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**|**Stat Appl Genet Mol Biol**|**PMC3192003

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- Abstract
- Introduction
- Hierarchical Model
- Estimation of Hyperparameters
- Simulations and Examples
- Discussion and Conclusions
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Stat Appl Genet Mol Biol. 2011 January 1; 10(1): Article 42.

Published online 2011 September 15. doi: 10.2202/1544-6115.1701

PMCID: PMC3192003

Lianbo Yu, The Ohio State University;

Copyright © 2011 The Berkeley Electronic Press. All rights reserved

This article has been cited by other articles in PMC.

Gene expression microarray experiments with few replications lead to great variability in estimates of gene variances. Several Bayesian methods have been developed to reduce this variability and to increase power. Thus far, moderated t methods assumed a constant coefficient of variation (CV) for the gene variances. We provide evidence against this assumption, and extend the method by allowing the CV to vary with gene expression. Our CV varying method, which we refer to as the fully moderated t-statistic, was compared to three other methods (ordinary t, and two moderated t predecessors). A simulation study and a familiar spike-in data set were used to assess the performance of the testing methods. The results showed that our CV varying method had higher power than the other three methods, identified a greater number of true positives in spike-in data, fit simulated data under varying assumptions very well, and in a real data set better identified higher expressing genes that were consistent with functional pathways associated with the experiments.

In testing for differential expression with microarrays, several methods have been proposed which borrow information across genes for estimating variances (Long et al., 2001, Tusher et al., 2001, Efron et al., 2001, Lonnstedt and Speed, 2002, Smyth, 2004, Cui et al., 2005, Fox and Dimmic, 2006, Sartor et al., 2006). Most of these references show that smoothing methods increase power relative to the ordinary t-test. In addition, use of the ordinary t-test means that variance estimates are gene specific, and the distribution of gene specific variance estimates across many thousands of genes will have some very low values due to sampling error. With small sample sizes, many gene specific variance estimates can be close to zero, leading to very small p-values associated with small and uninteresting differential expression values.

Smyth (2004) generalized the empirical Bayes model of Lonnstedt and Speed (2002) into an approach that can be used for small sample sizes. The method borrows information across genes to smooth variances and uses a single posterior variance estimate in a t-test. It performs well by increasing power, relative to the ordinary t-test, but assumes no dependence between gene variances and their corresponding expression values. Microarray data often show a relationship between gene expression and gene variance and this relationship has been considered in other Bayesian methods (Long et al., 2001, Fox and Dimmic, 2006, Sartor et al., 2006). Long et al.’s (2001) CyberT method uses a t-statistic with moving average variance estimates. However, the method requires an a priori value for the prior degrees of freedom rather than empirical estimates, and a chosen window size for smoothing the prior variances. Fox and Dimmic (2006) proposed an improvemnent to the CyberT. Their method uses a similar moving average approach to estimate the variances, but the value of prior degrees of freedom is not assigned a priori. Like the CyberT method, Fox and Dimmic’s method requires users to fix the window size for variance smoothing.

Sartor et al. (2006) proposed the intensity-based moderated t-statistic (IBMT) as an extension of Smyth’s moderated t-statistic (SMT). It uses local regression to model a functional relationship between gene variances and expression levels. IBMT provides better posterior variance estimates than the SMT method, because it shrinks sample variances towards expression specific values. As a result, IBMT reduces the variance of gene variances compared to SMT, which increases the prior degrees of freedom for the t-statistic, thereby increasing power. This is especially noticeable with small sample sizes. Although IBMT allows variances to change across gene expression levels, it fixes the prior degrees of freedom to one value. This means that the coefficient of variation (CV) of gene variances is assumed to be constant across genes (See Appendix). This assumption of constant CV for the gene variances is too restrictive in microarray experiments as we show with examples. In particular, filtering methods at low expression values and ceiling effects at high ones can lead to more or less homogeneous variances.

We propose a t-test that more fully moderates the denominator of the test statistic (fully moderated t-statistic or FMT) than IBMT or SMT. Using local linear regression, we allow both gene variances and the CV of gene variances to change as a function of gene expression resulting in improved estimates of the prior degrees of freedom as well as gene variances. Consequently, the FMT method should have more accurate degrees of freedom and false positive and negative control across different expression levels. The proposed method is applied to simulated and spike-in data and its performance is compared with three other t-test methods. We also analyzed a small sample size real data set to show the differences in the gene lists produced by these methods.

Our FMT method is adapted from the hierarchical Bayesian model proposed by Smyth (2004). In this model, assuming a linear model for log transformed expression data, the residual variance for gene *g*,
, is assumed to follow a scaled chi-square distribution with *d _{g}* degrees of freedom:

where is the gene variance, which is assigned a scaled inverse chi-square prior:

(1)

and where *d*_{0g} and
are the prior degrees of freedom and location, respectively. Under this model, the posterior mean of
given
, is

which is incorporated into the denominator of our fully moderated t-statistic:

where * _{g}* is the difference in average log-expression values for gene

For our FMT method, we allow both *d*_{0g} and
to vary across genes. Note that the SMT method assumes they are constant. The consequence of assuming
constant under SMT is that all variances are shrunk toward one value. When
varies with expression level, SMT not only biases estimates of
, but also inflates the variance of
. Inflated estimates of
produce underestimates of the prior degrees of freedom, which reduces the power of the test statistic. Sartor et al.’s IBMT method (2006) extends SMT by allowing
to vary with expression levels. However, like SMT, IBMT assumes that the prior degrees of freedom are constant. The consequence of this assumption is that prior degrees of freedom may be under or overestimated at different expression levels resulting in reduced power or increased false positive error at different levels. In addition, estimates of
require prior estimates of *d*_{0g}, which means that
estimates could be biased by assuming constant *d*_{0g}. As seen below, our FMT method allows both the prior variance and prior degrees of freedom to vary flexibly with expression levels, thus removing the restrictions imposed by these previous methods. We have frequently found that prior degrees of freedom are higher at higher expression levels and lower at low expression levels.

The hyperparameters *d*_{0g} and
are estimated from the data using an empirical Bayes approach similar to that implemented in SMT and IBMT (Smyth, 2004, Sartor et al., 2006). Under our model with the addition of a varying *d*_{0g}, the marginal distribution of the sample variance
is a scaled F-distribution (Smyth, 2004):

The log of the sample variance, , is distributed as a constant plus Fisher’s Z distribution (Johnson and Kotz, 1970). The first two moments of are

(2)

and

(3)

where Ψ( ), the digamma function, is the first derivative of the log gamma function and Ψ′ ( ), the trigamma function, is the second derivative of the log gamma function. Define *e _{g}* as an adjusted log-variance of the

which (based on Equation 2) has expected value

(4)

and variance

The adjusted log variances, *e _{g}*, are modeled as a function of the mean log-expression values using LOESS local regression (Cleveland, 1979, Cleveland and Devlin, 1988) to obtain the smoothed adjusted log variance values,

(5)

We replace
of Equation 3 by
and solve for Ψ′(*d*_{0g}/2):

(6)

For the moving average, we chose a window size of 40 genes, but this does not seem to be a critical choice as the same results are obtained for a wide range of window sizes. We present this result in our simulations section. The estimated values
, obtained from Equation 6, are then modeled as a function of the mean log-expression values using LOESS local regression, and the smoothed Ψ′(*d*_{0g}/2) values provide *pred*(Ψ′(*d*_{0g}/2)). We found that LOESS span parameters in the interval [0.8, 1.0] gave similar results and easily captured the true functions of simulation models. This was also obvious visually. When lower spans were used, smoothing seemed insufficient. Finally, we solve for *d*_{0g} using the smoothed predicted values

where Ψ′^{−1} is the inverse of the trigamma function. The gene-specific
are obtained by substituting the smoothed log variance values, *pred*(*e _{g}*), and the estimated prior degrees of freedom,
, in Equation 4 and solving for
:

Note that the moving average in Equation 5 uses multiple deviances between *e _{g}* and

Expression levels of 20,000 genes were simulated under three different model assumptions (see Simulation Steps in Appendix). Forty percent were simulated as non-expressed genes. Of the remaining 60% expressed genes, 300 or 2.5% were simulated as differentially expressed. Simulation Model 1 was based on our FMT method’s model assumptions and allowed the hyperparameters, prior location and prior degrees of freedom of , to vary across the genes as a function of their expression levels. We also studied the performance of the FMT, when the assumptions of the other methods (IBMT, SMT) were met. Simulation Model 2 was based on the SMT model assumptions where the prior degrees of freedom and prior location of were constant for all the genes. Simulation Model 3 was based on the IBMT model assumptions where the prior degrees of freedom of were constant for all the genes, but the prior location changed with gene expression level. All the simulations were set to resemble real data from prior gene expression array Affymetrix experiments. Under each of the three models, we generated data sets for three different sample sizes (3, 5, and 10 samples in each of 2 groups). A total of 100 simulations were run for each model and sample size combination. We found 100 simulations were sufficient because the empirical coefficient of variation of the estimators we report here were less than 5%. A threshold filtering method was applied to each simulated data set to filter out genes which were simulated to be non-expressed. This method compares the signal from each gene in each sample to a cut-off value (close to background level), and filters out genes if that gene has signal levels smaller than the cut-off in all or a very high percentage of the samples. Consideration of filtering is critical when smoothing variances. Without filtering, variances and their CVs would be smoothed toward the values of genes whose expression levels are close to background noise which will downwardly bias variance estimates of the genes that are actually expressed.

The FMT simulation model (Model 1) and the IBMT simulation model (Model 3) simulation steps differ from those of the SMT model (Model 2) only in the assumption about the two hyperparameters, prior degrees of freedom and prior location. Under the IBMT model, we induced a dependency structure between
and the average log expression values (Figure 1*A*), but kept the prior degrees of freedom constant (*d*_{0} = 4) for all the genes. Under the FMT model, both
and *d*_{0g} are a function of average log expression (Figure 1). The range of prior location and prior degrees of freedom chosen for Model 1 are based on our experience with real data examples. In each of the figures presented here, we used a sample of *n* = 3 per group.

Relationship between hyperparameters and gene expression for Simulation Model 1. A) Prior variance (
). B) Prior degrees of freedom (*d*_{0g}).

We tested for differential expression in each simulated data set controlling the per family error rate (PFER). PFER has been shown to have superior stability in terms of the variance of both the number of true positives and the number of total positives when compared to Benjamini and Hochberg’s (BH) procedure (Gordon et al., 2007). This method focuses on the expected number of false positives rather than on the probability of false positives (Tukey, 1953). PFER is defined as:

where *E*(*V*) is the expected number of false positives and *G*_{0} is the total number of true null hypotheses, *G* is the total number of hypotheses tested, *P _{g}* (

The mean number of false positives for the SMT, IBMT and FMT testing methods were slightly less than or equal to the expected values of *V*. The same false positive control was seen for simulation Models 2 and 3. These results are exactly paralleled by the more commonly used false discovery rate (FDR) method (Benjamini and Hochberg, 1995, Gordon et al., 2007). SMT, IBMT, and FMT methods under all 3 models exhibited the same levels of FDR control.

Power under Simulation Models 1 and 2 are shown in Figures 2. Under the FMT simulation model (Model 1), the FMT testing method performs the best with respect to power, followed by IBMT, SMT and the ordinary t-test, respectively (Figure 2*A*). It is not surprising that the FMT performed the best as it accurately estimated the prior degrees of freedom (Figure 3) across the gene expression range while the IBMT and SMT methods underestimated the prior degrees of freedom for genes whose average log2 expression levels were greater than 10. The estimated prior degrees of freedom were averaged over 100 simulations, and per gene averages were displayed for the FMT method and compared to the true curve. As expected, both IBMT and FMT accurately estimated the prior location of the variance while SMT underestimated its value at low expression levels (data not shown). Under the SMT simulation model (Model 2), all three moderated t-statistic methods displayed similar power, and higher than that obtained from the ordinary t-test (Figure 2*B*). This demonstrates the flexibility of IBMT and FMT methods; because they produce the same results as the SMT method when its stronger assumptions are met. So that the only cost to use FMT or IBMT over SMT is the additional calculations.

Power plots of four testing methods under 2 different simulation models. Power averaged over 100 simulated datasets was calculated separately for the simulated data by using 4 different testing methods: t-test (purple), SMT (blue), IBMT (red), and FMT **...**

Prior degrees of freedom estimated by different moderated testing methods under the FMT simulation model (Model 1). Prior degrees of freedom were estimated using: SMT (blue), IBMT (red), and FMT (green) under the FMT simulation model (Model 1) and averaged **...**

Because IBMT and SMT underestimated the degrees of freedom at the larger expression levels under Model 1, these methods should have lower power than FMT to detect true positives among highly expressed genes. To confirm this, we grouped genes into three expression categories: low (1: average signal < 7), moderate (2: 7 < average signal < 11) and high (3: average signal > 11). With the expression level categories of the 300 true positives were fixed over the 100 simulations, the average number of true positives detected at each expression level category were calculated. Figure 4 gives these results when the expected number of false positives was controlled at 5. All three moderated t-tests outperformed the ordinary t-test at each intensity level. At low intensity levels, the three moderated t-test methods were similar. At moderate intensity levels, the FMT and IBMT methods outperformed the SMT, because they correctly estimated the hyperparameters there while SMT underestimated them (Figure 3). At high intensity levels, FMT outperformed the other two moderated t-tests, because it correctly estimated prior degrees of freedom there, while IBMT and SMT underestimated them.

Observed true positives detected at different expression levels. Observed true positives averaged over 100 simulated datasets were plotted under 4 different testing methods: t-test (purple), SMT (blue), IBMT (red), and FMT (black) at three expression **...**

Figure 5 displays the number of false positives detected within each expression level category when the expected number of false positives was controlled at 5. FMT and IBMT provided very similar false positive control. SMT had most of its false positives observed at low intensity levels; this is because the method underestimates the variance at these expression levels (data not shown).

For each testing method, the genes were ranked by their p-values (lowest to highest) and the numbers of false positives among the most significant 300 genes were calculated. The results for Model 1 are presented in Figure 6. FMT accumulated the fewest false positives followed by IBMT, SMT and t-test, respectively. Under the SMT simulation model (Model 2), all three moderated t-statistic methods displayed similar false positives.

When 5 samples per group were simulated under the FMT model, the FMT method still outperformed the other three testing methods, but the differences were not as large as seen with a sample size of 3 per group. As the sample size increases, the total degrees of freedom also increase and hence modest underestimation or overestimation of the degrees of freedom does not have as large effect on power (data not shown).

To assess the sampling variability of LOESS smoothing curves of the moving average estimate
(Equation 6) and select appropriate moving average window sizes, we calculated the mean and standard deviation of LOESS estimates by intensity levels over 100 simulations (Figure 7). The average CV across the 100 simulations of LOESS estimates is less than 5% for all intensity levels and all selected window sizes (*m* = 10, …,2000). The window size of 40 genes seems the best choice for these data, but any moderate window size gave the same results.

To understand the effect of gene association on the tests’ performances, we induced a block diagonal correlation structure among genes under Model 1. We randomly divided the 12,000 expressed genes into 120 blocks of 100 genes each. Within each block we fixed the correlation to 0.5. The power and false positive number obtained from these data are highly similar to those obtained under data simulated assuming independence (Figures 4 and and55).

Another set of simulations were performed to determine the sensitivity of the moderated t tests to the inverse chi-square assumption about the residual variance. The residual variance, , for each of the 12, 000 expressed genes was randomly generated from either a scaled inverse chi-square distribution or a normal distribution with distribution selection probability 0.7 and 0.3, respectively. The mean of the normal distribution was fixed at the 80th percentile of the scaled-inverse chi-square distribution and the standard deviation was fixed at 0.3. The resulting distribution is bi-modal and has a much heavier tail than the inverse scaled chi-square distribution assumed by the moderated t methods. Under this mixture model, false positives were controlled at their nominal level. Because a constant variance and prior degrees of freedom were used, no differences among the moderated t methods were observed (nor expected).

The performance of the four testing methods (SMT, IBMT, ordinary t-test and FMT) were next compared using a controlled “spike-in” Affymetrix data set. In this data set, the numbers of true negatives and true positives are known. The “spike-in” data were generated by Choe et al. (2005) and mimic a two group (treatment vs. control) microarray experiment. The data set consists of 3,860 individual cR-NAs of known sequence which were divided into two samples: ’constant’ (C) and ’Spike’ (S). Each sample was hybridized in triplicate on Affymetrix GeneChips (six chips total). The data set also includes expression levels of 10,131 empty probe sets (blanks) in each of the six chips. Out of 3,860 cRNAs, 1,309 true positives were spiked-in with different known concentrations in the S and C samples. The remaining 2,551 cRNAs had the same concentrations in each sample (true nulls). Choe et al. (2005) generated 152 expression data sets by using different combinations of preprocessing methods and ranked the preprocessed data sets based on their ability to detect differentially expressed genes. As in Choe et al. (2005) and Sartor et al. (2006), we focused on the average of the 10 data sets with the highest ranking (best) preprocessing steps.

Figure 8 displays prior degrees of freedom estimates of the three moderated t-statistics (SMT, IBMT, and FMT). Because SMT and IBMT assume constant prior degrees of freedom, *d*_{0}, they estimated a single *d*_{0} value (SMT=1.19, IBMT=2.30) for all the genes. Our method allowed the prior degrees of freedom to vary across the genes and they ranged from 1.19 to 19.74.

Spike-in data prior degrees of freedom estimates. SMT (blue), IBMT (red), and FMT (black) methods were used to estimate prior degrees of freedom over average log expressions.

We compared the four testing methods with respect to their power and number of false positives among the most significant 1,309 genes. When genes were ranked by p-values, our FMT method had the minimum number of false positives among the 1,309 genes with smallest p-values followed by IBMT, the t-test and SMT (Figure 9). This relationship was most apparent for genes 800 – 1,309. As noted by others (Storey and Dabney, 2006, Irizarry et al., 2006), type I error is not adequately controlled in these data sets because of experimental dependencies.

To demonstrate the advantage of our proposed method for identifying differentially expressed genes, we applied it to a real microarray dataset and compared it with the other three methods as in the simulation studies and analysis of the Spike-in data. Affymetrix mouse exon arrays were used for measuring gene expression from the thyroids of 4 control mice and 4 littermates carrying a thyroid-specific deletion of the Prkar1a gene (Pringle et al., 2011). PRKAR1A/Prkar1a encodes the type 1A regulatory subunit of the cAMP-dependent Protein Kinase (Protein Kinase A, PKA), and mutation of this gene is tumorigenic in both mice and men (Kirschner et al., 2009). Thus, this microarray compares normal to neoplastic thyroid tissue.

Figure 10 shows the fitted LOESS local regression curves of variance estimates of log gene variance against log expression levels for SMT, IBMT, and FMT. The estimated prior degrees of freedom for FMT increased from 4.34 to 8.30 across the log expression level range of 3.33 to 13.66. In addition, Figure 11 displays the fitted LOESS local regression curves of prior variance estimates against log expression levels for SMT, IBMT, and FMT. For testing differential expression, per family error rate was 10 (Gordon et al., 2007). We found 536 significant genes under FMT, 524 significant genes under IBMT, 533 significant genes under SMT, and 244 significant genes under the ordinary t-test. Figure 12 displays overlaps and differences of the 4 significant lists. FMT detected 18 unique genes that are not detected by IBMT. Note that 17 out of these 18 genes had moderate or high expression. IBMT detected 6 unique genes that were not detected by FMT and all of them were low expression genes close to background noise. The main distinction between these two methods is that IBMT estimated a fixed prior degrees of freedom of 5.40, which was higher than the prior degrees of freedom estimated by FMT at low expression, but was smaller than FMT’s estimates at the moderate and high expression levels thereby explaining the increase in positives observed for FMT at these expression levels. Similar differences were seen between FMT and SMT. Even though SMT has the second largest significant gene list, 25 unique SMT genes that are not detected by FMT are all low expression genes close to background noise.

Estimated prior degrees of freedom and variance of log variances against log intensity in the real data example. A) Estimated prior degrees of freedom by FMT (green), IBMT (red), and SMT (blue). B) Estimated variance of log variances by FMT. Moving average **...**

Estimated prior variance against log intensity in the real data example. Prior variance estimates by FMT (green) and IBMT (red) were obtained through fitting a LOESS local regression on the adjusted log-variance *e*_{g} with span=0.75. Differences between **...**

Venn Diagram of the 4 significant gene lists from 4 different testing methods:t-test, SMT, IBMT, and FMT

We studied the differences in gene functions of the FMT and IBMT significant gene lists through Ingenuity Pathway Analysis (IPA), which uses prior published data on gene function to identify associations. The main gene function networks for both IBMT and FMT gene lists are identical. In addition, we found that most of the function networks of the 18 genes uniquely identified by FMT are consistent with the function networks identified in the overall FMT and IBMT significance lists. In other words the IPA validated that these 18 genes should be considered positives. However most of the function networks of the 6 genes unique to IBMT are not consistent with those same function networks, which suggests that these 6 genes are likely false positives. We had similar findings for the 25 genes unique to SMT. Details of this analysis are to be published. An important difference in the FMT gene list is that it included Pde7b, a high-affinity cAMP-specific phosphodiesterase anticipated to be upregulated in response to elevated PKA activity (Hetman et al., 2000), and Sphingosine kinase 1, which is functionally upregulated in response to PKA activation (Rius et al., 1997).

We proposed a novel improvement of the moderated t-test for identifying differentially expressed genes in small sample size gene expression experiments. Previous moderated t-test methods, SMT and IBMT, are too restrictive in that they assume a constant coefficient of variation of gene variances. In contrast, the FMT method we propose allowed the CVs of gene variances to change as a function of gene expression levels.

When data were simulated under the SMT model, the three methods were comparable in terms of power, false positive control, and accuracy of hyperparameter estimates. However, when SMT assumptions were not met, differences among the methods were evident. This was expected, because SMT ignores the dependency of gene variances and the CV of gene variances on gene expression levels and results in incorrect estimation of these hyperparameters. Specifically, we found that SMT consistently underestimates the prior degrees of freedom in these situations, which is especially important in small sample size experiments. We also found that when the data were generated under the IBMT and FMT models where variances are larger at the lower expression levels, SMT generates most of its false positives at lower expression levels. While the performance of IBMT and FMT were comparable when data were simulated under the IBMT model, FMT was more powerful than IBMT when the CVs of gene variances were dependent on expression levels. Under an increasing trend in prior degrees of freedom, the differences in power were greatest at high intensity levels. The power of the moderated t methods were more comparable at larger sample sizes, because as the residual degrees of freedom increase, the posterior mean of the gene variance is pulled toward the empirical gene-specific estimate. Also, when the residual degrees of freedom are larger, the increase in degrees of freedom provided by the moderated t-tests is relatively smaller. We also compared the four testing methods using an Affymetrix controlled spike-in data set. Our method identified the largest number of true positives, followed by IBMT, t-test and SMT, respectively. This is due to the higher degrees of freedom associated with our method at moderate to high expression levels, where we gained most in power. These results were confirmed in an analysis of a small sample size experiment. In this analysis, FMT identified 18 unique genes at moderate to high expression levels. IPA analysis revealed that the function of these 18 genes were consistent with the other significant genes. An R-package to implement this method is in preparation, and will be called FMT. A temporary R function can be found on http://www.biostatistics.osu.edu under the first author’s page.

With *σ*^{2} following a scaled inverse chi-square distribution, the probability density function over the domain *x* > 0 is

where *d*_{0} is the degrees of freedom parameter and
is the scale parameter. The mean is
for *d*_{0} > 2 and the variance is
for *d*_{0} > 4. Therefore the coefficient of variation (CV) is simply

- For 20,000 genes (
*g*= 1,2,…,20,000) simulate the average log expression,*α*, from a three parameter log-normal distribution:_{g}*ln*(*α*– 1.75) ~_{g}*N*(1.35,0.5). **FMT Model (Model**1**)**: For the 12,000 expressed genes; calculate the gene-specific prior degrees of freedom*d*_{0g}and gene-specific prior location of , , as a function of the average log expression values (*α*) obtained from Step 1. We modeled the prior location and prior degrees of freedom as:_{g}**SMT MODEL (Model**2**)**: For the 12,000 expressed genes, we fixed the prior degrees of freedom at*d*_{0}= 4 and the prior location of the gene variance at .**IBMT Model (Model**3**)**: For the 12000 expressed genes, we fixed the prior degrees of freedom at*d*_{0}= 4 and modeled the gene-specific prior location of as a function of the average log expression values,*α*, obtained from Step 1 using the same expression as simulation Model 1._{g}

- For the 12,000 expressed genes, we simulated from an inverse chi square distribution (Equation 1) using the hyperparameters from Step 2.
- For the 8,000 non-expressed genes, we randomly selected from the genes sample variances from real data with noise level expression values.
- Among the 12,000 expressed genes, 300 genes were simulated to be differentially expressed using the following steps:
- We simulated the mean log-differences
*μ*from , where_{g}*k*= 1.5 for 150 differentially expressed genes and*k*= −1.5 for the remaining 150 differentially expressed genes and . - We simulated the raw data for each sample in group 1 from and for each sample in group 2 from .

- For the remaining 11,700 expressed genes and 8,000 non-expressed genes, we simulated the raw data for each sample from .

Lianbo Yu, The Ohio State University.

Parul Gulati, The Ohio State University.

Soledad Fernandez, The Ohio State University.

Michael Pennell, The Ohio State University.

Lawrence Kirschner, The Ohio State University.

David Jarjoura, The Ohio State University.

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