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 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/.
Zhou
Baiyu
B
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Xu
Weihong
W
Herndon
David
D
Tompkins
Ronald
R
Davis
Ronald
R
Xiao
Wenzhong
W
Wong
Wing Hung
WH
Toner
Mehmet
M
Warren
H Shaw
HS
Schoenfeld
David A
DA
Rahme
Laurence
L
McDonald-Smith
Grace P
GP
Hayden
Douglas
D
Mason
Philip
P
Fagan
Shawn
S
Yu
Yong-Ming
YM
Cobb
J Perren
JP
Remick
Daniel G
DG
Mannick
John A
JA
Lederer
James A
JA
Gamelli
Richard L
RL
Silver
Geoffrey M
GM
West
Michael A
MA
Shapiro
Michael B
MB
Smith
Richard
R
Camp
David G
DG
Qian
Weijun
W
Storey
John
J
Mindrinos
Michael
M
Tibshirani
Rob
R
Lowry
Stephen
S
Calvano
Steven
S
Chaudry
Irshad
I
West
Michael A
MA
Cohen
Mitchell
M
Moore
Ernest E
EE
Johnson
Jeffrey
J
Moldawer
Lyle L
LL
Baker
Henry V
HV
Efron
Philip A
PA
Balis
Ulysses G J
UG
Billiar
Timothy R
TR
Ochoa
Juan B
JB
Sperry
Jason L
JL
Miller-Graziano
Carol L
CL
De
Asit K
AK
Bankey
Paul E
PE
Finnerty
Celeste C
CC
Jeschke
Marc G
MG
Minei
Joseph P
JP
Arnoldo
Brett D
BD
Hunt
John L
JL
Horton
Jureta
J
Cobb
J Perren
JP
Brownstein
Bernard
B
Freeman
Bradley
B
Maier
Ronald V
RV
Nathens
Avery B
AB
Cuschieri
Joseph
J
Gibran
Nicole
N
Klein
Matthew
M
O'Keefe
Grant
G
D000328:Adult; D000367:Age Factors; D000704:Analysis of Variance; D002056:Burns / Q000235:genetics* / Q000276:immunology; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005803:Genes, Immunoglobulin; D050259:Genes, Mitochondrial; D006801:Humans; D007223:Infant; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011379:Prognosis; D012984:Software; D013997:Time Factors