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Bioinformatics. 2004 Aug 4;20 Suppl 1:i23-30.

Deconvolving cell cycle expression data with complementary information.

Author information

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. zivbj@cs.cmu.edu

Abstract

MOTIVATION:

In the study of many systems, cells are first synchronized so that a large population of cells exhibit similar behavior. While synchronization can usually be achieved for a short duration, after a while cells begin to lose their synchronization. Synchronization loss is a continuous process and so the observed value in a population of cells for a gene at time t is actually a convolution of its values in an interval around t. Deconvolving the observed values from a mixed population will allow us to obtain better models for these systems and to accurately detect the genes that participate in these systems.

RESULTS:

We present an algorithm which combines budding index and gene expression data to deconvolve expression profiles. Using the budding index data we first fit a synchronization loss model for the cell cycle system. Our deconvolution algorithm uses this loss model and can also use information from co-expressed genes, making it more robust against noise and missing values. Using expression and budding data for yeast we show that our algorithm is able to reconstruct a more accurate representation when compared with the observed values. In addition, using the deconvolved profiles we are able to correctly identify 15% more cycling genes when compared to a set identified using the observed values.

AVAILABILITY:

Matlab implementation can be downloaded from the supporting website http://www.cs.cmu.edu/~zivbj/decon/decon.html

PMID:
15262777
[PubMed - indexed for MEDLINE]
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