TASIC: determining branching models from time series single cell data

Motivation: Single cell RNA-Seq analysis holds great promise for elucidating the networks and pathways controlling cellular differentiation and disease. However, the analysis of time series single cell RNA-Seq data raises several new computational challenges. Cells at each time point are often sampled from a mixture of cell types, each of which may be a progenitor of one, or several, specific fates making it hard to determine which cells should be used to reconstruct temporal trajectories. In addition, cells, even from the same time point, may be unsynchronized making it hard to rely on the measured time for determining these trajectories. Results: We present TASIC a new method for determining temporal trajectories, branching and cell assignments in single cell time series experiments. Unlike prior approaches TASIC uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and stochastic variations. Applying TASIC to in vitro myoblast differentiation and in-vivo lung development data we show that it accurately reconstructs developmental trajectories from single cell experiments. The reconstructed models enabled us to identify key genes involved in cell fate determination and to obtain new insights about a specific type of lung cells and its role in development.