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research.qmd
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research.qmd
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---
title: "Research"
---
We 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.
- Use **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**.
###
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. There is also a [print version published by CUP](https://www.cambridge.org/it/universitypress/subjects/statistics-probability/statistics-life-sciences-medicine-and-health/modern-statistics-modern-biology?format=HB).](photos/MSFMB-Cover2-compressed.jpg){width=240 fig-align="center"}
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.](photos/clusterfreeDE-2048x1649.png){width=240 fig-align="center"}
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.](photos/Transformations-Fig1c.png)
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.](photos/JCI2018-fig_02_l.jpg)
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### 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 Zurich 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.
### Teaching
We maintain the textbook Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber. The book is available [online, for free, as HTML](https://www.huber.embl.de/msmb). It was published as a [printed book in 2019 by Cambridge University Press](https://www.cambridge.org/it/universitypress/subjects/statistics-probability/statistics-life-sciences-medicine-and-health/modern-statistics-modern-biology?format=HB).
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](https://csama2023.bioconductor.eu). See [here for some impressions](https://www.huber.embl.de/group/posts/2023-06-16-csama.html).
In July 2023, we co-organized the first [Biological Data Science Summer School in Ukraine, in Uzhhorod](https://www.bds3.org). See also [Wolfgang's post about it](https://www.huber.embl.de/group/posts/2023-08-onbds3.html).
### Software
We are a frequent contributor to the Bioconductor project
| | |
|--------------|------------------------------------|
|[LEMUR](https://github.com/const-ae/lemur/)|Cluster-free differential expression analysis of multi-condition single-cell data using Latent Embedding Multivariate Regression|
|[MOFA](https://biofam.github.io/MOFA2/)|Multi-Omics Factor Analysis|
|[DESeq2](https://bioconductor.org/packages/DESeq2)|Differential gene expression analysis based on the negative binomial distribution|
|[IHW](https://bioconductor.org/packages/IHW)|Multiple testing and false discovery rate (FDR) control by Independent Hypothesis Weighting|
|[EBImage](https://bioconductor.org/packages/EBImage)|Image processing and analysis toolbox for R|
|[Rarr](https://bioconductor.org/packages/Rarr)|Read Zarr Files in R|
|[rdf5](https://bioconductor.org/packages/rhdf5)|R Interface to HDF5|
|[vsn](https://bioconductor.org/packages/vsn)|Normalization and variance stabilizing transformation of fluorescence intensity data|
|[cellHTS2](https://bioconductor.org/packages/cellHTS2)|Analysis of cell-based high-throughput screens|
|[DEXSeq](https://bioconductor.org/packages/DEXSeq)|Inference of differential exon usage in RNA-Seq|
|[HilbertVis](https://bioconductor.org/packages/HilbertVis)|Visualize long vectors of data using Hilbert curves|
| | |
|**Python**| |
|[HTSeq](https://htseq.readthedocs.io/en/latest/)|Processing and analyzing data from high-throughput sequencing assays|
: {tbl-colwidths="[20,80]"}
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