Velten Group Multi-factorial data analysis & machine learning for the life sciences

Publications

Here you can find our recent publications.

Selected

Multi Omics Factor Analysis a framework for unsupervised integration of multi omics data sets
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets
Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, Oliver Stegle
Molecular Systems Biology  ·  18 Jun 2018  ·  doi:10.15252/msb.20178124
MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion
multi-modal-data-integration probabilistic-factor-models multi-omics
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
Britta Velten, Jana M. Braunger, Ricard Argelaguet, Damien Arnol, Jakob Wirbel, Danila Bredikhin, Georg Zeller, Oliver Stegle
Nature Methods  ·  13 Jan 2022  ·  doi:10.1038/s41592-021-01343-9
MEFISTO is a unsupervised method to integrate multi-modal data with continuous structures among the samples, e.g. in spatial or temporal data.
multi-modal-data-integration probabilistic-factor-models spatio-temporal-omics
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution
Florin C Walter, Oliver Stegle, Britta Velten
Bioinformatics  ·  11 Apr 2023  ·  doi:10.1093/bioinformatics/btad183
FISHFactor identifies spatial gene expression patterns at subcellular resolution
probabilistic-factor-models spatial-omics
Guide assignment in single-cell CRISPR screens using crispat
Guide assignment in single-cell CRISPR screens using crispat
Jana M Braunger, Britta Velten
Bioinformatics  ·  01 Sep 2024  ·  doi:10.1093/bioinformatics/btae535
crispat is a Python framework for assigning cells to guides in pooled single cell CRISPR assays
crispr guide-assignment

All

2024

A genome-scale single cell CRISPRi map of i trans i gene regulation across human pluripotent stem cell lines
A genome-scale single cell CRISPRi map oftransgene regulation across human pluripotent stem cell lines
Claudia Feng, Elin Madli Peets, Yan Zhou, Luca Crepaldi, Sunay Usluer, …, Bo Fussing, David Burke, Leopold Parts, Oliver Stegle, Britta Velten
Cold Spring Harbor Laboratory  ·  28 Nov 2024  ·  doi:10.1101/2024.11.28.625833
Guide assignment in single-cell CRISPR screens using crispat
Guide assignment in single-cell CRISPR screens using crispat
Jana M Braunger, Britta Velten
Bioinformatics  ·  01 Sep 2024  ·  doi:10.1093/bioinformatics/btae535
crispat is a Python framework for assigning cells to guides in pooled single cell CRISPR assays
crispr guide-assignment
In situ analysis of osmolyte mechanisms of proteome thermal stabilization
In situ analysis of osmolyte mechanisms of proteome thermal stabilization
Monika Pepelnjak, Britta Velten, Nicolas Näpflin, Tatjana von Rosen, Umberto Capasso Palmiero, …, Paolo Arosio, Eilika Weber-Ban, Natalie de Souza, Wolfgang Huber, Paola Picotti
Nature Chemical Biology  ·  29 Feb 2024  ·  doi:10.1038/s41589-024-01568-7

2023

The changing career paths of PhDs and postdocs trained at EMBL
The changing career paths of PhDs and postdocs trained at EMBL
Junyan Lu, Britta Velten, Bernd Klaus, Mauricio Ramm, Wolfgang Huber, Rachel Coulthard-Graf
eLife  ·  23 Nov 2023  ·  doi:10.7554/elife.78706
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
Ricardo Omar Ramirez Flores, Jan David Lanzer, Daniel Dimitrov, Britta Velten, Julio Saez-Rodriguez
eLife  ·  22 Nov 2023  ·  doi:10.7554/elife.93161
Principles and challenges of modeling temporal and spatial omics data
Principles and challenges of modeling temporal and spatial omics data
Britta Velten, Oliver Stegle
Nature Methods  ·  14 Sep 2023  ·  doi:10.1038/s41592-023-01992-y
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution
Florin C Walter, Oliver Stegle, Britta Velten
Bioinformatics  ·  11 Apr 2023  ·  doi:10.1093/bioinformatics/btad183
FISHFactor identifies spatial gene expression patterns at subcellular resolution
probabilistic-factor-models spatial-omics
Spatial multiomics map of trophoblast development in early pregnancy
Spatial multiomics map of trophoblast development in early pregnancy
Anna Arutyunyan, Kenny Roberts, Kevin Troulé, Frederick C. K. Wong, Megan A. Sheridan, …, Omer Ali Bayraktar, Ashley Moffett, Oliver Stegle, Margherita Y. Turco, Roser Vento-Tormo
Nature  ·  29 Mar 2023  ·  doi:10.1038/s41586-023-05869-0

2022

Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
Britta Velten, Jana M. Braunger, Ricard Argelaguet, Damien Arnol, Jakob Wirbel, Danila Bredikhin, Georg Zeller, Oliver Stegle
Nature Methods  ·  13 Jan 2022  ·  doi:10.1038/s41592-021-01343-9
MEFISTO is a unsupervised method to integrate multi-modal data with continuous structures among the samples, e.g. in spatial or temporal data.
multi-modal-data-integration probabilistic-factor-models spatio-temporal-omics

2021

Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes.
Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes.
Britta Velten, Wolfgang Huber
Biostatistics (Oxford, England)  ·  10 Apr 2021  ·  pmid:31596468

2020

Developmental Gene Expression Differences between Humans and Mammalian Models.
Developmental Gene Expression Differences between Humans and Mammalian Models.
Margarida Cardoso-Moreira, Ioannis Sarropoulos, Britta Velten, Matthew Mort, David N Cooper, Wolfgang Huber, Henrik Kaessmann
Cell reports  ·  27 Oct 2020  ·  pmid:33113372
Survey of ex vivo drug combination effects in chronic lymphocytic leukemia reveals synergistic drug effects and genetic dependencies.
Survey of ex vivo drug combination effects in chronic lymphocytic leukemia reveals synergistic drug effects and genetic dependencies.
Marina Lukas, Britta Velten, Leopold Sellner, Katarzyna Tomska, Jennifer Hüellein, …, Alexander Jethwa, Hanibal Bohnenberger, Junyan Lu, Wolfgang Huber, Thorsten Zenz
Leukemia  ·  13 May 2020  ·  pmid:32404973
MOFA : a statistical framework for comprehensive integration of multi-modal single-cell data.
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.
Ricard Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, Britta Velten, John C Marioni, Oliver Stegle
Genome biology  ·  11 May 2020  ·  pmid:32393329
Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.
Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.
Ignacio L Ibarra, Nele M Hollmann, Bernd Klaus, Sandra Augsten, Britta Velten, Janosch Hennig, Judith B Zaugg
Nature communications  ·  08 Jan 2020  ·  pmid:31913281

2019

Gene expression across mammalian organ development.
Gene expression across mammalian organ development.
Margarida Cardoso-Moreira, Jean Halbert, Delphine Valloton, Britta Velten, Chunyan Chen, …, Wolfgang Huber, Julie Baker, Simon Anders, Yong E Zhang, Henrik Kaessmann
Nature  ·  26 Jun 2019  ·  pmid:31243369

2018

Multi Omics Factor Analysis a framework for unsupervised integration of multi omics data sets
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets
Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, Oliver Stegle
Molecular Systems Biology  ·  18 Jun 2018  ·  doi:10.15252/msb.20178124
MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion
multi-modal-data-integration probabilistic-factor-models multi-omics

2017

Drug-perturbation-based stratification of blood cancer.
Drug-perturbation-based stratification of blood cancer.
Sascha Dietrich, Małgorzata Oleś, Junyan Lu, Leopold Sellner, Simon Anders, …, Jan Dürig, Ingo Ringshausen, Marc Zapatka, Wolfgang Huber, Thorsten Zenz
The Journal of clinical investigation  ·  11 Dec 2017  ·  pmid:29227286