ArticlePloS one2016
IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Gadoxetic Acid-enhanced MRI Radiomics Features of Tumor Margins for Predicting High-Risk Solitary Hepatocellular Carcinoma Aggressiveness and Prognosis.Radiology. Imaging cancer · 2026Article
- Exploration of common pathogenic genes between cerebral amyloid angiopathy and insomnia based on bioinformatics and experimental validation.Scientific reports · 2025Article
- TMEM106A mediates atherosclerosis progression through macrophage-centered immune responses and chemokine signaling.Frontiers in immunology · 2025Article
- Prognostic risk analysis related to radioresistance genes in colorectal cancer.Frontiers in oncology · 2022Article
- Comprehensive Analysis of Tumor Microenvironment Identified Prognostic Immune-Related Gene Signature in Ovarian Cancer.Frontiers in genetics · 2021Article
- Integrating RNA sequencing into neuro-oncology practice.Translational research : the journal of laboratory and clinical medicine · 2017Review
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Authors and funding
10 authors.
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Abstract
Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.
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