TY - JOUR
T1 - Time series experimental design under one-shot sampling
T2 - The importance of condition diversity
AU - Kang, Xiaohan
AU - Hajek, Bruce
AU - Wu, Faqiang
AU - Hanzawa, Yoshie
N1 - Funding Information:
This work was supported by the Plant Genome Research Program from the National Science Foundation (NSF-IOS-PGRP-1823145) to B.H. and Y.H.
Publisher Copyright:
© 2019 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0224577
DO - 10.1371/journal.pone.0224577
M3 - Article
C2 - 31671126
AN - SCOPUS:85074424075
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 10
M1 - e0224577
ER -