title: Selecting the most appropriate time points to profile in high-throughput studies.
abstract: 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.TPSutilizes expression data from a small set of genes sampled at a high rate. As we show by applyingTPSto study mouse lung development, the points selected byTPScan 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.
authors.affiliation: Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
authors.forename: Michael
authors.initials: M
authors.lastname: Kleyman
authors.affiliation: Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
authors.forename: Emre
authors.initials: E
authors.lastname: Sefer
authors.affiliation: Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States.
authors.forename: Teodora
authors.initials: T
authors.lastname: Nicola
authors.affiliation: Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.|CARady Children's Hospital San Diego, San Diego, United States.
authors.forename: Celia
authors.initials: C
authors.lastname: Espinoza
authors.affiliation: Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.|CARady Children's Hospital San Diego, San Diego, United States.
authors.forename: Divya
authors.initials: D
authors.lastname: Chhabra
authors.affiliation: Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.|CARady Children's Hospital San Diego, San Diego, United States.
authors.forename: James S
authors.initials: JS
authors.lastname: Hagood
authors.affiliation: Section of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, Yale University, New Haven, United States.
authors.forename: Naftali
authors.initials: N
authors.lastname: Kaminski
authors.affiliation: Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States.
authors.forename: Namasivayam
authors.initials: N
authors.lastname: Ambalavanan
authors.affiliation: Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
authors.forename: Ziv
authors.identifier: 0000-0003-3430-6051
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D008168:Lung / Q000196:embryology; D051379:Mice; D013997:Time Factors
keywords: computational biology; experimental design; lung developement; mouse; systems biology; time point selection