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Stochastic models for single‐cell data: Current challenges and the way forward
Author(s) -
Hsu Ian S.,
Moses Alan M.
Publication year - 2022
Publication title -
the febs journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 204
eISSN - 1742-4658
pISSN - 1742-464X
DOI - 10.1111/febs.15760
Subject(s) - stochastic modelling , computer science , data set , set (abstract data type) , stochastic process , data quality , experimental data , econometrics , data mining , mathematics , statistics , artificial intelligence , metric (unit) , operations management , economics , programming language
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.