Learning Pathway Dynamics from Single-Cell Proteomic Data: A Comparative Study.

ABSTRACT: Single-cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general-purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) t

SUBMITTER: Verrou KM 

PROVIDER: S-EPMC7687117 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature