1. Computational and Systems Biology

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

  1. Michael Kleyman
  2. Emre Sefer
  3. Teodora Nicola
  4. Celia Espinoza
  5. Divya Chhabra
  6. James S Hagood
  7. Naftali Kaminski
  8. Namasivayam Ambalavanan
  9. Ziv Bar-Joseph  Is a corresponding author
  1. Carnegie Mellon University, United States
  2. University of Alabama at Birmingham, United States
  3. University of California, San Diego, United States
  4. Yale University, United States
https://doi.org/10.7554/eLife.18541
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Cite this article
  1. Michael Kleyman
  2. Emre Sefer
  3. Teodora Nicola
  4. Celia Espinoza
  5. Divya Chhabra
  6. James S Hagood
  7. Naftali Kaminski
  8. Namasivayam Ambalavanan
  9. Ziv Bar-Joseph
(2017)
Selecting the most appropriate time points to profile in high-throughput studies
eLife 6:e18541.
https://doi.org/10.7554/eLife.18541
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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. 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.

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Author details

  1. Michael Kleyman

    Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Emre Sefer

    Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Teodora Nicola

    Department of Pediatrics, Division of Neonatology, University of Alabama at Birmingham, Birmingham, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Celia Espinoza

    Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Divya Chhabra

    Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. James S Hagood

    Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Naftali Kaminski

    Section of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Namasivayam Ambalavanan

    Department of Pediatrics, Division of Neonatology, University of Alabama at Birmingham, Birgmingham, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Ziv Bar-Joseph

    Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
    For correspondence
    zivbj@cs.cmu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3430-6051

Funding

National Institutes of Health (U01 HL122626)

  • Ziv Bar-Joseph

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (APN 10042) of the University of Alabama at Birmingham. All lungs were isolated immediately following euthanasia using approved protocols.

Reviewing Editor

  1. Anshul Kundaje

Publication history

  1. Received: June 6, 2016
  2. Accepted: January 23, 2017
  3. Accepted Manuscript published: January 26, 2017 (version 1)
  4. Version of Record published: February 21, 2017 (version 2)

Copyright

© 2017, Kleyman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Michael Kleyman
  2. Emre Sefer
  3. Teodora Nicola
  4. Celia Espinoza
  5. Divya Chhabra
  6. James S Hagood
  7. Naftali Kaminski
  8. Namasivayam Ambalavanan
  9. Ziv Bar-Joseph
(2017)
Selecting the most appropriate time points to profile in high-throughput studies
eLife 6:e18541.
https://doi.org/10.7554/eLife.18541

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