Open AccessTime series experimental design under one-shot sampling: The importance of condition diversity
Author(s) -
Xiaohan Kang,
Bruce Hajek,
Fen Wu,
Yoshie Hanzawa
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0224577
Subject(s) - sampling (signal processing) , estimator , series (stratigraphy) , shot (pellet) , statistics , computer science , biological system , time series , mathematics , one shot , biology , computational biology , statistical physics , algorithm , physics , telecommunications , engineering , paleontology , chemistry , organic chemistry , detector , mechanical engineering
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.