Bayesian median regression for temporal gene expression data

被引:0
作者
Yu, Keming [1 ]
Vinciotti, Veronica
Liu, Xiaohui
't Hoen, Peter A. C. [2 ]
机构
[1] Brunel Univ, Dept Math, Uxbridge UB8 3PH, Middx, England
[2] Leiden Univ Med Ctr, Ctr Human & Clin Genet, Leiden, Netherlands
来源
COMPLIFE 2007: 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL LIFE SCIENCE | 2007年 / 940卷
基金
英国生物技术与生命科学研究理事会;
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T-2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.
引用
收藏
页码:60 / +
页数:2
相关论文
共 12 条
[1]  
[Anonymous], 2004, BAYESIAN THEORY
[2]  
Jeffreys H., 1998, The Theory of Probability
[3]   Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation [J].
Jelier, Rob ;
Jenster, Guido ;
Dorssers, Lambert C. J. ;
Wouters, Bas J. ;
Hendriksen, Peter J. M. ;
Mons, Barend ;
Delwel, Ruud ;
Kors, Jan A. .
BMC BIOINFORMATICS, 2007, 8 (1)
[4]   BAYES FACTORS [J].
KASS, RE ;
RAFTERY, AE .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1995, 90 (430) :773-795
[5]   REGRESSION QUANTILES [J].
KOENKER, R ;
BASSETT, G .
ECONOMETRICA, 1978, 46 (01) :33-50
[6]   Bayesian modeling of differential gene expression [J].
Lewin, A ;
Richardson, S ;
Marshall, C ;
Glazier, A ;
Aitman, T .
BIOMETRICS, 2006, 62 (01) :1-9
[7]   Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data [J].
Vinciotti, V ;
Liu, XH ;
Turk, R ;
de Meijer, EJ ;
't Hoen, PAC .
BMC BIOINFORMATICS, 2006, 7 (1)
[8]   An experimental evaluation of a loop versus a reference design for two-channel microarrays [J].
Vinciotti, V ;
Khanin, R ;
D'Alimonte, D ;
Liu, X ;
Cattini, N ;
Hotchkiss, G ;
Bucca, G ;
de Jesus, O ;
Rasaiyaah, J ;
Smith, CP ;
Kellam, P ;
Wit, E .
BIOINFORMATICS, 2005, 21 (04) :492-501
[9]   Detecting differential expressions in GeneChip microarray studies: A quantile approach [J].
Wang, Huixia ;
He, Xuming .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2007, 102 (477) :104-112
[10]   Bayesian model selection and model averaging [J].
Wasserman, L .
JOURNAL OF MATHEMATICAL PSYCHOLOGY, 2000, 44 (01) :92-107