Most biological processes are dynamic and thus many have been studied using high throughput time series data. A key question when designing such studies is: 'What are the most appropriate sampling points?' To date, this question was either answered in an ad-hoc manner or by uniform sampling. Here we present the Time Point Selection (TPS) method that provides a principled and practical strategy to select the most relevant time points for such studies. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of miRNA expression profiles and methylation changes over time. TPS can thus serve as a key design strategy for experiments profiling diverse biological process and datasets over time.
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