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

Abstract

MotivationIntegrated 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. ResultsWe 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 query samples to be classified. Availability and implementationPACMOS is an open-source R package available at https://github.com/IARCbioinfo/PACMOS and archived on Zenodo at https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette. Supplementary informationSupplementary data are available in separate files. Key messagesO_LIPACMOS enables the projection of query cancer samples into pretrained multi-omic latent factor spaces. C_LIO_LIThe package supports both continuous and discrete classifications. C_LIO_LIPACMOS provides a reproducible, per-sample workflow implemented in an R package. C_LIO_LIPACMOS demonstrates robust performance on pleural mesothelioma and lung neuroendocrine tumor datasets. C_LI