%0 Journal Article %1 Wu:2016:PLoS-One:27764138 %A Wu, L %A Chen, X %A Zhang, D %A Zhang, W %A Liu, L %A Ma, H %A Yang, J %A Xie, H %A Liu, B %A Jin, Q %D 2016 %J PLoS One %K GSEA benchmark comparison enrichment fulltext gene-set shouldread software %N 10 %R 10.1371/journal.pone.0164542 %T IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering %U https://www.ncbi.nlm.nih.gov/pubmed/27764138 %V 11 %X 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.

@article{Wu:2016:PLoS-One:27764138, 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.}, added-at = {2018-12-19T12:51:27.000+0100}, author = {Wu, L and Chen, X and Zhang, D and Zhang, W and Liu, L and Ma, H and Yang, J and Xie, H and Liu, B and Jin, Q}, biburl = {https://www.bibsonomy.org/bibtex/2689ac29f0b127783d96b8206bb5055e8/marcsaric}, description = {IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering. - PubMed - NCBI}, doi = {10.1371/journal.pone.0164542}, interhash = {8e55159d830ea568a245aace3228e280}, intrahash = {689ac29f0b127783d96b8206bb5055e8}, journal = {PLoS One}, keywords = {GSEA benchmark comparison enrichment fulltext gene-set shouldread software}, number = 10, pmid = {27764138}, timestamp = {2018-12-19T12:51:27.000+0100}, title = {IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering}, url = {https://www.ncbi.nlm.nih.gov/pubmed/27764138}, volume = 11, year = 2016 }