molecular phenotypes

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 provides the Molecular Signatures Database (MSigDB) in a R accessible objects. Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object.

A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample’s gene expression profile. It scores the expression activities of gene sets at a single-sample level.

This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis.

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.

In this work, we developed models to predict drug response in cancer patients using cell line models

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