title: A new estimator of significance of correlation in time series data.
abstract: Many expression array experiments monitor gene activity as an organism goes through some biological process. It is desirable to find genes with similar expression patterns in the resulting time series data. We propose a new simulation approach that assesses the statistical significance of similarity scores between expression patterns. The simulation takes into account the dependence between columns of data.
authors.affiliation: Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: S
authors.initials: S
authors.lastname: Kruglyak
authors.forename: H
authors.initials: H
authors.lastname: Tang
mesh_terms: D002453:Cell Cycle; D003198:Computer Simulation; D005786:Gene Expression Regulation; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D013997:Time Factors