to discover patterns in data, understand mechanisms, and to build and investigate models. The interdisciplinary team comprises researchers from quantitative disciplines – mathematics, statistics, physics and computer science – and different fields of biology and medicine. Our work pursues three principal aims:
Genomics and other molecular profiling technologies have produced increasingly detailed biology-based understanding of human health and disease. The next challenge is using this knowledge to engineer treatments and cures. To this end, we integrate observational data, such as from large-scale sequencing and molecular profiling, with interventional data, such as from systematic genetic or chemical screens, to reconstruct a fuller picture of the underlying causal relationships and actionable intervention points. A fascinating example is our collaboration on molecular mechanisms of individual sensitivity and resistance of tumours to treatments in our precision oncology project together with Thorsten Zenz at University Hospital Zurich and Sascha Dietrich at University Hospital Heidelberg.
As we engage with new data types, our aim is to develop high-quality computational methods of wide applicability. We consider the release and maintenance of scientific software an integral part of doing science, and we contribute to the Bioconductor Project, an open source software collaboration to provide tools for the analysis and understanding of genome-scale data. An example is our DESeq2 package for analysing count data from high-throughput sequencing.
We aim to enable exploitation of new data types and new types of experiments and studies by developing the computational techniques needed to turn raw data into biology.