Please select the desired project time frame:
- July 2026
- January 2026
- July 2025
- January 2025
- July 2024
- January 2024
- July 2023
- January 2023
- July 2022
- January 2022
- July 2021
- January 2021
- July 2020
- January 2020
- July 2019
- January 2019
- July 2018
- January 2018
- July 2017
- January 2017
- July 2016
- January 2016
- July 2015
- January 2015
- July 2014
- January 2014
- July 2013
- January 2013
- July 2012
- January 2012
- July 2011
- January 2011
- July 2010
- January 2010
- July 2009
- January 2009
- July 2008
- January 2008
- July 2007
- January 2007
- July 2006
- January 2006
- July 2005
- January 2005
- July 2004
- January 2004
- July 2003
- January 2003
- July 2002
- January 2002
- July 2001
- January 2001
Start of funding 01.01.2019
Scalable differential expression analysis for computational pipelines of the Human Cell Atlas
Prof. Dr. Fabian Theis
Technische Universität München
Dept. of Mathematik - Institute of computational biology (ICB)
Joshua Batson
Chan Zuckerberg Biohub
Several noise models and optimizers and implementations thereof have been proposed for differential expression analysis both for bulk and for single-cell RNA-seq. We present diffxpy, a software framework designed to integrate multiple noise models and optimizers for differential expression analysis which addresses three current issues in the field: 1) Computation speed and memory requirements on large data sets and single-cell-specific testing scenarios, 2) side-by-side comparison of noise models in a single framework (“model zoo”) to avoid confounding results with optimization failures and 3) a novel model to perform batch correction in cases of perfect confounding which integrates into multiple previously proposed models. Diffxpy is based on the high-performance computing framework tensorflow and provides a software basis that easily allows the integration of noise models so that it can easily adapt to changes in the field in the future. We collaborate in this project with the team of the Chan Zuckerberg BioHub in San Fransico to adapt the software and models to the needs of the Human Cell Atlas community and to organize its deployment to this large user base.