Huber Group

Quantitative Biology and Statistics

The Huber group develops statistical methods for modern biotechnologies, applies them to biological discovery, and translates them into reusable tools.

Go to https://www.huber.embl.de/ for more.

Our team studies biological systems by developing statistical and computational methods for the analysis of new data types and novel, large systematic datasets. These include single-cell and spatial omics, high-throughput drug- and CRISPR-based perturbation assays, and quantitative imaging. Projects range from applied data analysis for biological discovery to theoretical method development. We study fundamental biological model systems, as well as clinical samples for direct applications in biomedicine and precision oncology. 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 the Bioconductor project.

Our interdisciplinary team comprises researchers from quantitative disciplines – such as physics, mathematics, statistics, computer science – and different fields of biology and medicine. We employ statistics and machine learning to discover patterns in data, understand mechanisms, and to build and investigate models. We pursue three main aims:

  • Develop and improve new data generating technologies by powering them with the best statistical methods. This includes inference – reasoning with uncertainty, making optimal decisions based on incomplete, noisy or overwhelming data – as well as data exploration and visualization: helping scientists make and discoveries from large, complex datasets that they could not grasp otherwise.
  • Make biological discoveries on drug–gene–environment interaction networks and their dynamical and context-dependent outcome in phenotypes. This includes the use of high-throughput perturbation data, single-cell, spatial, multimodal omics, and imaging.
  • Make statistical methods more widely usable, not only for experts, but for the range of natural scientists. This aim is embodied by our engagement in open source, open science and the Bioconductor project.

Functional precision medicine

Omics and imaging technologies are producing 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 tumors to treatments in our precision oncology project together with Thorsten Zenz at University Hospital Zürich and Sascha Dietrich at University Hospital Düsseldorf.

Open science

As we engage with new data types, we aim to develop high-quality computational methods of wide applicability. We consider the release and maintenance of scientific software an integral part of doing science. 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 analyzing count data from high-throughput sequencing.

Mentoring and career development

Science is an intellectual adventure and a creative process done by people. Their training and professional development is at the center of what we do. Former group members have moved on to rewarding careers: professors, independent group leaders, senior management or professional scientist roles in industry.


We maintain the textbook Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber. The book is available online, for free, as HTML. It was published as a printed book in 2019 by Cambridge University Press.

We run the annual summer school CSAMA—Biological Data Science. It usually takes place in June in Brixen/Bressanone. Here is the webpage of the 2023 edition.

In July 2023, we co-organized the first Biological Data Science Summer School in Ukraine, in Uzhhorod.

Future projects and goals

We aim to exploit new data types and new types of experiments and studies by developing the computational techniques needed to turn raw data into biology.

  • Multi-scale biology in space and time: bringing together different data types and resolutions to find low-dimensional explanations (factors, gradients, clusters, trees and networks) of high-dimensional data, using statistical models, first-principles based theory and machine learning.
  • Driving the use of spatial omics in immunooncology to find and improve treatment options for patients.
  • Multidimensional phenotyping of genetic and drug-based perturbation assays to map context-dependent gene-gene and gene-drug interaction networks.
  • Many powerful mathematical and computational ideas exist but are difficult to access. We aim to translate them into practical methods and software that make a real difference to biomedical researchers. We sometimes term this approach ‘Translational Statistics’.


Summer 2023
Modern Statistics for Modern Biology textbook: online version. There is also a print version published by Cambridge University Press.
Cellular neighborhood analysis of healthy and malignant lymph nodes based on single-cell resolution spatial proteomics by multiplexed immunohistochemistry.

Cluster-free differential expression analysis of sc-RNA-seq data using LEMUR. Paper link.
Comparison of transformations for single-cell RNA-seq data. Paper link.
Ternary plots of relative sensitivities to targeted kinase inhibitors for a cohort of primary tumour samples of chronic lymphocytic leukaemia (CLL). Paper link.

ми з україною 🇺🇦 We stand with Ukraine