title: IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering.
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.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Lingxiang
authors.initials: L
authors.lastname: Wu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Xiujie
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Denan
authors.initials: D
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Wubing
authors.initials: W
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Ma
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Jingbo
authors.initials: J
authors.lastname: Yang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Xie
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Liu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Jin
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics*; D010190:Pancreatic Neoplasms / Q000235:genetics*; D012984:Software; D013274:Stomach Neoplasms / Q000235:genetics*