ScEvoNet: a gradient boosting-based method for prediction of cell state evolution.

ABSTRACT:

Background

Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option.

Results

Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-

SUBMITTER: Kotov A 

PROVIDER: S-EPMC9990205 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature