Estimation and inversion of the effects of cell population asynchrony in gene expression time-series

  • Authors:

  • Harri Lähdesmäki;Heikki Huttunen;Tommi Aho;Marja-Leena Linne;Jari Niemi;Juha Kesseli;Ron Pearson;Olli Yli-Harja

  • Affiliations:

  • Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland and MediCel Ltd, Haartmaninkatu 8, FIN-00290 Helsinki, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland

  • Venue:
  • Signal Processing - Special issue: Genomic signal processing
  • Year:
  • 2003

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Abstract

We introduce several approaches to improve the quality of gene expression data obtained from time-series measurements by applying signal processing tools. Performance of the proposed methods are examined using both simulated and real yeast gene expression data. In particular, we concentrate especially on a smoothing effect caused by the distribution of the cell population in time and introduce several methods for inverting this phenomenon. The proposed methods can be used to significantly improve the accuracy of the gene expression time-series measurements since the cell population asynchrony (wide distribution) is inevitably caused by the different operation pace of the cells. Some of the proposed methods rely on the partition of the genes, as well as the corresponding expression profiles, into the cell cycle regulated and noncell cycle regulated genes. For that purpose, we first study the cell cycle regulated genes and introduce a method that can be used to estimate the period length of those genes. We also estimate the spreading rate of the underlying distribution of the cell population based solely on the observed gene expression data. After the preliminary experiments, we introduce some methods for estimating the underlying distribution of the cell population instead of its spreading rate. These methods assume certain additional measurements, such as flow cytometry (e.g. fluorescent-activated cell sorter (FACS)) or bud counting measurements, to be available. We also apply the standard blind deconvolution method for estimating the true distribution of the cell population. The found estimates of the spreading rate of the cell distribution and the distributions of the cell population themself are used to invert the smoothing effect. To that end, we discuss some inversion approaches applicable to the problem in hand.