IPEAP: Integrating multiple omics and genetic data for pathway enrichment analysis

Haoqi Sun, Haiping Wang, Ruixin Zhu, Kailin Tang, Qin Gong, Juan Cui, Zhiwei Cao, Qi Liu

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in highthroughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods.

Original languageEnglish (US)
Pages (from-to)737-739
Number of pages3
Issue number5
StatePublished - Mar 2014
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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