Biology subjects

Walter, F. C.

Publications and source records attributed to Walter, F. C..

2 recordsLinked to original sources

bioRxiv2025

MOFA-FLEX: A Factor Model Framework for Integrating Omics Data with Prior Knowledge

Latent factor models are first-line analysis approaches for single- and multi-omics data, essential for data integration, alignment, and biological signal discovery. To cater for new technologies and experimental designs, bespoke extensions of factor models have been proposed, incorporating spatial structure, temporal dynamics and the noise characteristics of single-cell assays. However, the development of tailored methods and software for individual use cases is laborious and requires advanced statistical and domain expertise, posing a significant barrier to users. To address this, we here propose MOFA-FLEX, a flexible and modular factor analysis framework designed for customisable modelling across diverse multi-omics data scenarios. Built on probabilistic programming, MOFA-FLEX unifies previously isolated extensions of factor analysis - including flexible priors, non-negativity constraints, supervision signals, and alternative data likelihoods - allowing models to be configured declaratively without requiring manual engineering. Additionally, MOFA-FLEX features a novel domain knowledge module to inform and connect latent factors to gene programs. We demonstrate MOFA-FLEX across multiple applications, showing (i) improved robustness in recovering gene programs from noisy prior knowledge in scRNA-seq data; (ii) effective disentanglement of technical and biological variation in multi-omic CITE-seq; and (iii) tailored spatial modelling that reveals spatially organised disease-associated gene programs in breast cancer.

bioinformatics↗

bioRxiv2021

FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution

MotivationFactor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput data sets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shares information across a potentially large number of cells in a common weight matrix, allowing consistent interpretation of factors across cells and yielding improved latent variable estimates. ResultsWe compare FISHFactor to existing methods that rely on aggregating information through spatial binning and cannot combine information from multiple cells, and show that our method leads to more accurate results on simulated data. We demonstrate on a real data set that FISHFactor is able to identify major subcellular expression patterns and spatial gene clusters in a data-driven manner. Availability and ImplementationThe model implementation, data simulation and experiment scripts are available under https://www.github.com/bioFAM/FISHFactor. Contactb.velten@dkfz.de

bioinformatics↗