Interactive visualisation and exploration of biological data

International audienceno abstrac

DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

This work presents DeepReGraph, a novel method for co-clustering genes and cis-regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as well as data from three CRE activity markers from a publicly available dataset of mouse fetal heart tissue, were used for DeepReGraph concept proofing. In this study we used open chromatin accessibility from ATAC-seq experiments, as well as H3K27ac and H3K27me3 histone marks as CREs activity markers. However, this method can be executed with other sets of markers. We modelled all data sources as a heterogeneous graph and adapted a state-of-the-art representation learning algorithm to produce a low-dimensional and easy-to-cluster embedding of genes and CREs. Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph. The main contribution of our work is the design of proper combination rules for the heterogeneous gene expression and CRE activity data and the computational encoding of well-known gene expression regulatory mechanisms into a suitable objective function for graph embedding. We showed that the co-clusters of genes and CREs in the final embedding shed light on developmental regulatory mechanisms in mouse fetal-heart tissue. Such clustering could not be achieved by using only gene expression data. Function enrichment analysis proves that the genes in the co-clusters are involved in distinct biological processes. The enriched transcription factor binding sites in CREs prioritize the candidate transcript factors which drive the temporal changes in gene expression. Consequently, we conclude that DeepReGraph could foster hypothesis-driven tissue development research from high-throughput expression and epigenomic data. Full source code and data are available on the DeepReGraph GitHub project

GENE EXPRESSION PROSPECTIVE SIMULATION AND ANALYSIS USING DATA MINING AND IMMERSIVE VIRTUAL REALITY VISUALIZATION

Biological exploration on genetic expression and protein synthesis in living organisms is used to discover causal and interactive relationships in biological processes. Current GeneChip microarray technology provides a platform to an- alyze up to 500,000 molecular reactions on a single chip, providing thousands of genetic and protein expression results per test. Using visualization tools and priori knowledge of genetic and protein interactions, visual networks are used to model and analyze the results. The virtual reality environment designed and implemented for this project provides visualization and data modeling tools commonly used in genetic ex- pression data analysis. The software processes normalized genetic profile data from microarray testing results and association information from protein-to- protein databases. The data is modeled using a network of nodes to represent data points and edges to show relationships. This information is visualized in virtual reality and modeled using force directed networking algorithms in a fully explorable environment

Microarray Gene Expression Data Mining using High End Clustering Algorithm based on Attraction-Repulsion Technique

Abstract—Microarray Gene expression data analysis is one of the key domains in the modern cellular and molecular biology system design and analysis; shortly we called it computational simulation of genome-wide expression from DNA hybridization. We present here a high end clustering algorithm basically a technique following the inspiration led by natural attraction and the repulsion processes. It groups the similarly expressed genes in same clusters, co-expressed and differently expressed ones in different clusters. Most importantly, it takes into account of the outliers in an efficient manner by not allowing them to interfere with the similarly expressed gene clusters on the fly. In the first clustering process, it calculates the distances of all the genes in a proximity range set in prior, henceforth attracting all the least distant genes from the seed gene. Varying the proximity range in the subsequent run, repulse the maximally distant genes from the same cluster, thereby achieving a near to perfect cluster formation at the end. We include cluster validity testing using Hubert’s statistics technique, which shows a very optimal clusters validity result

A semi-supervised approach to visualizing and manipulating overlapping communities

When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE