Dictionary learning allows model-free pseudotime estimation of transcriptomic data.

ABSTRACT:

Background

Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime es

SUBMITTER: Rams M 

PROVIDER: S-EPMC8760643 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature