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Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime
Author(s) -
Elvis Han Cui,
Dongyuan Song,
Weng Kee Wong,
Jingyi Jessica Li
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac423
Subject(s) - interpretability , computer science , nonparametric statistics , flexibility (engineering) , python (programming language) , data mining , artificial intelligence , machine learning , statistics , mathematics , operating system
Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.

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