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Analysis of factorial time-course microarrays with application to a clinical study of burn injury

Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simult... Full description

Journal Title: Proceedings of the National Academy of Sciences 01 June 2010, Vol.107(22), p.9923
Main Author: Baiyu Zhou
Other Authors: Weihong Xu , David Herndon , Ronald Tompkins , Ronald Davis , Wenzhong Xiao , Wing Hung Wong , Inflammation and Host Response to Injury Program , Mehmet Toner , H. Shaw Warren , David A. Schoenfeld , Laurence Rahme , Grace P. Mcdonald-Smith , Douglas Hayden , Philip Mason , Shawn Fagan , Yong-Ming Yu , J. Perren Cobb , Daniel G. Remick , John A. Mannick , James A. Lederer , Richard L. Gamelli , Geoffrey M. Silver , Michael A. West , Michael B. Shapiro , Richard Smith , David G. Camp II , Weijun Qian , John Storey , Michael Mindrinos , Rob Tibshirani , Stephen Lowry , Steven Calvano , Irshad Chaudry , Mitchell Cohen , Ernest E. Moore , Jeffrey Johnson , Lyle L. Moldawer , Henry V. Baker , Philip A. Efron , Ulysses G.J. Balis , Timothy R. Billiar , Juan B. Ochoa , Jason L. Sperry , Carol L. Miller-Graziano , Asit K. De , Paul E. Bankey , Celeste C. Finnerty , Marc G. Jeschke , Joseph P. Minei , Brett D. Arnoldo , John L. Hunt , Jureta Horton , Bernard Brownstein , Bradley Freeman , Ronald V. Maier , Avery B. Nathens , Joseph Cuschieri , Nicole Gibran , Matthew Klein , Grant O’keefe
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0027-8424 ; E-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1002757107
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recordid: pnas_s107_22_9923
title: Analysis of factorial time-course microarrays with application to a clinical study of burn injury
format: Article
creator:
  • Baiyu Zhou
  • Weihong Xu
  • David Herndon
  • Ronald Tompkins
  • Ronald Davis
  • Wenzhong Xiao
  • Wing Hung Wong
  • Inflammation and Host Response to Injury Program
  • Mehmet Toner
  • H. Shaw Warren
  • David A. Schoenfeld
  • Laurence Rahme
  • Grace P. Mcdonald-Smith
  • Douglas Hayden
  • Philip Mason
  • Shawn Fagan
  • Yong-Ming Yu
  • J. Perren Cobb
  • Daniel G. Remick
  • John A. Mannick
  • James A. Lederer
  • Richard L. Gamelli
  • Geoffrey M. Silver
  • Michael A. West
  • Michael B. Shapiro
  • Richard Smith
  • David G. Camp II
  • Weijun Qian
  • John Storey
  • Michael Mindrinos
  • Rob Tibshirani
  • Stephen Lowry
  • Steven Calvano
  • Irshad Chaudry
  • Mitchell Cohen
  • Ernest E. Moore
  • Jeffrey Johnson
  • Lyle L. Moldawer
  • Henry V. Baker
  • Philip A. Efron
  • Ulysses G.J. Balis
  • Timothy R. Billiar
  • Juan B. Ochoa
  • Jason L. Sperry
  • Carol L. Miller-Graziano
  • Asit K. De
  • Paul E. Bankey
  • Celeste C. Finnerty
  • Marc G. Jeschke
  • Joseph P. Minei
  • Brett D. Arnoldo
  • John L. Hunt
  • Jureta Horton
  • Bernard Brownstein
  • Bradley Freeman
  • Ronald V. Maier
  • Avery B. Nathens
  • Joseph Cuschieri
  • Nicole Gibran
  • Matthew Klein
  • Grant O’keefe
subjects:
  • Sciences (General)
ispartof: Proceedings of the National Academy of Sciences, 01 June 2010, Vol.107(22), p.9923
description: Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body’s response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package “TANOVA” and submitted to the Comprehensive R Archive Network at http://www.r-project.org/ . It is also available for download at http://gluegrant1.stanford.edu/TANOVA/ .
language: eng
source:
identifier: ISSN: 0027-8424 ; E-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1002757107
fulltext: fulltext_linktorsrc
issn:
  • 0027-8424
  • 00278424
  • 1091-6490
  • 10916490
url: Link


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titleAnalysis of factorial time-course microarrays with application to a clinical study of burn injury
creatorBaiyu Zhou ; Weihong Xu ; David Herndon ; Ronald Tompkins ; Ronald Davis ; Wenzhong Xiao ; Wing Hung Wong ; Inflammation and Host Response to Injury Program ; Mehmet Toner ; H. Shaw Warren ; David A. Schoenfeld ; Laurence Rahme ; Grace P. Mcdonald-Smith ; Douglas Hayden ; Philip Mason ; Shawn Fagan ; Yong-Ming Yu ; J. Perren Cobb ; Daniel G. Remick ; John A. Mannick ; James A. Lederer ; Richard L. Gamelli ; Geoffrey M. Silver ; Michael A. West ; Michael B. Shapiro ; Richard Smith ; David G. Camp II ; Weijun Qian ; John Storey ; Michael Mindrinos ; Rob Tibshirani ; Stephen Lowry ; Steven Calvano ; Irshad Chaudry ; Mitchell Cohen ; Ernest E. Moore ; Jeffrey Johnson ; Lyle L. Moldawer ; Henry V. Baker ; Philip A. Efron ; Ulysses G.J. Balis ; Timothy R. Billiar ; Juan B. Ochoa ; Jason L. Sperry ; Carol L. Miller-Graziano ; Asit K. De ; Paul E. Bankey ; Celeste C. Finnerty ; Marc G. Jeschke ; Joseph P. Minei ; Brett D. Arnoldo ; John L. Hunt ; Jureta Horton ; Bernard Brownstein ; Bradley Freeman ; Ronald V. Maier ; Avery B. Nathens ; Joseph Cuschieri ; Nicole Gibran ; Matthew Klein ; Grant O’keefe
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descriptionTime-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body’s response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package “TANOVA” and submitted to the Comprehensive R Archive Network at http://www.r-project.org/ . It is also available for download at http://gluegrant1.stanford.edu/TANOVA/ .
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titleAnalysis of factorial time-course microarrays with application to a clinical study of burn injury
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Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body’s response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package “TANOVA” and submitted to the Comprehensive R Archive Network at http://www.r-project.org/ . It is also available for download at http://gluegrant1.stanford.edu/TANOVA/ .

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authorBaiyu Zhou ; Weihong Xu ; David Herndon ; Ronald Tompkins ; Ronald Davis ; Wenzhong Xiao ; Wing Hung Wong ; Inflammation and Host Response to Injury Program ; Mehmet Toner ; H. Shaw Warren ; David A. Schoenfeld ; Laurence Rahme ; Grace P. Mcdonald-Smith ; Douglas Hayden ; Philip Mason ; Shawn Fagan ; Yong-Ming Yu ; J. Perren Cobb ; Daniel G. Remick ; John A. Mannick ; James A. Lederer ; Richard L. Gamelli ; Geoffrey M. Silver ; Michael A. West ; Michael B. Shapiro ; Richard Smith ; David G. Camp II ; Weijun Qian ; John Storey ; Michael Mindrinos ; Rob Tibshirani ; Stephen Lowry ; Steven Calvano ; Irshad Chaudry ; Mitchell Cohen ; Ernest E. Moore ; Jeffrey Johnson ; Lyle L. Moldawer ; Henry V. Baker ; Philip A. Efron ; Ulysses G.J. Balis ; Timothy R. Billiar ; Juan B. Ochoa ; Jason L. Sperry ; Carol L. Miller-Graziano ; Asit K. De ; Paul E. Bankey ; Celeste C. Finnerty ; Marc G. Jeschke ; Joseph P. Minei ; Brett D. Arnoldo ; John L. Hunt ; Jureta Horton ; Bernard Brownstein ; Bradley Freeman ; Ronald V. Maier ; Avery B. Nathens ; Joseph Cuschieri ; Nicole Gibran ; Matthew Klein ; Grant O’keefe
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23Michael A. West
24Michael B. Shapiro
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29Michael Mindrinos
30Rob Tibshirani
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32Steven Calvano
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34Mitchell Cohen
35Ernest E. Moore
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39Philip A. Efron
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15Shawn Fagan
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20James A. Lederer
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atitleAnalysis of factorial time-course microarrays with application to a clinical study of burn injury
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abstract

Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body’s response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package “TANOVA” and submitted to the Comprehensive R Archive Network at http://www.r-project.org/ . It is also available for download at http://gluegrant1.stanford.edu/TANOVA/ .

pubNational Acad Sciences
doi10.1073/pnas.1002757107
urlhttp://www.pnas.org/content/107/22/9923.abstract
lad01Proceedings of the National Academy of Sciences
pages9923-9928
date2010-06-01