PACMOS: an R package for Projection And Classification of Multi-Omic Samples

Abstract

Motivation: Integrated multi-omic analyses have transformed our understanding of cancer biology, giving rise to data-driven molecular classifications that capture disease heterogeneity beyond conventional histopathology. Among these approaches, multi-omic factor analysis (MOFA), a multimodal extension of principal component analysis, has been widely used to identify sources of molecular variation across omic layers and classify samples into molecular groups. However, classifying query samples according to an existing MOFA-based classification remains challenging, as there is no validated computational method for projecting samples into pretrained MOFA latent factor spaces. Results: We present PACMOS, an R package that provides a generalizable approach to project query samples into pretrained MOFA latent factor spaces. We validate PACMOS using two cancer datasets with published MOFA-based classifications - lung neuroendocrine neoplasms and pleural mesothelioma - showing that PACMOS preserves the existing MOFA latent factor space while allowing to classify query samples. Availability and implementation: PACMOS is an open-source R package available on the IARC bioinformatics GitHub organization (submitted to Bioconductor) at https://github.com/IARCbioinfo/PACMOS and DOI in Zenodo: https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette with an application. Supplementary information: Supplementary data are available in separate files.

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• biorxiv v1 2026-07-07 source ↗

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