title: Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples.
abstract: BACKGROUND
  Pathway enrichment analysis is a useful tool to study biology and biomedicine, due to its functional screening on well-defined biological procedures rather than separate molecules. The measurement of malfunctions of pathways with a phenotype change, e.g., from normal to diseased, is the key issue when applying enrichment analysis on a pathway. The differentially expressed genes (DEGs) are widely focused in conventional analysis, which is based on the great purity of samples. However, the disease samples are usually heterogeneous, so that, the genes with great differential expression variance (DEVGs) are becoming attractive and important to indicate the specific state of a biological system. In the context of differential expression variance, it is still a challenge to measure the enrichment or status of a pathway. To address this issue, we proposed Integrative Enrichment Analysis (IEA) based on a novel enrichment measurement.
  
  
  RESULTS
  The main competitive ability of IEA is to identify dysregulated pathways containing DEGs and DEVGs simultaneously, which are usually under-scored by other methods. Next, IEA provides two additional assistant approaches to investigate such dysregulated pathways. One is to infer the association among identified dysregulated pathways and expected target pathways by estimating pathway crosstalks. The other one is to recognize subtype-factors as dysregulated pathways associated to particular clinical indices according to the DEVGs' relative expressions rather than conventional raw expressions. Based on a previously established evaluation scheme, we found that, in particular cohorts (i.e., a group of real gene expression datasets from human patients), a few target disease pathways can be significantly high-ranked by IEA, which is more effective than other state-of-the-art methods. Furthermore, we present a proof-of-concept study on Diabetes to indicate: IEA rather than conventional ORA or GSEA can capture the under-estimated dysregulated pathways full of DEVGs and DEGs; these newly identified pathways could be significantly linked to prior-known disease pathways by estimated crosstalks; and many candidate subtype-factors recognized by IEA also have significant relation with the risk of subtypes of genotype-phenotype associations.
  
  
  CONCLUSIONS
  Totally, IEA supplies a new tool to carry on enrichment analysis in the complicate context of clinical application (i.e., heterogeneity of disease), as a necessary complementary and cooperative approach to conventional ones.
authors.affiliation: School of Mathematics, Shandong University, Jinan, 250100, China. graceyu1985@163.com.
authors.forename: Xiangtian
authors.initials: X
authors.lastname: Yu
authors.affiliation: Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Cell Building Level 3, YueYang Road 320, Shanghai, 200031, China. zengtao@sibs.ac.cn.
authors.forename: Tao
authors.initials: T
authors.lastname: Zeng
authors.affiliation: School of Mathematics, Shandong University, Jinan, 250100, China. guojunsdu@gmail.com.
authors.forename: Guojun
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies / Q000379:methods; D006801:Humans; D008954:Models, Biological; D015398:Signal Transduction