- Methodology article
- Open Access
A comparison of probe-level and probeset models for small-sample gene expression data
© Stevens et al; licensee BioMed Central Ltd. 2010
Received: 18 December 2009
Accepted: 26 May 2010
Published: 26 May 2010
Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.
The performance of several probe-level and probeset models was assessed graphically and numerically using three spike-in datasets. Based on the Affymetrix GeneChip, a novel nested factorial model was developed and found to perform competitively on small-sample spike-in experiments.
Statistical methods with test statistics related to the estimated log fold change tend to be more consistent in their performance on small-sample gene expression data. For such small-sample experiments, the nested factorial model can be a useful statistical tool. This method is implemented in freely-available R code (affyNFM), available with a tutorial document at http://www.stat.usu.edu/~jrstevens.
Over the past decade, gene expression microarray technology  has become a relatively common tool in the biological sciences. A traditional application of this technology is to measure gene expression in two or more groups of individuals that differ according to some characteristic of interest, and then to identify genes whose expression levels change systematically between groups.
An ongoing collaboration with the J. R. Simplot Company involves the cloning of cattle using nuclear transfer (NT). Published results include an evaluation of the differences in NT success rates using cow vs. heifer oocytes  and an evaluation of the effects of various time intervals between fusion and activation on the development of bovine NT pregnancies . Recently, microarray technology has been employed in this collaboration, in particular examining the gene expression differences between NT and control (non-NT) samples at various stages of the NT cloning process . One goal of the microarray application in this work is to identify a potential genetic basis for successful NT pregnancies, as well as a potential genetic basis for NT failure. Once understood, this genetic basis can be used to achieve a greater success rate in NT pregnancies, so that attributes such as carcass quality can be maximized and preserved in successive generations of cattle.
Because of the high cost in time and money for NT and gene expression studies, the high level of work necessary to perform NT, and the difficulty in achieving successful bovine NT pregnancies, the sample sizes in these microarray experiments tend to be quite low. While many biomedical applications currently involve dozens of samples, the microarray work done thus far with bovine NT studies has been limited to six to eight samples for each comparison. Much of the statistical methodology that has been developed for microarray data analysis over the past decade  has been driven by medical applications such as human cancer research, where samples are much more abundant. As a result, the most common statistical models assume a larger sample size than has been practical in the bovine NT work.
This study evaluates alternative statistical models in an effort to identify appropriate approaches for microarray experiments with small sample sizes. The Results and Discussion section consists of a summary of these methods, including a novel presentation of our probe-level nested factorial model, and a discussion of the relative performance of these methods on spike-in data.
The relative performance of competing statistical methods can be assessed using spike-in data, and the most promising method was applied to the bovine NT data. For this study, all data considered are of a simple treatment vs. control design.
The motivating bovine NT dataset, mentioned in the Introduction subsection, was taken from an experiment in the cloning of cattle (NT). Tissue samples were taken and prepared from cotyledon (part of the uterine side of the placenta) in an impregnated cow. Four of these pregnancies involved NT and three were control, making 7 total arrays. For these data, we are interested in identifying genes that behave differently in the NT samples than in the non-NT samples. The Affymetrix bovine array was used, with 24,128 probesets.
In spike-in microarray experiments, the "truth" is known a priori. In particular, genes whose mRNA has been artificially increased in a sample to be hybridized onto the array are said to be "spiked-in." The "expression levels" of these genes can be controlled by modifying their spike-in concentrations. Thus their "expression levels" can be made to change across "treatments." That is, they are known to be differentially expressed. Hence it can be verified how well statistical models identify differentially expressed genes in general by how well they find spiked-in genes in these types of experiments.
Data from three spike-in experiments were used to compare the different statistical models. The Affymetrix HGU95A spike-in data  consist of 59 arrays and 12,626 probesets on each array, out of which 14 probesets were spiked-in. For this small-sample study, only 8 arrays were used, corresponding to groups M-T of wafer 1532 of the spike-in data, with 4 control and 4 treatment arrays.
The Affymetrix HGU133A spike-in data  consist of 42 arrays and 22,300 probesets on each array, out of which 42 probesets were originally spiked-in, with 3 more probesets known to contain probes exactly matching the spiked sequences. Subsequent work  identified 22 additional spike-in probesets, but the inclusion of these additional 22 did not substantially change the results of our study. We considered the HGU133A experiment as having 45 spiked-in probesets. For this small-sample study, only 6 arrays were used, corresponding to experiments 5 and 6 of the spike-in data, with 3 control and 3 treatment arrays.
The third and final spike-in dataset used was the "Golden Spike" dataset . There has been some question regarding the credibility of this experiment [9, 10]. Specifically, the dataset uses potentially unrealistically high levels of spike-in concentration, and the spike-ins are all up-regulated in the treatment group relative to the control group. However, it is still sometimes used as a tool in comparing statistical models, and we include it here for discussion. This experiment consists of 6 arrays (3 control, 3 treatment), with 1,331 of 14,010 probesets spiked-in.
Results and Discussion
The Affymetrix GeneChip is among the most commonly used microarray platforms, and typically represents each gene on the array as a set of 11-20 probes, called a probeset. Preprocessing methods such as RMA  combine probe-level intensities to give an expression estimate of each gene in each sample of the experiment. Our initial hypothesis in this study was that using only preprocessed data in assessing differential expression does not take full advantage of available information at the probe level and could be improved. This loss of information seems especially concerning in cases such as the motivating bovine NT study, where there are a relatively small number of samples. Probe-level models for differential expression provide a possible alternative, without this loss of information.
The purpose of this study was to compare several probe-level models with one of the most popular probeset models. In this section, we summarize the approaches of several methods (both probeset and probe-level) to calculate test statistics for evaluating differential expression. Unless otherwise stated, RMA preprocessing  was done prior to all probeset models, and prior to all probe-level models, the background correction and quantile normalization steps of RMA were performed. (As an alternative, GCRMA preprocessing  was done prior to all probeset models, and prior to all probe-level models, GCRMA background correction  followed by quantile normalization was performed.) All analyses were conducted in R , making use of several packages in Bioconductor .
Y ijl is the background-corrected quantile-normalized log-scale perfect match intensity for probe l of the gene on array j under treatment level i, T i is the treatment effect, Sj(i)is the subject (or array) effect nested within treatment, P l is the probe effect, (TP) il is an interaction between treatment and probe, and (SP)jl(i)is used as the error term. This model clearly takes into account any probe information that may be lost in a probeset model.
Since we are interested in determining whether or not each gene is differentially expressed between treatment levels, we are testing H0: T1 = T2, and we can look at an F-statistic for the treatment effect. This statistic can be obtained via restricted maximum likelihood (REML) estimation, as implemented in the lme function of the nlme R package [15, 16].
It is important to note that, due to the nature of the nested design, an iterative process (REML) is necessary to obtain the F-statistic for each gene. This iterative process takes some time and can be very slow when there is a very large number of factor levels, such as a large number of probes per gene on some array types (as in tiling arrays).
Here, is a vector of estimated array effects and Σ is the portion of the estimated variance/covariance matrix corresponding to . The length of vector c is the total number of arrays in the dataset. The elements of c depend on how many arrays are in each treatment level. For a simple two-group design, element j of c is if array j is in treatment group 1, if array j is in treatment group 2, and 0 otherwise, where n i is the number of samples in treatment group i. Then c' is the log fold change across treatments. PLM is a very fast process since it involves nothing more than some matrix computations, which are very efficient in R.
Here, Y ijk is the log-scale expression level for gene k on array j in treatment group i, and βk,1is the treatment effect. T i is the treatment level of the array, coded as a dummy variable (here, 0 for control and 1 for treatment). The ε ijk is an error term with Var(ε ijk ) = .
Under the H0, follows a t-distribution with degrees of freedom d0 + d k . LE is a very efficient method in the sense that it runs as fast as PLM. The LE method is implemented in the R package limma .
To fit the Gaussian mixture, after using such plots to decide the number of components to include, we used the implementation in the R package mclust . To test for differential expression, we use as the test statistic the conditional probability that the gene belongs to the most extreme cluster (in terms of the absolute treatment effect) from the Gaussian mixture model.
One drawback to this model is that it can not be automated. One has to manually choose the number of components to use in the Gaussian mixture model by looking at these plots. It is not always clear how many components to choose for the mixture model (see Figure 1ab). Based on recommendations from Lemieux (personal communication), anywhere in the order of 5-15 components seems reasonable.
The probe-level locally moderated weighted median-t method (PLW) uses a hierarchical Bayes model to obtain a moderated, weighted t-statistic for each PM probe [21, 22]. With the background-corrected, quantile-normalized data, a weighted, moderated test statistic is calculated for each probe. The test statistic for whether or not gene k is differentially expressed is then calculated by taking the median of the moderated test statistics for each probe within gene k. This method has been implemented in the R package plw [21, 22].
For purposes of comparison, the naive fold change (FC) across treatments is also considered as a test statistic in identifying differentially expressed genes. For this study, the (log) fold change was calculated as the numerator of Equation 3, i.e., c' , where c and are as previously described in the PLM discussion.
The robustified MANOVA (RMANOVA) approach is a probe-level model based on the framework of multivariate analysis of variance , fit on a per-gene basis. For a given gene, let yj(i)be the vector of background-corrected quantile-normalized log-scale perfect match intensities on array j within treatment level i. For treatment level i, let μ(i)represent the corresponding vector of expected values for treatment level i. Similarly, let μ be the corresponding vector of expected values across all treatment levels. In the two-treatment case (of interest here), the null hypothesis of interest is H0: μ(1) = μ(2).
Here the sum and median are applied component-wise.
Here again the median is applied component-wise. Both of these test statistics are constructed with the same philosophy of the F-statistic in a one-way ANOVA model, as the ratio of between-treatment to within-treatment error. However, unlike the ANOVA F-statistic, the numerators of these test statistics are not directly related to the log fold change. Still, larger test statistics are taken as greater evidence of differential expression. Xu and Cui (2008)  provide Matlab functions for this method, but we use a custom R implementation for convenience in comparison.
where μ l is the population mean for probe l, and Ti, lis the effect of treatment level i for probe l. Using traditional parametric ANOVA methods, a p-value (p l ) is obtained for the null hypothesis H0: T1, l= T2, l, for each probe l of the gene. (We encountered undefined p l values for the occasional probes where the Y values were the same on all arrays. This was more common with very small numbers of arrays. Our strategy was to reset these p l values to 1 to represent no evidence of differential expression of the probe.)
For the overall test of differential expression for the gene, the test statistic for the gene is the median (over all probes l) of 1 - p l . Larger values of the test statistic are indicative of greater evidence of differential expression. This FIRSTP approach is similar in spirit to the PLW method (because both utilize medians of probe-level test statistics), but differs in that the "first principles" philosophy limits the number of assumptions and model components. We used a custom R implementation of this FIRSTP method.
Propagating Uncertainty in Microarray Analysis, or PUMA, accounts for uncertainty of the measured value of each gene when doing these experiments, and includes a probe-specific parameter in a probabilistic model [25–27]. The model accounts for information from both the perfect match (PM) and mismatch (MM) probes, instead of treating MM probes as background.
The first step of PUMA is to preprocess the raw microarray data using a method called multi-chip modified gamma Model for Oligonucleotide Signal, or multi-mgMOS. This method uses a hierarchical Bayes model to obtain the expression level for each gene.
After this preprocessing, PUMA uses a hierarchical model to obtain the posterior distribution on μ i , the mean expression value of a gene for treatment level i. Since this posterior distribution cannot be written in closed form, an EM algorithm (using MCMC) is used to approximate the distribution. The test statistic for each gene is then the probability that the gene is up-regulated (or down-regulated); i.e., the probability that μ1 >μ2 (or μ2 >μ1), is then calculated. These test statistics can be converted into a "p-like-value," which is the probability that each gene is differentially expressed. The p-like-values are more comparable to the p-values returned from other methods, in that smaller values represent stronger evidence of differential expression. These methods have been implemented in the R package puma .
In order to understand how well each of the previously defined statistical models can identify differentially expressed genes, each was compared simultaneously by considering the true positive rate (TPR) and the false positive rate (FPR) in the three spike-in datasets described previously. For each model, a test statistic was obtained for each gene and the absolute values of these test statistics were sorted in increasing order. The TPR and FPR values were defined as in Bolstad (2004) : For the entire set of sorted test statistics, count the number of spike-in genes that are called significant (i.e. that are in the set being considered) and divide that by the total number of spike-in genes. Call that the TPR for the first set of sorted test statistics. Count the number of non-spike-in genes called significant and divide that by the total number of genes that are not spiked-in. Call this the FPR for the first set of sorted test statistics. Then omit the test statistic with the smallest absolute value and obtain a TPR and FPR for that subset of test statistics. Repeat this process by removing the test statistic with the smallest absolute value each time, obtaining TPRs and FPRs for each subset of test statistics. For the last iteration, the subset will only contain one test statistic. The total number of TPR and FPR values obtained will be equal to the total number of genes in the dataset.
For each spike-in dataset, a receiver operating characteristic (ROC) curve was created to visually assess how well each model performed. The ROC curve is a plot of TPR vs. FPR. A higher curve indicates higher performance in identifying differentially expressed genes. "Higher" here can be assessed both graphically (with the ROC curve) and numerically (with the AUC, or area under the ROC curve).
Because our focus is performance in small-sample studies, we also looked at subsets of the spike-in studies. For example, the HGU95A dataset has four arrays in each of two treatments. In addition to this 4 × 4 comparison, we also tested for differential expression in all 16 3 × 3 and all 36 2 × 2 comparisons. For each method fit, the TPR and FPR were averaged across all 16 3 × 3 comparisons, and separately averaged across all 36 2 × 2 comparisons. The HGU133A and Golden Spike datasets both have three arrays in each of two treatments. In addition to their 3 × 3 comparisons, we averaged the TPR and FPR across their respective 9 2 × 2 comparisons.
Area Under Curve (AUC) values for each model on each data subset, using RMA preprocessing.
4 × 4
3 × 3s
2 × 2s
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Area Under Curve (AUC) values for each model on each data subset, using GCRMA preprocessing.
4 × 4
3 × 3s
2 × 2s
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The NFM method is consistently among the top performers at low FPR, except in the Golden Spike data. (The poorer performance in the Golden Spike data is examined in the two following paragraphs.) The PLM methods perform well in the HGU95A data, and are among the best at low FPR for the Golden Spike data with GCRMA preprocessing. The LE method has a slightly variable performance, doing well in the HGU95A data, and among the best at low FPR for the HGU133A data with RMA preprocessing, but among the worst at low FPR for the HGU133A data with GCRMA preprocessing. The PLLM method is more variable, as it is clearly the best performer on the Golden Spike data with RMA preprocessing, and among the best at low FPR on the HGU133A data with GCRMA preprocessing, but among the worst at low FPR for the Golden Spike data with GCRMA preprocessing. The PLW method is among the best for the Golden Spike data with GCRMA preprocessing, and at low FPR for the HGU133A data with GCRMA preprocessing. The FC method is among the best for the Golden Spike data with GCRMA preprocessing and at low FPR for the HGU133A data with RMA preprocessing, but among the worst at low FPR for subsets of the HGU95A and HGU133A data with GCRMA preprocessing. RMANOVA tends to be among the lowest performers, but gives the best overall performance on the HGU133A data with RMA preprocessing (and even there, it is a low performer at low FPR). The FIRSTP method gives a variable performance, and is never the best or worst overall. PUMA is the top overall performer on the HGU133A data with GCRMA preprocessing, but there (and consistently in the other data sets) it is among the low performers at low FPR.
The methods comprising this fourth level of performance (NFM, PLM, LE, PLW, FC, and FIRSTP) all share a common characteristic, that their test statistics are related to the simple estimated log fold change. This is due to their being based on traditional ANOVA-type models. (The PLLM method begins with the ANOVA-type model in Equation 10, but the test statistic is actually based on the result of the subsequent Gaussian mixture model.) When this estimated log fold change is low, the test statistics for these methods tend to be lower. This is what caused these methods' performance to drop in the Golden Spike data at fold-changes 1.5 and 1.7. Figure 7f shows that at these fold-changes, the estimated log fold changes were noticeably and systematically lower.
All Methods - Bovine NT Application
NFM P-Value Calculation
Based on its consistently strong performance on the spike-in data, including small-sample subsets, NFM was applied to the motivating bovine NT data. To assess statistical significance in this application, p-values (with subsequent multiple testing adjustment) are necessary. The F-statistics from NFM have a theoretical sampling distribution, where g is the number of treatment levels and n i is the number of samples within treatment level i. In other words, when the null hypothesis H0: T1 = T2 is true, the F-statistics will theoretically have this approximate distribution, and p-values could be obtained as tail probabilities. The validity of this distributional assumption can be assessed using the F-statistics for the non-spiked-in genes in the three spike-in datasets, because the null hypothesis is known to be true for these genes.
An alternative sampling distribution is generated by a permutation approach. Existing permutation or bootstrap approaches permute or resample treatment labels, and the p-value for each gene is the proportion of all resamples producing a test statistic for the gene at least as extreme as the original test statistic for the gene. An implementation of such an approach  is found in the R package multtest . Using this existing approach, the smallest p-value possible will be the inverse of the number of resamples. For this reason, such a per-gene permutation-based approach with very small sample sizes (as in the current application) will not yield small enough p-values to claim statistical significance, because the number of possible permutations is low.
For example, in the HGU133A dataset, there are 3 "control" and 3 "treatment" arrays under consideration, so there are 6!/(3!·3!) = 20 possible permutations of treatment labels. Due to the one-sided nature of the NFM test statistic, there are actually only half that number of permutations that will give unique (or non-redundant) test statistics for any given gene. For example, the treatment labels (C,C,C,T,T,T) and (T,T,T,C,C,C) will produce identical F-statistics for a given gene, where C represents control and T represents treatment. Then the smallest p-value using this per-gene permutation approach is 1/10, and no claims of significance can be made, particularly after adjusting for multiple hypothesis testing.
Instead, we propose to use the same sampling distribution for the test statistics of all genes in an experiment. If the distribution had been a good approximation for the sampling distribution of the NFM test statistics for which the null hypothesis was known to be true, then that same theoretical distribution would have been used for the p-value calculation of all genes in the experiment. Since this theoretical distribution was shown to be a poor approximation (Figure 9), we generate an alternative, more appropriate distribution using permutations.
For a given experiment, we enumerate all treatment label permutations, and for each permutation we calculate the NFM test statistic for each gene. Our sampling distribution is the collection of test statistics for all genes across all permutations of treatment labels. Then for each gene, the NFM p-value is the proportion of all test statistics in this collection that are at least as extreme (large) as the gene's test statistic using the original treatment labels. The false discovery rate  is controlled by converting these raw p-values to q-values using the R package qvalue .
The iterative REML process used by the NFM method to obtain test statistics can converge slowly, and for some genes it does not by default converge in R. However, model convergence for these same genes in SAS  is not problematic. We have found that the non-convergence in R can be eliminated by trivial rounding of the log-scale intensity data (to the fifth decimal place, for example).
NFM Bovine NT Application
In applying the NFM method to the motivating bovine NT data, we used a non-specific filter [35, 36], and only calculated q-values for the 16,706 genes with expression above 20 on at least two of the seven arrays. With RMA preprocessing, this resulted in 584 significant genes (FDR .05). As a check for biological relevance, we then tested for over-representation of biological process Gene Ontology terms  using a conditional hypergeometric test [38, 39].
The clone samples in these motivating bovine NT data were the result of somatic cell nuclear transfer (SCNT). Despite improvements in recent years, bovine somatic cell nuclear transfer efficiency remains quite low. While pregnancy rates of transferred SCNT embryos approach those of transferred in vitro fertilized (IVF) embryos, pregnancy loss throughout gestation, and particularly in the first trimester, is much higher for SCNT pregnancies. The placental structure in SCNT pregnancies is often remarkably different in bovine SCNT pregnancies, with significantly fewer and significantly larger cotyledons and caruncles . In addition, SCNT fetuses that are lost mid-gestation as well as a portion of those that reach term demonstrate developmental abnormalities and metabolic problems [41–43].
An evaluation of the most significantly over-represented biological processes (from the conditional hypergeometric test) provides interesting insight regarding aberrations in SCNT cotyledons. Three main categories of over-represented biological processes were identified - cell cycle regulation, metabolism, and early developmental processes. Cell cycle arrest was the most significantly over-represented biological process. The most significantly over-represented metabolism ontologies were RNA processing and sphingolipid metabolic process. Among the most significantly over-represented developmental processes were brain development, neuron maturation, hindlimb morphogenesis, in utero embryonic development, and muscle organ development. These all fit very well with the abnormal phenotypes we see in SCNT pregnancies.
Of the statistical methods considered here for small-sample gene expression data, none was shown to be consistently superior, but a few interesting conclusions can be made. The fold-change-based methods (NFM, PLM, LE, PLW, FC, and FIRSTP) tended to be more consistent in their performance and provided the expected increase in test statistic ranks for higher fold-changes. By contrast, the non-fold-change-based methods (PLLM, RMANOVA, and PUMA) tended to be more variable in performance, and two of them (PLLM and PUMA) showed an unexpected decrease in test statistic ranks for higher fold-changes.
For small-sample gene expression experiments, the nested factorial model (NFM) was shown to be a competitive statistical approach for identifying differentially expressed genes, particularly at low false positive rates. The NFM method uses a permutation approach to calculate a p-value for each gene. These permutations add computational expense, but this expense must be taken in perspective. Small-sample studies (for which NFM is of immediate interest) are common in applications where physical samples are either scarce or prohibitively expensive. When a research team is unable to acquire additional biological samples or chips to perform a larger-sample gene expression study, they will have to compensate with additional waiting time. For the HGU95A spike-in dataset, with 12,626 genes on 4 control and 4 treatment arrays, the total runtime to acquire the permutation p-values was about 38 hours on a typical desktop PC. The HGU133A spike-in dataset, with 22,300 genes on 3 control and 3 treatment arrays, required a runtime of about 16 hours. The Golden Spike spike-in dataset, with 14,010 genes on 3 control and 3 treatment arrays, required a runtime of about 10 hours. The runtime for the bovine NT dataset, with 24,128 genes on 3 control and 4 treatment arrays, was about 50 hours. When a research team faces insufficient resources for a larger-sample gene expression study, these runtimes should not seem particularly onerous.
The NFM methodology described here, including the p-value permutation approach and trivial rounding in cases of initial non-convergence, has been implemented in R code (affyNFM) freely available from the first author's website at http://www.stat.usu.edu/~jrstevens. A tutorial document (including a reproducible example) is provided on the same website.
We conclude with a caveat for those who may attempt too much analysis with too little data. The development and availability of statistical methodology specifically for small-sample gene expression studies (such as the NFM here) should not be taken as encouragement of poor design. Our motivating example involved data where it was extremely expensive to obtain a single biological sample (of the clone). In practice, we would have preferred a larger sample size to provide more information on population-level biological variability. It was not merely expensive to acquire the chips, and not merely inconvenient to acquire additional biological replicates. We emphasize that sufficient sample size for useful inference should remain a guiding principle of experimental design in gene expression studies.
This research was supported by the Utah Agricultural Experiment Station, Utah State University, and approved as journal paper number 8201.
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