ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis.
Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther).
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Junwei
J
Han
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Xinrui
X
Shi
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Yunpeng
Y
Zhang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Yanjun
Y
Xu
College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin 150040, PR China.
Ying
Y
Jiang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Chunlong
C
Zhang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Li
L
Feng
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Haixiu
H
Yang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Desi
D
Shang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Zeguo
Z
Sun
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Fei
F
Su
School of Medical Informatics, Daqing Campus, Harbin Medical University, Harbin, 150081, PR China.
Chunquan
C
Li
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
Xia
X
Li
D000465:Algorithms; D015179:Colorectal Neoplasms / Q000235:genetics; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D058977:Molecular Sequence Annotation; D012372:ROC Curve; D012984:Software; D059467:Transcriptome; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53