Time Series Experimental Design Under One-Shot Sampling: The Importance of Condition Diversity

Many biological data sets are prepared using one-shot sampling, in which each individual organism provides only one sample. Time series therefore do not follow trajectories of individuals over time. However, samples collected at different times from individuals grown/raised 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/raising individuals under multiple conditions if one-shot sampling is used. This paper models the condition effect explicitly by correlated perturbations in the variations driving the expression dynamics, quantifies the performance of the generalized likelihood-ratio test for network structure, and illustrates the difficulty in network reconstruction under one-shot sampling when the condition effect is absent.