A powerful score-based statistical test for group difference in weighted biological networks.

ABSTRACT:

Background

Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes.

Results

Simulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One applica

SUBMITTER: Ji J 

PROVIDER: S-EPMC4751708 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature