Abstract
The purpose of this study is to outline how ideas from information theory may be used to analyze single-cell data and better understand stem cell behavior. Recent technological breakthroughs in single-cell profiling have made it possible to interrogate cell–cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional, and noisy datasets that arise from single-cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability, and dynamics. We provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single-cell level. We also discuss how work in this area might develop in the near future.
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