networks

vissE.Cloud is a web-server that can help identify, visualise and explore higher-order biological processes from the results of a gene-set enrichment analysis (GSEA). It provides end-to-end analysis of scRNA-seq (10x Chromium) and spatial transcriptomics (10x Visium, 10x Xenium, and NanoString CosMx) datasets.

This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease).

This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

This workshop presents our vissE tool and shows how protein-protein interaction networks derived from it can be further interpreted using the Cytoscape network analysis tool.

This talk presents a simple mathematical model of gene regulation used to simulate expression data to evaluate differential co-expression network inference methods.

vissE is a tool that can help identify, visualise and explore higher-order biological processes from the results of a gene-set enrichment analysis (GSEA)

A workshop on using vissE to analyse transcriptomic data

A talk on using vissE to analyse transcriptomic data

This masterclass includes an introductory lecture on gene-set enrichment analysis followed by a demonstration of the vissE and singscore tools we developed.

A talk on the inner workings of vissE