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

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.

Data availability

The following data sets were generated

Article and author information

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.

Metrics

  • 2,409
    Page views
  • 487
    Downloads
  • 17
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Zhe Chen, Garrett J Blair ... Hugh T Blair
    Tools and Resources

    Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data is typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n=12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n=2) during an instrumental task from calcium fluorescence in orbitofrontal cortex (OFC). DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array (FPGA) hardware for real time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.

    1. Computational and Systems Biology
    2. Immunology and Inflammation
    Anastasia O Smirnova, Anna M Miroshnichenkova ... Alexander Komkov
    Tools and Resources

    High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies is still challenging. Here we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: Immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.