Deconvolving cell cycle expression data with complementary information

Deconvolving cell cycle expression data with complementary information. Bar-Joseph, Z, Farkash, S, Gifford, D., Simon, I, & Rosenfeld, R Bioinformatics, 20 Suppl 1:i23–i30, August, 2004.

Paper doi abstract bibtex

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

@article{bar-joseph_deconvolving_2004,
	title = {Deconvolving cell cycle expression data with complementary information},
	volume = {20 Suppl 1},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/15262777},
	doi = {10.1093/bioinformatics/bth915},
	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},
	language = {eng},
	journal = {Bioinformatics},
	author = {Bar-Joseph, Z and Farkash, S and Gifford, DK and Simon, I and Rosenfeld, R},
	month = aug,
	year = {2004},
	keywords = {Computational Epidemiology and Computational Biology},
	pages = {i23--i30},
}

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