Abstract
Motivation The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.
Results In this study, we design a survey to analyse how biologists use previous research outcomes to inform their decisions about which time points to sample in subsequent experiments. We then determine how the choice of time points affects the type of perturbations in gene expression that can be observed. Finally, we present our main result: NITPicker, a computational strategy for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making while maximising information about the shape of the underlying curves, utilising ideas from the field of functional data analysis.
Availability NITPicker is available on GIThub (https://github.com/ezer/NITPicker).
