title: Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments.
abstract: An important experimental design question for high-throughput time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data, we observe that, under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high-throughput time series experiments that do not use replicates.
authors.affiliation: Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
authors.forename: Emre
authors.initials: E
authors.lastname: Sefer
authors.affiliation: Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Kleyman
authors.affiliation: Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. Electronic address: zivbj@cs.cmu.edu.
authors.forename: Ziv
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling; D012107:Research Design