Abstract
Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the “over-counting” bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called “Gene Associaqtion Network-based Pathway Analysis” (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.
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Acknowledgements
We thank Lei Bao (University of California, San Diego, USA), Yun Li (SIBS, China), Tao Huang (SIBS, China), Micheal Wu (Harvard University, USA), Brad Efron (Stanford University, USA), Fei He (Shanghai Center for Bioinformatics Technology, China), Yan Feng, Zuoyun Wang, Wenjing Zhang, Jingqi Chen and Junrui Li (SIBS, China) for helpful communications or suggestions. We thank Yijun Gao and Chao Zheng for their help in manuscript preparation. This work was supported by the National Basic Research Program of China (2012CB910800, 2010CB912102), the National Natural Science Foundation of China (30871284, 30971461, 30971643 and 31071113), the Chinese Academy of Sciences (KSCX1-YW-22), and the Science and Technology Commission of Shanghai Municipality (09JC1416300, 09PJ1401000). HJ is a scholar of the Hundred Talents Program of the Chinese Academy of Sciences.
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Supplementary information
Supplementary information, Data S1
Gene functional association network-based pathway gene weighting (PDF 659 kb)
Supplementary information, Data S2
A list of GEO datasets used for gene co-expressions. (PDF 129 kb)
Supplementary information, Data S3
Gene weights for 833 pathways without correction for multi-subunit genes. (PDF 934 kb)
Supplementary information, Data S4
A list of multi-subunit proteins. (PDF 116 kb)
Supplementary information, Data S5
Gene weights for 833 pathways after correction for multi-subunit genes. (PDF 934 kb)
Supplementary information, Table S1
P53 weights in different pathways (PDF 129 kb)
Supplementary information, Table S2
EGFR weights in different pathways (PDF 67 kb)
Supplementary information, Table S3
Pathways significant in P53 dataset by MeanAbs and W-MeanAbs (PDF 67 kb)
Supplementary information, Table S4
Pathways significant in P53 dataset by GSEA and W-GSEA (PDF 126 kb)
Supplementary information, Figure S1
Conserved pathways across breast cancer datasets. (PDF 338 kb)
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Fang, Z., Tian, W. & Ji, H. A network-based gene-weighting approach for pathway analysis. Cell Res 22, 565–580 (2012). https://doi.org/10.1038/cr.2011.149
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DOI: https://doi.org/10.1038/cr.2011.149
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