Construction and visualization of diseasome of lung diseases associated with COVID-19 from co-association networks of multi-omics data
Creators: name: Reddy Rani Vangimalla
name: Sreevalsan-Nair, Jaya
Publisher: name: F1000 Research Limited
Year: 2020

description: Studying protein-protein interactions has become important for identifying appropriate drugs to fight the disease. COVID-19 is a global pandemic that has affected ~4.5 million people in 213 countries with ~0.3 million fatalities, since December 2019. It has affected livelihoods apart from overwhelming the healthcare systems of these countries in an unprecedented manner. Hence, there is a race against time to identify a cure for COVID-19, caused by the novel coronavirus, SARS-COV-2, a positive-sense single-stranded RNA virus. In this work, we propose a multi-omics and multi-microarray analysis of comorbidities associated with COVID-19, e.g. pneumonia, and acute respiratory distress syndrome (ARDS). We also analyze related lung diseases, such as Lung Squamous Cell Carcinoma and tuberculosis for more insights into molecular-level influences in the disease. The gene-gene association network of these diseases reveals significant genes, gene-pairs, and gene-clusters.<br><br><b>Method</b>: We propose to build correlation matrices between significant genes, which are filtered and<br>collated from the multi-omics data of theses diseases, i.e. the DNA methylation features and mRNA gene<br>expressions. We focus on a set of genes in both the omics data with the same name. These correlation<br>networks are created using partial correlations computed from multiple linear multivariate regression models. Different regression methods have been aggregated for similar work [1]. Here, we use the onsensus method of voting to aggregate the correlation networks to identify a final score between the<br>genes for a single disease. Once we construct the final network of genes for each disease, we use<br>appropriate matrix seriation methods to identify clusters and significant gene pairs. The genes are then<br>gathered to build a diseasome using a significance score to the disease considered. The diseasome now<br>is an unbalanced bipartite graph of the diseases considered and the filtered genes.<br><br>We use visual analytics to identify genes in diseases co-occurring with COVID-19. We use our proposed<br>visualization method for unbalanced bipartite graphs, RadTrix [2], to visualize the diseasome. RadTrix is a composite visualization that uses matrix representation of the smaller set of nodes and circular/radial graph layout for the larger set in the bipartite graph. It follows the nesting composite visualization view design [3]. The matrix layout of nodes follows a two-way one-mode representation of D nodes. Each node on the matrix has four “connection points”, on either ends of the corresponding row as well as column in the matrix. Using the nearest connection point reduces edge length, and thereby reduces clutter. We place the N nodes on the circumference of the circle uniformly.<br><br>We estimate the radius of the circle, r, as max(D×s,4×N) where s is the size of the rendered square cell in<br>the matrix. The matrix and circular layouts of the D and N nodes, respectively, also enable the use of<br>visualization marks (matrix cells, glyphs for nodes, etc.) and channels (color, size, etc.). These marks and<br>channels represent properties pertaining to unary and binary operators within the set of D nodes and<br>unary operators within the N nodes.<br><br><b>Results</b>: Our goal in this work is to then identify appropriate genes as well as gene pairs to target for<br>drug therapy based on the findings from comorbidities associated with COVID-19. For example, ACE-2<br>has been identified as a prominent gene of interest in patients with comorbidities associated with<br>severe COVID-19 [4]. In our work, we look at identifying ACE-2 and similar genes to identify the extent of<br>association of these genes with related diseases. RadTrix visualization helps us in seeing the association of genes to multiple diseases, associations between genes, and associations between diseases.<br><br><b>Conclusion</b>: The visual analytics provide us insights into the co-association of genes and gene-pairs with<br>comorbidities associated with COVID-19 as well as related lung diseases. Using networks systematically<br>reveals the co-expression of genes in multi-omics data, as we perform an integrated analysis of DNA<br>methylation features and mRNA genes. This enables understanding of the role of methylation in gene<br>expression, which further helps in drug design.<br><br><b>References:</b><br> [1] Vangimalla, R. R., Jeong, H.- H ., and Sohn K.-A.: Integrative regression network for genomic association study. BMC Medical Genomics 9, 1 (2016), 31,<br>doi: https://doi.org/10.1186/s12920-016-0192-7 .<br> [2] Vangimalla, R. R., and Sreevalsan-Nair, J.: "RadTrix: A Composite Hybrid Visualization for Unbalanced Bipartite<br>Graphs in Biological Datasets," 9th Eurographics Workshop on Visual Computing for Biology and Medicine, September 2019.<br>https://conferences.eg.org/vcbm2019/wp-content/uploads/sites/2/2019/09/05.pdf<br> [3] Javed W., and Elmqvist N.: Exploring the design space of composite visualization. In 2012 IEEE Pacific Visualization Symposium (2012), IEEE, pp. 1–8.<br> [4] Bruna GG Pinto, Antonio ER Oliveira, Youvika Singh, Leandro Jimenez, Andre NA Goncalves, Rodrigo LT Ogava, Rachel Creighton, Jean PS Peron, and Helder I Nakaya, ACE2 Expression is Increased in the Lungs of Patients with Comorbidities Associated with Severe COVID-19, medRxiv 2020.03.21.20040261; doi: https://doi.org/10.1101/2020.03.21.20040261