Safinianaini, N. | Search bioRxiv

bioRxiv2025

OMIDIENT: Multiomics Integration for Cancer by Dirichlet Auto-Encoder Networks

To achieve a more comprehensive understanding of cancer, novel computational methods are required for the integrative analysis of data from different molecular layers, such as genomics, transcriptomics, and epigenomics. Here, we present a novel multi-omics integrative method that performs unsupervised representation learning, referred to as OMIDIENT: multiOMics Integration for cancer by DIrichlet auto-ENcoder neTworks. OMIDIENT provides a natural framework for modeling sparse and compositional latent representations by employing a deep generative model, where the latent space is distributed as the product of Dirichlet distributions. Applied to five different cancers, we demonstrate that OMIDIENT outperforms the top state-of-the-art unsupervised multi-omics integrative analysis approaches in clustering, classification, and reconstruction of missing data using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, we provide interpretability analyses for OMIDIENT that not only support its improved performance, but also offer valuable insights into the underlying structure captured by the learned representations.

bioinformatics↗

bioRxiv2020

CopyMix: Mixture Model Based Single-Cell Clustering and Copy Number Profiling using Variational Inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. Moreover, clonal copy number profiling has been missing except for one method, CONET, which unfortunately computes it by a post-processing tool. Finally, a common copy number profiling tool, HMMcopy, requires parameter tuning. We avoid the clustering artifact issues and provide clonal copy number profiles without the labor of parameter tuning in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. We evaluate CopyMix using simulated data and published biological data from ovarian cancer. The results reveal high clustering performance and low errors in copy number profiling. These favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

bioinformatics↗