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

Proceedings of the National Academy of Sciences of the United States of America

Volumn 107, Issue 22, 2010, Pages 9923-9928

  Zhou, Baiyu ^a   Xu, Weihong ^b   Herndon, David ^c   Tompkins, Ronald ^d   Davis, Ronald ^b   Xiao, Wenzhong ^b,d   Wong, Wing Hung ^a  

^a STANFORD UNIVERSITY   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c University of Texas Medical Branch School of Medicine   (United States)

^d SHRINERS HOSPITALS FOR CHILDREN   (United States)

Author keywords

Analysis of variance; Factorial design; Longitudinal data

Indexed keywords

ADULT; AGE; ARTICLE; BURN; CHILD; CONTROLLED STUDY; FEMALE; GENE EXPRESSION; HUMAN; IMMUNOGLOBULIN GENE; MAJOR CLINICAL STUDY; MALE; MICROARRAY ANALYSIS; MITOCHONDRION; PRESCHOOL CHILD; PRIORITY JOURNAL; SIMULATION;

ADULT; AGE FACTORS; ANALYSIS OF VARIANCE; BURNS; CHILD; CHILD, PRESCHOOL; CROSS-SECTIONAL STUDIES; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; FEMALE; GENE EXPRESSION PROFILING; GENES, IMMUNOGLOBULIN; GENES, MITOCHONDRIAL; HUMANS; INFANT; LONGITUDINAL STUDIES; MALE; MIDDLE AGED; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROGNOSIS; SOFTWARE; TIME FACTORS;

EID: 77953426735     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1002757107     Document Type: Article

References (21)

1. Wakefield J, Zhou C, Self S (2003) Modeling gene expression data over time: Curve clustering with informative prior distributions. Bayesian Stat 7:21-732.
2. Bar-Joseph Z, Gerber G, Jaakkola TS, Gifford DK, Simon I (2003) Continuous representations of time-series gene expression data. J Comput Bio 10:341-356. (Pubitemid 37372153)
3. Luan Y, Li H (2003) Clustering time course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19:474-482. (Pubitemid 36336513)
4. Ma P, Castillo-Davis CI, Zhong W, Liu JS (2006) A data-driven clustering method for time course gene expression data. Nucleic Acids Res 34:1261-1269.
5. Storey JD, XiaoW, Leek JT, Tompkins RG, Davis RW(2005) Significance analysis of time course microarray experiments. Proc Natl Acad Sci USA 102:12837-12842.
6. Yuan M, Kendziorski C (2006) Hidden Markov models for microarray time course data under multiple biological conditions (with discussion). J Am Stat Assoc 101:1323-1340.
7. Tai YC, Speed T (2006) A multivariate empirical Bayes statistic for replicated microarray time course data. Ann Stat 34:2387-2412.
8. Wei Z, Li H (2008) A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data. Ann Appl Stat 2:408-429.
9. Zhou B, Wong WH (2009) A bootstrap-based non-parametric ANOVA method and its application to multi-factorial microarray data. Stat Sinica, in press.
10. Anderson TW, Stein C, Zaman A (1985) Best Invariant Estimation of a Direction Parameter. Ann Stat 13:526-533.
11. Pinheiro JC, Bates DM (2000) Mixed-Effects Models in S and S-PLUS (Springer, New York).
12. Ryan CM, et al. (1998) Objective estimates of the probability of death from burn injuries. N Engl J Med 338:362-366.
13. Herndon DN, Tompkins RG (2004) Support of the metabolic response to burn injury. Lancet 363:1895-1902.
14. Finnerty CC, et al. (2008) Temporal cytokine profiles in severely burned patients: A comparison of adults and children. Mol Med 14:553-560.
15. Tasat DR, Mancuso R, O'Connor S, Molinari B (2003) Age-dependent change in reactive oxygen species and nitric oxide generation by rat alveolar macrophages. Aging Cell 2:159-164.
16. Padfield KE, et al. (2005) Burn injury causes mitochondrial dysfunction in skeletal muscle. Proc Natl Acad Sci USA 102:5368-5373.
17. Cree MG, et al. (2007) Insulin sensitivity and mitochondrial function are improved in children with burn injury during a randomized controlled trial of fenofibrate. Ann Surg 245:214-221.
18. Arturson G, Högman CF, Johansson SG, Killander J (1969) Changes in immunoglobulin levels in severely burned patients. Lancet 293:546-548.
19. Zhang M, et al. (2004) Identification of a specific self-reactive IgM antibody that initiates intestinal ischemia/reperfusion injury. Proc Natl Acad Sci USA 101:3886-3891.
20. Zhang M, et al. (2006) Identification of the target self-antigens in reperfusion injury. J Exp Med 203:141-152.
21. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99:6567-6572.