Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

ABSTRACT: Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC3071805 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature