Generalized correlation measure using count statistics for gene expression data with ordered samples.

ABSTRACT:

Motivation

Capturing association patterns in gene expression levels under different conditions or time points is important for inferring gene regulatory interactions. In practice, temporal changes in gene expression may result in complex association patterns that require more sophisticated detection methods than simple correlation measures. For instance, the effect of regulation may lead to time-lagged associations and interactions local to a subset of samples. Furthermore, expression profiles of interest may not be aligned or directly comparable (e.g. gene expression profiles from two species).

Results

We propose a count statistic for measuring association between pairs of gene expression profiles consisting of ordered samples (e.g. time-course), where correlation may only

SUBMITTER: Wang YXR 

PROVIDER: S-EPMC5860612 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature