Abstract
Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
Highlights
Single-cell methods have provided unprecedented opportunities to assay cellular heterogeneity
Building on the Bayesian Group Factor Analysis framework, Multi-Omics Factor Analysis (MOFA) infers a low-dimensional representation of the data in terms of a small number of factors that capture the global sources of variability
MOFA employs Automatic Relevance Determination (ARD), a hierarchical prior structure that facilitates untangling variation that is shared across multiple modalities from variability that is present in a single modality
Summary
Single-cell methods have provided unprecedented opportunities to assay cellular heterogeneity. This is important for studying complex biological processes, including the immune system, embryonic development, and cancer [1,2,3,4]. Seq) [14], single-cell DNA methylation and transcriptome (scM&T-seq) [15], single-cell chromatin accessibility and transcriptome (sci-CAR) [16], and single-cell nucleosome, transcriptome and methylation (scNMT-seq) [17], among others [18,19,20,21,22,23,24] These experimental techniques provide the basis for studying regulatory dependencies between transcriptomic and (epi)-genetic diversity at the single-cell level.
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