Comparison of statistical methods for subnetwork detection in the integration of
gene expression and protein interaction network
#MMPMID28253853
He H
; Lin D
; Zhang J
; Wang YP
; Deng HW
BMC Bioinformatics
2017[Mar]; 18
(1
): 149
PMID28253853
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BACKGROUND: With the advancement of high-throughput technologies and enrichment
of popular public databases, more and more research focuses of bioinformatics
research have been on computational integration of network and gene expression
profiles for extracting context-dependent active subnetworks. Many methods for
subnetwork searching have been developed. Scoring and searching algorithms
present a range of computational considerations and implementations. The primary
goal of present study is to comprehensively evaluate the performance of different
subnetwork detection methods. Eleven popular methods were selected for
comprehensive comparison. RESULTS: First, taking into account the dependence of
genes given a protein-protein interaction (PPI) network, we simulated microarray
gene expression data under case and control conditions. Then each method was
applied to the simulated data for subnetwork identification. Second, a large
microarray data set of prostate cancer was used to assess the practical
performance of each method. Using both simulation studies and a real data
application, we evaluated the performance of different methods in terms of recall
and precision. CONCLUSIONS: jActiveModules, PinnacleZ and WMAXC performed well in
identifying subnetwork with relative high precision and recall. BioNet performed
very well only in precision. As none of methods outperformed other methods
overall, users should choose an appropriate method based on the purposes of their
studies.
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