Jonckheere-Terpstra-Kendall-based non-parametric analysis of temporal differential gene expression.

ABSTRACT: Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere-Terpstra-Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation

SUBMITTER: Iuchi H 

PROVIDER: S-EPMC7991226 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature