- Methodology article
- Open Access
Between-groups within-gene heterogeneity of residual variances in microarray gene expression data
- Joaquim Casellas^{1}Email author and
- Luis Varona^{1, 2}
https://doi.org/10.1186/1471-2164-9-319
© Casellas and Varona; licensee BioMed Central Ltd. 2008
Received: 04 March 2008
Accepted: 04 July 2008
Published: 04 July 2008
Abstract
Background
The analysis of microarray gene expression data typically tries to identify differential gene expression patterns in terms of differences of the mathematical expectation between groups of arrays (e.g. treatments or biological conditions). Nevertheless, the differential expression pattern could also be characterized by group-specific dispersion patterns, although little is known about this phenomenon in microarray data. Commonly, a homogeneous gene-specific residual variance is assumed in hierarchical mixed models for gene expression data, although it could result in substantial biases if this assumption is not true.
Results
In this manuscript, a hierarchical mixed model with within-gene heterogeneous residual variances is proposed to analyze gene expression data from non-competitive hybridized microarrays. Moreover, a straightforward Bayes factor is adapted to easily check within-gene (between groups) heterogeneity of residual variances when samples are grouped in two different treatments. This Bayes factor only requires the analysis of the complex model (hierarchical mixed model with between-groups heterogeneous residual variances for all analyzed genes) and gene-specific Bayes factors are provided from the output of a simple Markov chain Monte Carlo sampling.
Conclusion
This statistical development opens new research possibilities within the gene expression framework, where heterogeneity in residual variability could be viewed as an alternative and plausible characterization of differential expression patterns.
Keywords
- Gene Expression Data
- Residual Variance
- Differential Expression Pattern
- Dispersion Pattern
- Microarray Gene Expression Data
Background
Gene expression measured by microarray chips is an emerging and cost-effective tool to assess the expression of thousands of genes in different tissues and organisms [1]. This technology has been intensively used to monitor changes in gene expression between tissues, treatments or time points in order to detect genes, or even metabolic pathways, involved in differential expression patterns [2]. As was highlighted by Wolfinger et al. [3], inference in microarray gene expression analyses is typically focused on gene-specific differences between mathematical expectations of two (or more) groups of biological conditions. However, discrepancies in gene expression could also be characterized by other statistics of interest like dispersion parameters [4, 5].
Heterogeneity of residual variances is a topic of main concern in biological studies where residual variance changes under alternative treatments [6, 7]. In gene expression analyses, heterogeneity of gene-specific residual dispersion has been addressed recently [8, 9], where hierarchical mixed models with gene-specific residual variances substantially reduced the rate of false positives and allowed for a more realistic fit of gene expression data [10]. Nevertheless, a common within-gene residual variance was assumed in these analyses, although within-gene discrepancies in the dispersion parameters could also be feasible. To our best knowledge, discrepancies in terms of gene-specific residual variance across different biological conditions (groups or arrays) have never been considered in the microarray literature. Besides a plausible scale effect on the residual variance due to changes in mathematical expectation under different groups of microarrays, within-gene heterogeneity of the residual variance could suggest a group-specific pattern of variability at the transcription level. Variability could be just due to within tissue variability in cell type composition, but may or may not be related to any meaningful difference in transcription.
The aim of this research is to propose a hierarchical mixed model analysis of microarray gene expression data assuming within-gene heterogeneous residual variances. In addition, a straightforward Bayes factor approach to test differences between two within-gene residual variances is developed, taking Verdinelli and Wasserman [11] and Varona et al. [12] as starting point. This methodology could open a new research field in gene expression analysis where differential gene expression will be characterized in terms of variability of the transcription process.
Methods
Hierarchical mixed model with within-gene heterogeneous residual variances
Assume as starting point n replicates of non-competitive hybridization microarray data with m genes (or probes; each probe is a fragment of complementary nucleic acid covering genomic or inter-genomic annotated regions) per array. Under the simplest design, these replicates are distributed in two different groups of treatments (e.g. tissues, species or time points) with r and s replicates per treatment, respectively (r + s = n). This gene expression data can be analyzed under the following hierarchical mixed model [13],
y = Xa + Z_{1}p_{1} + Z_{2}p_{2} +e,
where y is the (nm) × 1 column vector of intensity scores sorted by array within treatment within gene and e is the (nm) × 1 column vector of residuals. Effects in model were array (a; dimension n × 1) and probe (p_{1} and p_{2}; dimension m × 1) linked to y by appropriate incidence matrices (X, Z_{1} and Z_{2}, respectively). Vector e is assumed to be normally distributed [14],
e ~ N(0, R),
All the unknowns in model can be sampled under a Markov chain Monte Carlo framework by standard Gibbs sampling [15].
Bayes factor to test within-gene heterogeneous residual variances
and Bayesian likelihood,
p(y|a,p_{1},p_{2},R*) ~ N(Xa + Z_{1}p_{1} + Z_{2}p_{2}, R*).
Note that this prior distribution is the key point for the further calculation of the Bayes factor and covers all possible values taken by π_{ i }with equal probability, following Verdinelli and Wasserman [11] and Varona et al. [12]. As in previous model parameterization, all unknowns can be updated by Gibbs sampling [15] with the exception of π_{ i }that requires a Metropolis-Hastings step [16].
because p(π_{ i }= 0.5) was previously defined with the a priori distribution of π_{ i }. On the basis of GEAMM v.1.4 program [13], the Bayes factor developed above was implemented with FORTRAN90 language. All the subsequent analyses were performed with this software.
Example 1. Simulated data
Our Bayes factor approach was tested on simulated data sets under three different scenarios in order to check its statistical performance. For each scenario, a total of 100 data sets were generated, each one including 40 arrays (unrelated individuals), 10,000 genes per array and two groups of treatments (A and B). More specifically, scenario 1 (S1) assigned 20 arrays to each treatment without missing data, scenario 2 (S2) assumed an unbalanced design with 10 and 30 arrays for treatments A and B, respectively (no missing data), and scenario 3 (S3) assumed two groups of 20 arrays with a 5% of randomly distributed missing data. Intensity scores (y_{ ijk }) were simulated under the following model,
y_{ ijk }= μ + a_{ i }+ g_{ j }= e_{ ijk },
were μ was the overall mean arbitrarily fitted to 6, a_{ i }was the effect of each array sampled from an uniform distribution between 0 and 1, g_{ j }was the effect of the gene sampled from a Gaussian distribution with mean 0 and variance 1, and e_{ ijk }was the residual term obtained from a Gaussian distribution with mean zero and variance 0.1 × π_{ i }(Group A) or 0.1 × (1 - π_{ i }) (Group B). We assumed a unique (and plausible) value for the overall residual variance in order to allow for a direct comparison and interpretations of the results. Genes were grouped in five levels with different values of π_{ i }: 0.5 (6,000 genes), 0.4 (1,000 genes), 0.3 (1,000 genes), 0.2 (1,000 genes) and 0.1 (1,000 genes). Statistical performance of the developed BF to check differential expression in terms of dispersion pattern (residual variances) was compared with a well-known standard F-test. The effect of within-gene differential expression was not considered in order to allow for a straightforward comparison between Group A and Group B residual variances under F-test. Indeed, additional sources of variation were avoided in order to allow for a direct calculation of the F-test without requiring preliminary pre-correction of the data. Each data set was analyzed by the Bayes factor approach described above with the scaled χ^{-2} prior distribution for variances components generalized to proper flat priors (S^{2} = 0 and ν = -2) defined between > 0 and 1000. A unique Monte Carlo Markov chain with 100,000 elements was launched for each data set, after discarding the first 10,000 iterations as burn-in. Convergence was checked by the Raftery and Lewis [19] algorithm.
Example 2. Free-access gene expression data
To illustrate the methodology described above, we applied the model to free-access fibroblast gene expression data from 10 chimps and 11 gorillas (available at Gene Expression Omnibus [20], accession number GDS340). As described Karaman et al. [21], hybridization was performed in the Human Genome U95 Set platform (Affymetrix, Santa Clara, CA). A rough normalization was performed on the original data set by calculating multiplicative scaling factors on the basis of the median intensity of the 60th and 95th quintile of gene-expression scores [21]. All gene-expression scores below 100 were set to 100 in the original data set (untransformed scale) and. Within this context, all genes with one or more scores equal to 100 were removed from the final analysis. After editing, data set included gene expression scores of 3,700 genes, transformed by a base-2-logarithm as suggested Yeung et al. [22]. The analytical process followed the same specifications as for simulated data sets.
Results
Example 1. Simulated data
Simulated (π) and predicted ($\tilde{\pi}$; average of the posterior mean across-genes and replicates) heterogeneity and percentage of genes falling within each category of the Bayes Factor for the three simulation scenarios
1 ≥ | 3.16 ≥ | 10 ≥ | 31.62 ≥ | ||||
---|---|---|---|---|---|---|---|
π | $\tilde{\pi}$ ^{1} | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ | ${\text{BF}}_{{\text{HE/HO}}_{i}}$ |
< 1 | < 3.16 | < 10 | < 31.62 | < 100 | ≥ 100 | ||
Simulation scenario 1 | |||||||
0.5 | 0.498 | 88 to 98 | 1 to 12 | 1 to 3 | 0 | 0 | 0 |
0.4 | 0.412 | 60 to 84 | 2 to 28 | 1 to 16 | 0 to 8 | 0 to 4 | 0 to 4 |
0.3 | 0.301 | 20 to 48 | 16 to 36 | 12 to 28 | 4 to 12 | 0 to 12 | 0 to 16 |
0.2 | 0.196 | 4 to 16 | 8 to 32 | 4 to 28 | 4 to 24 | 8 to 28 | 16 to 36 |
0.1 | 0.121 | 0 | 0 to 4 | 0 to 4 | 0 to 8 | 0 to 8 | 84 to 100 |
Simulation scenario 2 | |||||||
0.5 | 0.492 | 90 to 98 | 2 to 11 | 0 to 2 | 0 to 1 | 0 | 0 |
0.4 | 0.407 | 64 to 89 | 1 to 22 | 0 to 11 | 0 to 7 | 0 to 2 | 0 to 1 |
0.3 | 0.296 | 23 to 51 | 11 to 27 | 9 to 22 | 2 to 9 | 0 to 8 | 0 to 7 |
0.2 | 0.201 | 10 to 21 | 7 to 30 | 2 to 25 | 2 to 25 | 3 to 27 | 9 to 31 |
0.1 | 0.115 | 0 to 1 | 0 to 8 | 0 to 10 | 0 to 12 | 0 to 17 | 76 to 100 |
Simulation scenario 3 | |||||||
0.5 | 0.508 | 90 to 97 | 1 to 11 | 1 to 4 | 0 | 0 | 0 |
0.4 | 0.409 | 62 to 87 | 1 to 26 | 0 to 14 | 0 to 6 | 0 to 4 | 0 to 3 |
0.3 | 0.308 | 23 to 51 | 15 to 34 | 10 to 25 | 3 to 10 | 0 to 9 | 0 to 10 |
0.2 | 0.211 | 5 to 17 | 7 to 31 | 4 to 26 | 4 to 23 | 7 to 26 | 14 to 31 |
0.1 | 0.119 | 0 to 1 | 0 to 8 | 0 to 8 | 0 to 10 | 0 to 11 | 77 to 100 |
As was expected, ${\text{BF}}_{{\text{HE/HO}}_{i}}$ showed an overall increase when π values used in the simulation process decreased (Table 1). The percentage of ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 1 decreased with π, it ranging between 60% and 84% (π = 0.4), between 20% and 48% (π = 0.3), between 4% and 16% (π = 0.2) and 0% (π = 0.1). Additionally, evidences favoring the presence of heterogeneous variances increased when gene expression data were simulated under small π, 84% to 100% of the genes reaching ${\text{BF}}_{{\text{HE/HO}}_{i}}$ ≥ 100 for π = 0.1 simulated genes (decisive evidence according to Jeffreys's [23] scale). This increase in ${\text{BF}}_{{\text{HE/HO}}_{i}}$ when the bounded variable (π) departed from the "null hypothesis" (π = 0.5) agrees with previous results published by García-Cortés et al. [18] and Casellas et al. [24] with the same Bayes factor approach although adapted to test heritability of linear and threshold traits.
Example 2. Free-access gene expression data
Distribution of genes according to BF_{HE/Hoi}, and across-genes average estimates (and empirical standard error across average estimates) for the heterogeneity parameter and residual variance
${\text{BF}}_{{\text{HE/HO}}_{i}}$ | Genes | π*^{1} | ${\sigma}_{e\left(i\right)}^{2}$ |
---|---|---|---|
${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 1 | 2,511^{2} (67.9)^{3} | 0.431 (0.001) | 0.244 (0.005) |
1 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 3.16 | 743 (20.1) | 0.316 (0.001) | 0.243 (0.008) |
3.16 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 10 | 302 (8.2) | 0.249 (0.001) | 0.241 (0.013) |
10 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 31.62 | 80 (2.2) | 0.262 (0.022) | 0.725 (0.021) |
31.62 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 100 | 41 (1.1) | 0.275 (0.030) | 0.665 (0.050) |
${\text{BF}}_{{\text{HE/HO}}_{i}}$ ≥ 100 | 23 (0.6) | 0.278 (0.042) | 0.744 (0.061) |
As was expected, the across-genes average π values (transformed to 1 - π when π was greater than 0.5) was maximum for genes with ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 1 (0.431), whereas this parameter reduced to around 0.25 when ${\text{BF}}_{{\text{HE/HO}}_{i}}$ increased (Table 2). Note that in our analyses, extreme values of π values (< 0.1 or > 0.9) were scarce (Figure 2). For the joint residual variance (${\sigma}_{e\left(i\right)}^{2}$), averages ranged between 0.241 and 0.744 (Table 2).
Discussion
Within-gene heterogeneity of the residual dispersion patter in real data
Distribution of relevant genes according to BF_{HE/Hoi}and under two different analytical models
Posterior probability^{1} < 0.05 | Posterior probability^{1} < 10^{-5} | |||
---|---|---|---|---|
${\text{BF}}_{{\text{HE/HO}}_{i}}$ | Model HE | Model HO^{2} | Model HE | Model HO^{2} |
${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 1 | 995 | 1058 | 210 | 240 |
1 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 3.16 | 337 | 349 | 73 | 79 |
3.16 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 10 | 161 | 168 | 31 | 33 |
10 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 31.62 | 45 | 46 | 10 | 10 |
31.62 ≥ ${\text{BF}}_{{\text{HE/HO}}_{i}}$ < 100 | 28 | 32 | 2 | 4 |
${\text{BF}}_{{\text{HE/HO}}_{i}}$ ≥ 100 | 8 | 9 | 3 | 4 |
Bayes factor to compare dispersion patterns in microarray studies
Although gene expression analyses have been typically focused on the comparison between mathematical expectations of two or more (within-gene) groups of arrays, the analytical approach developed in the present paper allow for an alternative characterization of differential expression patterns. Moreover, it allows for an appropriate data modeling when within-gene heterogeneity of residual variances exists. This approach could be viewed as statistically inefficient for those genes with homogeneity of residual variances [28, 29]. Nevertheless, the aim of this research was to provide an accurate method to compare dispersion patterns, whereas differences between the mathematical expectances of groups of treatments are not of interest in this case. This test could also be applied to experiments with less replicates per group although its results must be taken with caution, given the inherent loss of robustness under small data sets,. As is shown in Figure 1, our Bayes factor performed similarly to the standard F-test, with a stable a coherent behavior under moderate sample sizes (number or arrays per group). Although the Bayes factor approach has been described under a simple scenario (simulated datasets), this can be easily generalized to more complex frameworks without additional requirements. Within this context, across-genes shrinkage of residual variances is a topic of main interest in microarray research [29, 30] which can be easily adapted to the hierarchical mixed model applied above. Indeed, several Bayesian methods proposed for residual variances shrinkage [31, 32] can be applied to both residual variances and heterogeneity parameters, and the calculation of the Bayes factor does not substantially change within-gene or within a group of genes. In a similar way, other approaches can also be jointly implemented with the developed Bayes factor such as mixtures of distributions [33–35]. If several sources of variation are expected on the residual term, the mixed model with within-gene heterogeneous residual variances could be viewed as a useful tool to characterize their distribution pattern, the Bayes factor being a straightforward way to check their statistical relevance. Within this context, our Bayes factor procedure could provide preliminary results required for the application of more complex and computational demanding approaches like the mixed model with log-transformed hierarchical residual variances developed by Jaffrezic et al. [36].
Changes in residual dispersion patterns could be due to a scale effect when mathematical expectations of two (or more) groups of arrays are different. Nevertheless, this scale-related hypothesis was only attributable to a small percentage of genes with heterogeneous residual variances (Tables 3), whereas more than 75% of differential dispersion patters must be related to other unknown causes in the analyzed free-access microarray data. These changes in the dispersion pattern were previously suggested in genes involved in cancer pathogenesis [4, 37] and other diseases [38], although within-gene residual heterogeneity is not commonly considered in gene expression analyses [8, 9]. Moreover, heterogeneity in gene expression increases with age [5] and therefore, our approach could be of special interest in time-series analyses where individuals at different ages are compared. As a whole, the hierarchical mixed model with within-gene heterogeneous residual variances allows for a new and more accurate modeling of gene expression data with appealing perspectives, and the Bayes factor developed is an easy way to check differences between within-gene residual variances.
Under the Bayesian framework, model comparison is usually made by calculating Bayes factors [39], the ratio between the marginal probabilities of the data given the tested models and after integrating out all parameters in the models. The Bayes factor developed by Verdinelli and Wasserman [11] from generalization of the Savage-Dickey density ratio, and adapted to the animal breeding context by García-Cortés et al. [18] and Varona et al. [12], has been easily applied to check heterogeneous residual variances in gene expression analyses when two groups of treatments are compared. It provides a rigorous and clear framework to compare competing models, avoiding the calculation of significance levels and without depending upon asymptotic properties of frequentist estimators [40], Bayes factor behaves well even when the bounded variable to be tested is either close to the boundary of the parametric space [18]. In addition, Bayes factor provides a ratio of probabilities between models, without any requirement to define the null or the alternative hypothesis, without trying to discard the null hypothesis in favor of an alternative hypothesis, and without referring to the asymptotic properties of the frequentist estimators [12].
Although other Bayes factor approaches could be used, the Verdinelli and Wasserman's [11] approach allows for a simplified calculation, where only the analysis of the complex model is necessary. Moreover, a unique analysis is required to calculate all the gene-specific Bayes factors, and chain storage is not needed because only the (within-gene) average of the full conditional densities at π_{ i }= 0.5 is used during calculation. Under alternative Bayes factor approaches [39], an additional model with π = 0.5 for the gene to be tested (and sampling π for the remaining genes) must be analyzed for each gene, in order to obtain the gene-specific Bayes factor comparing heterogeneous versus homogeneous residual variances.
Given the null a priori information about the expected distribution of π, we assumed a flat prior distribution between 0 and 1 in order to give the same a priori probability to all plausible values. This is a standard assumption for the Verdinelli and Wasserman's Bayes factor [11, 12], although other prior distributions could also become reasonable. It is important to note that p(π) equally influences both p(π_{ i }= 0.5) and p(π_{ i }= 0.5|y) terms and therefore, the Bayes factor must be relatively robust to prior modifications. In the light of the results obtained from the analysis of great ape gene expression data, a priori distributions favoring values close to 0.5 and with decreasing probability in their tails could be realistic. Within this context, Gaussian, Laplace and Student's t distributions with mean 0.5 and truncated at 0 and 1 could be useful a priori distributions, among others. Nevertheless, further studies are required to confirm this pattern in real gene expression data.
As was recently demonstrated at the gene-specific level [10], an accurate modeling of residual dispersion allows for a more realistic fit of gene expression data. Moreover, it has a relevant impact on the rate of false positives when gene expression is characterized in terms of mathematical expectations or their differences [8–10]. In this manuscript, we have adapted Lo and Gottardo [10] mixed model to account for within gene heterogeneity of residual variances, where a relevant incidence of within-gene heterogeneity has been revealed in real gene expression data. Moreover, this heterogeneity can be easily checked gene-by-gene by applying a straightforward Bayes factor with a minimal increase in computational requirements. Note that differences between average gene expression without assuming equal residual variances is a typical example of the Behrens-Fisher problem [41], which could be easily by-passed in microarray analyses by appropriately adapting Welch's [42]t- test. Nevertheless, our approach seeks a novel point of view were, not only differences between mathematical means are tested but differences between residual dispersion patterns must also be checked and characterized. In addition, our Bayes factor allows to detect those genes with heterogeneous residual variances where Behrens-Fisher problem [41] holds.
Conclusion
Accounting for within-gene between-groups heterogeneous residual variances in mixed model analyses of non-competitive microarray gene expression data (or even competitive microarray gene expression data after suitable data editing) allows to characterize differential expression patterns in terms of gene expression variability. The Bayes factor approach here presented provides a straightforward comparison between within-gene group-specific residual variances with minimal computing requirements. This methodology is freely available in GEAMM v.1.7 software [43].
Declarations
Acknowledgements
The research contract of J. Casellas was partially financed by Spain's Ministerio de Educación y Ciencia (program Juan de la Cierva) within the context of project AGL2004-08368-CO3.
Authors’ Affiliations
References
- Brown P, Botstein D: Exploring the new world of the genome with DNA microarrays. Nat Genet. 1999, 21: 33-37. 10.1038/4462.PubMedView ArticleGoogle Scholar
- Lander ES: Array of hope. Nat Genet. 1999, 21 (suppl): 3-4. 10.1038/4427.PubMedView ArticleGoogle Scholar
- Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS: Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol. 2001, 8: 625-637. 10.1089/106652701753307520.PubMedView ArticleGoogle Scholar
- Bachtiary B, Boutros PC, Pintilie M, Bastianutto C, Li J-H, Schwock J, Zhang W, Penn LZ, Jurisica I, Fyles A, Liu F-F: Gene expression profiling in cervical cancer: an exploration of intratumor heterogeneity. Hum Cancer Res. 2006, 12: 5632-5640. 10.1158/1078-0432.CCR-06-0357.Google Scholar
- Somel M, Khaitovich P, Bahn S, Pääbo S, Lachmann M: Gene expression becomes heterogeneous with age. Current Biol. 2006, 16: R360-View ArticleGoogle Scholar
- Gill JL: Analyses of data with heterogeneous variance: a review. J Dairy Sci. 1971, 54: 369-373.View ArticleGoogle Scholar
- Weigel KA, Gianola D: Estimation of heterogeneous within-herd variance components using empirical Bayes methods: a simulation study. J Dairy Sci. 1992, 75: 2824-2833.PubMedView ArticleGoogle Scholar
- Kendziorski CM, Newton MA, Lan H, Gould MN: On parametric empirical Bayes methods for comparing multiple groups using replicate gene expression profiles. Stat Med. 2003, 22: 3899-3914. 10.1002/sim.1548.PubMedView ArticleGoogle Scholar
- Newton MA, Noueiry A, Sarkar D, Ahlquist P: Detecting differential gene expression with a semiparametrical hierarchical mixture method. Biostatistics. 2004, 5: 155-176. 10.1093/biostatistics/5.2.155.PubMedView ArticleGoogle Scholar
- Lo K, Gottardo R: Flexible empirical Bayes methods for differential gene expression. Bioinformatics. 2007, 23: 328-335. 10.1093/bioinformatics/btl612.PubMedView ArticleGoogle Scholar
- Verdinelli I, Wasserman L: Computing Bayes factors using a generalization of the Savage-Dickey density ratio. J Am Stat Assoc. 1995, 90: 614-618. 10.2307/2291073.View ArticleGoogle Scholar
- Varona L, García-Cortés LA, Pérez-Enciso M: Bayes factors for detection of Quantitative Trait Loci. Genet Sel Evol. 2001, 33: 133-152. 10.1051/gse:2001113.PubMedPubMed CentralView ArticleGoogle Scholar
- Casellas J, Ibáñez-Escriche N, Martínez-Giner M, Varona L: GEAMM v.1.4.: a versatile program for mixed model analysis of gene expression data. Anim Genet. 2008, 39: 89-90. 10.1111/j.1365-2052.2007.01670.x.PubMedView ArticleGoogle Scholar
- Cui X, Churchill CA: Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 2003, 4: 210-10.1186/gb-2003-4-4-210.PubMedPubMed CentralView ArticleGoogle Scholar
- Gelfand A, Smith AFM: Sampling based approaches to calculating marginal densities. J Am Stat Assoc. 1990, 85: 398-409. 10.2307/2289776.View ArticleGoogle Scholar
- Hastings WK: Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 1970, 57: 97-109. 10.1093/biomet/57.1.97.View ArticleGoogle Scholar
- Wang CS, Rutledge JJ, Gianola D: Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genet Sel Evol. 1994, 26: 91-115. 10.1051/gse:19940202.PubMed CentralView ArticleGoogle Scholar
- García-Cortés LA, Cabrillo C, Moreno C, Varona L: Hypothesis testing for the genetic background of quantitative traits. Genet Sel Evol. 2001, 33: 3-16. 10.1051/gse:2001107.PubMedPubMed CentralView ArticleGoogle Scholar
- Raftery AE, Lewis SM: How many iterations in the Gibbs sampler?. Bayesian Statistics IV. Edited by: Bernardo JM, Berger JO, Dawid AP, Smith AFM. 1992, Oxford, Oxford University Press, 763-773.Google Scholar
- Gene Expression Omnibus. [http://www.ncbi.nlm.nih.gov/geo/]
- Karaman MW, Houck ML, Chemnick LG, Nagpal S, Chawannakul D, Sudano D, Pike BL, Ho VV, Ryder OA, Hacia JG: Comparative analysis of gene-expression patterns in human and African great ape. Genome Res. 2003, 13: 1619-1630. 10.1101/gr.1289803.PubMedPubMed CentralView ArticleGoogle Scholar
- Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL: Model-based clustering and data transformation for gene expression data. Bioinformatics. 2001, 17: 977-987. 10.1093/bioinformatics/17.10.977.PubMedView ArticleGoogle Scholar
- Jeffreys H: Theory of probability. 1961, Oxford, Clarendon PressGoogle Scholar
- Casellas J, Piedrafita J: Bayes factor for testing the genetic background of quantitative threshold traits. J Anim Breed Genet. 2006, 123: 301-306. 10.1111/j.1439-0388.2006.00606.x.PubMedView ArticleGoogle Scholar
- Newton MA, Raftery AE: Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion). J Royal Stat Soc B. 1994, 56: 3-48.Google Scholar
- Spiegelhalter DJ, Best NG, Carlin BP, Linde van der A: Bayesian measures of model complexity and fit (with discussion). J Royal Stat Soc B. 2002, 64: 583-639. 10.1111/1467-9868.00353.View ArticleGoogle Scholar
- Searle SR, Casella G, McCulloch CE: Variance components. 1992, New York, John Wiley & SonsView ArticleGoogle Scholar
- Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics. 2001, 17: 509-519. 10.1093/bioinformatics/17.6.509.PubMedView ArticleGoogle Scholar
- Cui X, Hwang JTG, Qiu J, Blades NJ, Churchill GA: Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatisitics. 2005, 6: 59-75. 10.1093/biostatistics/kxh018.View ArticleGoogle Scholar
- Lonnstedt I, Speed T: Replicated microarray data. Statistica Sinica. 2002, 12: 31-46.Google Scholar
- Smyth GK: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: article 3Google Scholar
- Feng S, Wolfinger RD, Chu TM, Gibson GC, McGraw LA: Empirical Bayes analysis of variance component models for microarray data. J Agric Biol Env Stat. 2006, 11: 197-209. 10.1198/108571106X110676.View ArticleGoogle Scholar
- Lee MLT, Kuo FC, Whitemore GA, Sklar J: Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci USA. 2000, 97: 9834-9838. 10.1073/pnas.97.18.9834.PubMedPubMed CentralView ArticleGoogle Scholar
- Ibrahim JG, Chen M-H, Gray RJ: Bayesian models for gene expression with DNA microarray data. J Am Stat Assoc. 2002, 97: 88-99. 10.1198/016214502753479257.View ArticleGoogle Scholar
- McLachlan GJ, Bean RW, Peel D: A mixture model-based approach to the clustering of microarray expression data. Bioinformatics. 2002, 18: 413-422. 10.1093/bioinformatics/18.3.413.PubMedView ArticleGoogle Scholar
- Jaffrezic F, Marot G, Degrelle S, Hue I, Foulley JL: A structural mixed model for variances in differential gene expression studies. Genet Res. 2007, 89: 19-25. 10.1017/S0016672307008646.PubMedView ArticleGoogle Scholar
- Ho SB, Niehans GA, Lyftogt C, Yan PS, Cherwitz DL, Gum ET, Dahiya R, Kim YS: Heterogeneity of mucin gene expression in normal and neoplastic tissues. Cancer Res. 1993, 53: 641-651.PubMedGoogle Scholar
- Galligan CL, Baig E, Bykerk V, Keystone EC, Fish EN: Distinctive gene expression signatures in rheumatoid arthritis synovial tissue fibroblast cells: correlates with disease activity. Genes Immunity. 2007, 8: 480-491. 10.1038/sj.gene.6364400.PubMedView ArticleGoogle Scholar
- Kass RE, Raftery AE: Bayes factors. J Am Stat Assoc. 1995, 90: 773-795. 10.2307/2291091.View ArticleGoogle Scholar
- Stram DO, Lee JW: Variance components testing in longitudinal mixed effects model. Biometrics. 1994, 50: 1171-1177. 10.2307/2533455.PubMedView ArticleGoogle Scholar
- Fisher RA: The fiducial argument in statistical inference. Ann Eugenics. 1935, 6: 391-398.View ArticleGoogle Scholar
- Welch BL: The significance of the difference between two means when the population variances are unequal. Biometrika. 1938, 29: 350-362.View ArticleGoogle Scholar
- GEAMM v.1.7. [http://www.bdporc.irta.es/Publicacions/GEAMM.zip]
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