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Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection ( TPS ) method that solves this combinatorial problem in a principled and practical way.  TPS  utilizes expression data from a small set of genes sampled at a high rate. As we show by applying  TPS  to study mouse lung development, 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 protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website:  www.sb.cs.cmu.edu/TPS

Selecting the most appropriate time points to profile in high-throughput studies