Advancing graph-based computational approaches to decipher omic signature of diseases

Abstract

Omic signatures of disease are important for personalized treatment because of theheterogeneity of diseases. Despite the advancement of computational tools, there arelimited methods that can capture the latent inter-relationships between the individualcomponents (amino acids, genes) of proteins, transcriptomic profiles. This gap may beaddressed by the graph-based learning approach in a both supervised and unsupervisedway which enables the creation of scientifically driven learning problems on graphs. Weused graph signal processing which implements a range of tools for processing graphsignal that are functions defined over the nodes in a graph. These functions represent theindividual components of a biological unit. Further, these data points at the nodes aretransformed into different spaces in order to bring out the latent features of the biologi-cal unit for downstream analysis. These tools elaborate on traditional signal processingand provide access to several functionalities, including filtering and frequency analysis.In the first contribution, we devised an approach to address the noise in gene-expression profiles based on graph-wavelet driven gene-expression filtering to enhancegene-network inference. By using this approach, we were able to demonstrate howgene regulatory networks of young and elderly lung cells are different. Additionally,we contrasted differences in gene expression in lungs infected with COVID-19 with thepattern of changes in the effect of genes brought on by ageing.In the second contribution, we have proposed a smart graph-based embedding sys-tem in our search engine (ScEpiSearch) which is capable of embedding and provid-ing an integrative visualization of single-cell ATAC-seq profiles from various sourcesregardless of the species from which they originated and batch effect. Our method(scEpiSearch) calculates distance between query cells on the basis of the similaritywith reference expression and epigenome cells. Here, reference cells are selected fromlarge pool of cells based on their statistical significance of match. We demonstrated theiiiutility of our method in studying the lineage of cancer cells (mixed phenotype acuteleukaemia) and understanding their multipotent behaviour, emphasize unique regula-tory patterns in subpopulation of stem cells.In our third contribution, we have developed a novel graph signal processing basedmethodology to predict biophysical properties of proteins. The model utilizes graph-wavelet of physicochemical signals of amino-acid in protein residue networks to modelits biophysical properties. We demonstrate how our approach using graph wavelets canhelp in estimating the possible effect of disease-associated mutations on proteins usingexamples of prediction of globularity and folding rate.