Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Abstract

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free fromthe convolution effects associatedwith imperfect cell synchronization. Through wavelet- basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of ourmethod by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cellcycle- regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithmalso explicitly learns distinct transcription programs formother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.