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Nature Methods|September 14, 2023
Principles and challenges of modeling temporal and spatial omics dataBritta Velten, Oliver StegleBioinformatics (Oxford, England)|April 11, 2023
FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolutionFlorin C Walter, Oliver Stegle, Britta VeltenGenome Biology|May 13, 2020
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell dataRicard Argelaguet, Damien Arnol, Danila Bredikhin, et al.Nature Methods|January 14, 2022
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTOBritta Velten, Jana M Braunger, Ricard Argelaguet, et al.Molecular Systems Biology|June 22, 2018
Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data setsRicard Argelaguet, Britta Velten, Damien Arnol, et al.Biostatistics (Oxford, England)|October 10, 2019
Adaptive penalization in high-dimensional regression and classification with external covariates using variational BayesBritta Velten, Wolfgang HuberGenome Biology|February 26, 2016
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expressionBarbara Rakitsch, Oliver StegleBioinformatics (Oxford, England)|September 6, 2024
Guide assignment in single-cell CRISPR screens using crispatJana M Braunger, Britta VeltenGenome Biology|February 2, 2022
MUON: multimodal omics analysis frameworkDanila Bredikhin, Ilia Kats, Oliver StegleNature Communications|June 26, 2015
A random forest approach to capture genetic effects in the presence of population structureJohannes Stephan, Oliver Stegle, Andreas BeyerPageof 18