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Estimating Differentiation Potency Of Single Cells Using Single-Cell Entropy (SCENT).

Weiyan Chen, A. Teschendorff
Published 2019 · Computer Science, Medicine

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The ability to measure molecular properties (e.g., mRNA expression) at the single-cell level is revolutionizing our understanding of cellular developmental processes and how these are altered in diseases like cancer. The need for computational methods aimed at extracting biological knowledge from such single-cell data has never been greater. Here, we present a detailed protocol for estimating differentiation potency of single cells, based on our Single-Cell ENTropy (SCENT) algorithm. The estimation of differentiation potency is based on an explicit biophysical model that integrates the RNA-Seq profile of a single cell with an interaction network to approximate potency as the entropy of a diffusion process on the network. We here focus on the implementation, providing a step-by-step introduction to the method and illustrating it on a real scRNA-Seq dataset profiling human embryonic stem cells and multipotent progenitors representing the 3 main germ layers. SCENT is aimed particularly at single-cell studies trying to identify novel stem-or-progenitor like phenotypes, and may be particularly valuable for the unbiased identification of cancer stem cells. SCENT is implemented in R, licensed under the GNU General Public Licence v3, and freely available from https://github.com/aet21/SCENT .
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