Details

Autor(en) / Beteiligte

• Hsu, Ian S.
• Moses, Alan M.

Titel

Stochastic models for single‐cell data: Current challenges and the way forward

Ist Teil von

• The FEBS journal, 2022-02, Vol.289 (3), p.647-658

Ort / Verlag

England: Blackwell Publishing Ltd

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Although the quantity and quality of single‐cell data have progressed rapidly, making quantitative predictions with single‐cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single‐cell data: (a) because variability in single‐cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single‐cell data can lead to violations of the assumption of symmetric errors in least‐squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single‐cell data. The stochastic nature of molecular biology leads to many challenges in building predictive models for single cells. Here, we highlight recent progress in modeling approaches that aim to measure and predict single‐cell variability.