Biology subjects

Mariappan, R.

Publications and source records attributed to Mariappan, R..

3 recordsLinked to original sources

bioRxiv2022

scMoMaT: Mosaic integration of single cell multi-omics matrices using matrix trifactorization

Single cell data integration methods aim to integrate cells across data batches and modalities, and obtain a comprehensive view of the cells. Single cell data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. We also show that integrated cell embedding combined with learned bio-markers leads to cell type annotations of higher quality or resolution compared to their original annotations.

bioinformatics↗

bioRxiv2022

Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data

MotivationIn many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative representations that can be used in downstream tasks such as clustering or relation prediction with standard machine learning models. Previous CMF-based methods have numerous modeling limitations. They do not adequately capture complex non-linear interactions and do not explicitly model varying sparsity and noise levels in the inputs, and some cannot model inputs with multiple datatypes. These inadequacies limit their use on many biomedical datasets. ResultsTo address these limitations, we develop Neural Collective Matrix Factorization (NCMF), the first fully neural approach to CMF. We evaluate NCMF on two relation prediction tasks, gene-disease association prediction and adverse drug event prediction, using multiple datasets. In each case, data is obtained from heterogeneous publicly available databases, and used to learn representations to build predictive models. NCMF is found to outperform previous CMF-based methods and state-of-the-art graph embedding methods for representation learning in our experiments. Our experiments illustrate the versatility and efficacy of NCMF for seamless integration of heterogeneous data. Availabilityhttps://github.com/ncmfsrc/ncmf Contactvaibhav.rajan@nus.edu.sg

bioinformatics↗

bioRxiv2019

Deep Augmented Multiview Clustering

We develop a deep learning based method to cluster arbitrary collections of matrices. Our method co-clusters each matrix in the input collection and also associates clusters across matrices thereby enabling discovery of cluster chains. We present preliminary findings on a clinical dataset.

bioinformatics↗