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2015 ; 11
(11
): e1004574
Nephropedia Template TP
PLoS Comput Biol
2015[Nov]; 11
(11
): e1004574
PMID26618778
show ga
Gene co-expression network analysis has been shown effective in identifying
functional co-expressed gene modules associated with complex human diseases.
However, existing techniques to construct co-expression networks require some
critical prior information such as predefined number of clusters, numerical
thresholds for defining co-expression/interaction, or do not naturally reproduce
the hallmarks of complex systems such as the scale-free degree distribution of
small-worldness. Previously, a graph filtering technique called Planar Maximally
Filtered Graph (PMFG) has been applied to many real-world data sets such as
financial stock prices and gene expression to extract meaningful and relevant
interactions. However, PMFG is not suitable for large-scale genomic data due to
several drawbacks, such as the high computation complexity O(|V|3), the presence
of false-positives due to the maximal planarity constraint, and the inadequacy of
the clustering framework. Here, we developed a new co-expression network analysis
framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA)
by: i) introducing quality control of co-expression similarities, ii)
parallelizing embedded network construction, and iii) developing a novel
clustering technique to identify multi-scale clustering structures in Planar
Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the
gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer
Genome Atlas (TCGA). MEGENA showed improved performance over well-established
clustering methods and co-expression network construction approaches. MEGENA
revealed not only meaningful multi-scale organizations of co-expressed gene
clusters but also novel targets in breast carcinoma and lung adenocarcinoma.
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