Velo-Predictor: an ensemble learning pipeline for RNA velocity prediction.

ABSTRACT:

Background

RNA velocity is a novel and powerful concept which enables the inference of dynamical cell state changes from seemingly static single-cell RNA sequencing (scRNA-seq) data. However, accurate estimation of RNA velocity is still a challenging problem, and the underlying kinetic mechanisms of transcriptional and splicing regulations are not fully clear. Moreover, scRNA-seq data tend to be sparse compared with possible cell states, and a given dataset of estimated RNA velocities needs imputation for some cell states not yet covered.

Results

We formulate RNA velocity prediction as a supervised learning problem of classification for the first time, where a cell state space is divided into equal-sized segments by directions as classes, and the estimated RNA velocity vecto

SUBMITTER: Wang X 

PROVIDER: S-EPMC8414693 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature