Yi Chen

supplementary materials

Data and experimental methods to support the work in the following paper:

Establishing Consensus Annotation for the Hallmarks of Cancer, 2020, Yi Chen, F.J.Verbeek and K.Wolstencroft, in submission

This investigation serves as supplementary material for a SWAT4HCLS publication that describes minimum metadata and provenance requirements for reproducible enrichment analysis results.

Functional enrichment analysis is an essential downstream process in high throughput omics studies, such as transcriptomics and proteomics. By using the Gene Ontology (GO) and its annotations (GOA), underlying functional patterns of over-representation can be identified, leading to ...

The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. In this investigation we compare cancer hallmark mapping strategies from different studies, based on Gene Ontology and biological pathway annotation. By analysing the semantic ...

This study describes the results of a survey on enrichment analysis tool usage and provenance reporting for a corpus of SARS-CoV2 data.

The hallmarks mapping schemes under comparison were developed over the period of 7 years and therefore were developed using different versions of the Gene Ontology and associated annotation. Understanding which differences between mapping schemes were the result of topological or annotation changes to GO could therefore help to further refine consensus and make results and conclusions more comparable between studies.

Multiple studies have devised mapping schemes to associate cancer hallmarks with Gene Ontology terms and biological pathway. This study compares the similarities and differences between them, in order to establish consensus knowledge.

This study examines how different hallmark gene datasets intersect with prognostic cancer genes

A Jaccard Index of the overlap between prognostic and hallmark genes for 17 cancer types across different mapping schemes. The impact of selecting different mapping schemes was assessed by pairwise comparisons where there were 5 or more shared genes.

this assay include the hub genes of modules from different mapping schemes with highly functional similarities.

This file provides a full list of the PubMed IDs used as input to our survey of SARS-CoV2 enrichment analysis results

A list of the search terms used for identifying SARS-CoV2 studies involving enrichment analysis. A random sample of 100 papers were used as input to the experiment.

1222 patients

For each module, GSEA analysis was conducted by using web-tool g:profiler and only biological process (BP) were retained. Parameters for GSEA are default.

Calculated by using R package GOSemSim.

Modules were identified by WGCNA. hub genes of each modules were identified based on intramodular degree.

Including 91 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 289 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 277 genes and 1222 Breast cancer patients. This is an input data for WGCNA.

Including 294 genes and 1222 Breast cancer Patient. This is an input data for WGCNA

Consensus between selected mapping methods.

Additional file 2

Additional File 1

Hallmark gene sets belong to different mapping schemes.

derived from Ulhen's research published in 2017.

The excel presents the mapping from GO terms to individual cancer hallmarks retrieved from selected papers.

It includes annotation number data at June 2012, June 2016 and Jan 2021.

Figures showing the changes of edges and terms.

Prov-O Representation for an enrichment analysis experiment on differentially expressed RNA-Seq data, analysed with Enrichr and 2021 version of GO. Rectangle represents Activity, eclipse represents entity and rhombus represents agents