  Motivation: Modules in gene coexpression networks (GCN) can be regarded as
gene groups with individual relationships. No studies have optimized module
detection methods to extract diverse gene groups from GCN, especially for data
from clinical specimens. Results: Here, we optimized the flow from
transcriptome data to gene modules, aiming to cover diverse gene relationships.
We found the prediction accuracy of relationships in benchmark networks of
non-mammalian was not always suitable for evaluating gene relationships of
human and employed network based metrics. We also proposed a module detection
method involving a combination of graphical embedding and recursive
partitioning, and confirmed its stable and high performance in biological
plausibility of gene groupings. Analysis of differentially ex-pressed genes of
several reported cancers using the extracted modules successfully added
relational information consistent with previous reports, confirming the
usefulness of our framework.
