RT Journal
A1 Ding, Jun
A1 Aronow, Bruce
A1 Kaminski, Naftali
A1 Kitzmiller, Joseph
A1 Whitsett, Jeffrey
A1 Bar-Joseph, Ziv
T1 Reconstructing differentiation networks and their regulation from time series single cell expression data
JF Genome Research 
JO Genome Research 
YR 2018 
FD January 09 
DO 10.1101/gr.225979.117 
SP gr.225979.117 
UL http://genome.cshlp.org/content/early/2018/01/09/gr.225979.117.abstract 
AB Generating detailed and accurate organogenesis models using single cell RNA-seq data remains a major challenge. Current methods have relied primarily on the assumption that decedent cells are similar to their parents in terms of gene expression levels. These assumptions do not always hold for in-vivo studies which often include infrequently sampled, un-synchronized and diverse cell populations. Thus, additional information may be needed to determine the correct ordering and branching of progenitor cells and the set of transcription factors (TFs) that are active during advancing stages of organogenesis. To enable such modeling we have developed a method that learns a probabilistic model which integrates expression similarity with regulatory information to reconstruct the dynamic developmental cell trajectories. When applied to mouse lung developmental data the method accurately distinguished different cell types and lineages. Existing and new experimental data validated the ability of the method to identify key regulators of cell fate.