Rohbeck, M. | Search bioRxiv

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↗

bioRxiv2025

Decoding Plasticity Regulators and Transition Trajectories in Glioblastoma with Single-cell Multiomics

Glioblastoma (GB) is one of the most lethal human cancers, marked by profound intratumoral heterogeneity and near-universal treatment resistance. Cellular plasticity, the capacity of cancer cells to transition between phenotypic states, drives GB progression and resistance. However, the regulatory logic that permits or restricts specific state transitions remains poorly understood. Here, we integrated single-nucleus RNA and chromatin accessibility multi-ome profiles from over one million cells across primary IDH-wildtype GBs and developed scDORI, a scalable deep-learning framework to infer enhancer-driven gene regulatory networks (eGRNs) at single-cell resolution. Our analysis revealed a structured hierarchy of GB cell states governed by distinct regulatory programs, with marked variability in epigenetic plasticity that enables or constrains transitions. Neuronal-like tumor cells emerge as a low plasticity state that deploys active repression, in contrast to more permissive progenitor-like and astrocytic states. We identified the neuronal-like state-specific repressor MYT1L as a key regulator that silences master transcription factors of alternative states. MYT1L gain-of-function in patient-derived GB cells reduced chromatin accessibility, induced neuronal-like identity, and restricted proliferation and invasion in vivo, whereas loss-of-function reactivated plasticity and accelerated malignant features. Our findings delineate the epigenetic architecture and associated transcriptional master regulators that shape GB state trajectories, and establish safeguard repressors such as MYT1L as potential therapeutic targets to constrain malignant plasticity.

cancer biology↗