  A widely used approach for extracting information from gene expression data
employ the construction of a gene co-expression network and the subsequent
application of algorithms that discover network structure. In particular, a
common goal is the computational discovery of gene clusters, commonly called
modules. When applied on a novel gene expression dataset, the quality of the
computed modules can be evaluated automatically, using Gene Ontology
enrichment, a method that measures the frequencies of Gene Ontology terms in
the computed modules and evaluates their statistical likelihood. In this work
we propose SGC a novel pipeline for gene clustering based on relatively recent
seminal work in the mathematics of spectral network theory. SGC consists of
multiple novel steps that enable the computation of highly enriched modules in
an unsupervised manner. But unlike all existing frameworks, it further
incorporates a novel step that leverages Gene Ontology information in a
semi-supervised clustering method that further improves the quality of the
computed modules. Comparing with already well-known existing frameworks, we
show that SGC results in higher enrichment in real data. In particular, in 12
real gene expression datasets, SGC outperforms in all except one.
