In the past decade, advances in single-cell RNA sequencing technologies have radically improved the comprehension of cell biology. Pseudo-time computation has revealed greater heterogeneity in cell differentiation, which is important for the study of various diseases. In this paper we evaluate pseudo-time calculation methodologies and propose statistical models for analyzing the relationship between pseudo-time and gene expression.
Antonino Gagliano, Gianluca Sottile, Nicolina Sciaraffa, Claudia Coronnello, Luigi Augugliaro (2024). Single-cell Sequencing Data: Critical Analysis and Definition of Statistical Models. In Book of abstract.
Single-cell Sequencing Data: Critical Analysis and Definition of Statistical Models
Antonino Gagliano
;Gianluca Sottile;Claudia Coronnello;Luigi Augugliaro
2024-01-01
Abstract
In the past decade, advances in single-cell RNA sequencing technologies have radically improved the comprehension of cell biology. Pseudo-time computation has revealed greater heterogeneity in cell differentiation, which is important for the study of various diseases. In this paper we evaluate pseudo-time calculation methodologies and propose statistical models for analyzing the relationship between pseudo-time and gene expression.File | Dimensione | Formato | |
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