Sharif Digital Repository

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway. © 2019, The Author(s)  

Background: Pluripotency is proposed to exist in two different stages: Naive and Primed. Conventional human pluripotent cells are essentially in the primed stage. In recent years, several protocols have claimed to generate naive human embryonic stem cells (hESCs). To the best of our knowledge, none of these protocols is currently recognized as the gold standard method. Furthermore, the consistency of the resulting cells from these diverse protocols at the molecular level is yet to be shown. Additionally, little is known about the principles that govern the metabolic differences between naive and primed pluripotency. In this work, using a computational approach, we tried to shed light on...