Comparing the similarity of time-series gene expression using signal processing metrics.
Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.
Butte
A J
AJ
Children's Hospital Informatics Program, 300 Longwood Avenue, Boston, Massachusetts 02115, USA. atul_butte@harvard.edu
Bao
L
L
Reis
B Y
BY
Watkins
T W
TW
Kohane
I S
IS
D000465:Algorithms; D019295:Computational Biology; D004268:DNA-Binding Proteins / Q000235:genetics; D005090:Exodeoxyribonucleases / Q000235:genetics; D005656:Fungal Proteins / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005800:Genes, Fungal; D051718:MutS Homolog 2 Protein; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
D004268:DNA-Binding Proteins; D005656:Fungal Proteins; D029701:Saccharomyces cerevisiae Proteins; D005090:Exodeoxyribonucleases; C028450:exodeoxyribonuclease I; C489751:MSH2 protein, S cerevisiae; D051718:MutS Homolog 2 Protein