Comparison of pattern detection methods in microarray time series of the segmentation clock.

ABSTRACT: While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time

SUBMITTER: Dequeant ML 

PROVIDER: S-EPMC2481401 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature