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IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering

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Listed:
  • Lingxiang Wu
  • Xiujie Chen
  • Denan Zhang
  • Wubing Zhang
  • Lei Liu
  • Hongzhe Ma
  • Jingbo Yang
  • Hongbo Xie
  • Bo Liu
  • Qing Jin

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.

Suggested Citation

  • Lingxiang Wu & Xiujie Chen & Denan Zhang & Wubing Zhang & Lei Liu & Hongzhe Ma & Jingbo Yang & Hongbo Xie & Bo Liu & Qing Jin, 2016. "IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0164542
    DOI: 10.1371/journal.pone.0164542
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    References listed on IDEAS

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    1. Yolanda Sánchez & Victor Segura & Oskar Marín-Béjar & Alejandro Athie & Francesco P. Marchese & Jovanna González & Luis Bujanda & Shuling Guo & Ander Matheu & Maite Huarte, 2014. "Genome-wide analysis of the human p53 transcriptional network unveils a lncRNA tumour suppressor signature," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
    2. Rahnenführer Jörg & Domingues Francisco S & Maydt Jochen & Lengauer Thomas, 2004. "Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-31, June.
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