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Bioinformatics Advance Access originally published online on July 2, 2009
Bioinformatics 2009 25(20):2670-2676; doi:10.1093/bioinformatics/btp415
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Statistical lower bounds on protein copy number from fluorescence expression images

Lee Zamparo 1 and Theodore J. Perkins 2,*

1 Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada H3G 1M8 and 2 Ottawa Hospital Research Institute, Ottawa, Ontario, Canada K1H 8L6

*To whom correspondence should be addressed.


  Abstract

Motivation: Fluorescence imaging has become a commonplace for quantitatively measuring mRNA or protein expression in cells and tissues. However, such expression data are usually relative—absolute concentrations or molecular copy numbers are typically not known. While this is satisfactory for many applications, for certain kinds of quantitative network modeling and analysis of expression noise, absolute measures of expression are necessary.

Results: We propose two methods for estimating molecular copy numbers from single uncalibrated expression images of tissues. These methods rely on expression variability between cells, due either to steady-state fluctuations or unequal distribution of molecules during cell division, to make their estimates. We apply these methods to 152 protein fluorescence expression images of Drosophila melanogaster embryos during early development, generating copy number estimates for 14 genes in the segmentation network. We also analyze the effects of noise on our estimators and compare with empirical findings. Finally, we confirm an observation of Bar-Even et al., made in the much different setting of Saccharomyces cerevisiae, that steady-state expression variance tends to scale with mean expression.

Availability: The data are all drawn from FlyEx (explained within), and is available at http://flyex.ams.sunysb.edu/FlyEx/. Data and MATLAB codes for all algorithms described in this article are available at http://www.perkinslab.ca/pubs/ZP2009.html.

Contact: tperkins{at}ohri.ca

Associate Editor: John Quackenbush

Received on April 20, 2009; revised on June 11, 2009; accepted on June 28, 2009

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