Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction.

ABSTRACT:

Background

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics.

Results

Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datas

SUBMITTER: Ranek JS 

PROVIDER: S-EPMC9442962 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature