Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis.

ABSTRACT:

Motivation

Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that

SUBMITTER: Heerah S 

PROVIDER: S-EPMC8388030 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature