Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only about 6 days after reprogramming initiation, iPS cells can be experimentally determined via fluorescent probes. What is more, the ratio of progenitor cells during early reprograming period is below 5%, which is too low to capture experimentally in the early stage. We propose a novel computational approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Our model allows several missing values/frames in the sample datasets, thus it is applicable to a wide range of scenarios. 

Flow chart of the machine learning based approach for iPS progenitor cell identification.