Deconvolving Cell Cycle Expression Data with Complementary Information

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 is actually a convolution of its values in an interval around 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 pro- files. 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. the supporting website: