Time series experimental design under one-shot sampling: The importance of condition diversity


Autoři: Xiaohan Kang aff001;  Bruce Hajek aff001;  Faqiang Wu aff002;  Yoshie Hanzawa aff002
Působiště autorů: Coordinated Science Laboratory and Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois, United States of America aff001;  Department of Biology, California State University, Northridge, Northridge, California, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224577

Souhrn

Many biological data sets are prepared using one-shot sampling, in which each individual organism is sampled at most once. Time series therefore do not follow trajectories of individuals over time. However, samples collected at different times from individuals grown under the same conditions share the same perturbations of the biological processes, and hence behave as surrogates for multiple samples from a single individual at different times. This implies the importance of growing individuals under multiple conditions if one-shot sampling is used. This paper models the condition effect explicitly by using condition-dependent nominal mRNA production amounts for each gene, it quantifies the performance of network structure estimators both analytically and numerically, and it illustrates the difficulty in network reconstruction under one-shot sampling when the condition effect is absent. A case study of an Arabidopsis circadian clock network model is also included.

Klíčová slova:

Algorithms – Arabidopsis thaliana – Covariance – Gene expression – Gene regulatory networks – Genetic networks – Leaves – Network analysis


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Článek vyšel v časopise

PLOS One


2019 Číslo 10