Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets
 Daniel M Gatti^{1},
 William T Barry^{2},
 Andrew B Nobel^{3, 4, 5},
 Ivan Rusyn^{1, 5} and
 Fred A Wright^{4, 5}Email author
DOI: 10.1186/1471216411574
© Gatti et al; licensee BioMed Central Ltd. 2010
Received: 19 April 2010
Accepted: 18 October 2010
Published: 18 October 2010
Abstract
Background
Analysis of microarray experiments often involves testing for the overrepresentation of predefined sets of genes among lists of genes deemed individually significant. Most popular gene set testing methods assume the independence of genes within each set, an assumption that is seriously violated, as extensive correlation between genes is a welldocumented phenomenon.
Results
We conducted a metaanalysis of over 200 datasets from the Gene Expression Omnibus in order to demonstrate the practical impact of strong gene correlation patterns that are highly consistent across experiments. We show that a common independence assumptionbased gene set testing procedure produces very high false positive rates when applied to data sets for which treatment groups have been randomized, and that gene sets with high internal correlation are more likely to be declared significant. A reanalysis of the same datasets using an array resampling approach properly controls false positive rates, leading to more parsimonious and highconfidence gene set findings, which should facilitate pathwaybased interpretation of the microarray data.
Conclusions
These findings call into question many of the gene set testing results in the literature and argue strongly for the adoption of resampling based gene set testing criteria in the peer reviewed biomedical literature.
Background
Methods for statistical analysis of gene expression microarrays are maturing rapidly, and there are a variety of approaches to normalization, detection of differential expression, clustering, and class prediction [1]. In many experiments, a statistical test is performed to identify genes significantly associated with experimental condition, clinical response, or other sample attributes. The resulting list of significant genes may be so large that it defies easy interpretation, and it is natural to seek a concise, biological summary of results. One such approach is gene set testing (sometimes called "pathway analysis"), which detects overrepresentation of gene sets among the list of significant genes. Gene sets may be curated [2], or derived from databases such as Gene Ontology (GO) [3] or Kyoto Encyclopedia of Gene and Genomes (KEGG) [4].
As the independence assumption is clearly violated for gene expression microarrays [11, 20, 21], how can we explain the persistent use of independence methods? Independence assumption methods may appeal to the belief that the occurrence of multiple members of a gene set on a list of significant ones "cannot be due to chance." Indeed, in a posthoc interpretation, the fact that the genes within a set are correlated is often, incorrectly, viewed as reinforcing the biological plausibility [5, 22], despite evidence presented here and elsewhere that the presence of correlation increases falsepositive rates. It is conceivable to argue that correlation within a gene set itself provides additional information, if such correlation varied substantially across different experiments. However, as we show here, correlation within a gene set is largely a persistent property that is preserved across a wide variety of sample sources and experimental conditions.
Here, we clearly demonstrate that the correlation between genes inflates the false positive rate, that the magnitude of this inflation is quite high, and that a simple resampling method for gene set testing produces the correct false positive rate. We use a large number (over 200) of real experimental datasets, not simulated data, to show that 1) intergene correlation is a pervasive feature of gene sets, regardless of experimental condition, 2) intergene correlation inflates the apparent significance of gene set statistics, leading to an increase in false positives, and 3) array resampling approaches can correctly address the problem of intergene correlation. As there are several existing tools that use resampling approaches to determine functional enrichment among significant gene lists, we argue that the naïve approaches should no longer be used and should be replaced by tools employing a resampling approach.
Results
One simple way to describe the effect of correlation is through a standardized enrichment test statistic. Let z be the signed square root of the independence assumption χ^{2} statistic (see Methods). Under the null hypothesis (no enrichment), z should have approximately mean 0 and variance 1. However, the true variance can be much greater, with an increase proportional to (m  1)ρ (see Methods), where m is the number of genes in the set, and ρ is the average correlation among genes in the set (excluding selfcorrelations). Independence assumption methods assume ρ = 0, implying that the variance equals 1. However, if the average internal correlation of a gene set is positive, then the variance of z can be greatly inflated, even if this correlation is modest, depending on the gene set size m.
To empirically demonstrate that variance inflation results in the false significance of gene sets, we randomly permuted the sample labels associated with each of the 202 Gene Expression Omnibus data sets 10,000 times, and then performed a gene set analysis on each permutation using a common independence assumption method. For each permutation a list of "significant" genes with p ≤ 0.05 (using a ttest or Ftest, as appropriate) was created in order to have a meaningfullysized gene list, and the standard χ^{2} statistic was used to assess the significance of each gene set. A gene set was called significant if it had a Bonferronicorrected (for the number of sets) χ^{2}pvalue ≤ 0.05. Note that this is a highly conservative threshold, intended to guarantee that no more than 5% of the permutations result in one or more gene sets (falsely) called significant. The gene set null hypothesis is that each gene set has the same pattern and proportion of differentially expressed genes as any other set [24]. Permutation of the sample labels induces the null hypothesis for each gene, and thus no gene sets should exhibit enrichment for differential expression. Hence, any gene set declared significant is a false positive.
Fortunately, the shortcomings of independence assumption methods can be addressed through the use of resamplingbased methods. These methods use the resampling data to construct an empirical null distribution for the gene set test statistics, taking into account the correlation structure between genes and providing a more accurate assessment of statistical significance. Several existing tools use resampling based approaches [2, 16–19], either permuting sample labels relative to gene expression, or performing bootstrap resampling [24]. We empirically investigated the performance of the permutation approach, which we will refer to as "resampling" to avoid confusion with the permutations described above. The sample labels for each data set were resampled without replacement 10,000 times, and the resulting enrichment statistic distribution was used to obtain an empirical pvalue for each gene set. For example, if the observed enrichment statistic for a gene set was greater than or equal to 99% of the resampled enrichment statistics, a pvalue of 0.01 was assigned to that gene set. As the resampling units are entire arrays, correlation between genes is maintained under resampling. Therefore the effects of correlation within gene sets apply to both the resampled and observed data, properly controlling the false positive rate. To demonstrate that the resampling approach correctly controls the false positive rate, we applied the resampling procedure to all permutations of all 202 datasets, using the same χ^{2} statistic as above. Using the empirical distribution of test statistics under resampling to generate the gene set pvalue, proper control of the false positive rate was obtained (Figure 3; Additional file 1, Figure S1, rightmost boxplots).
Discussion
The most straightforward strategy for gene expression analysis is to focus on individual genes for which expression differs among samples of interest. Such approaches often consider the genes selected based on significance thresholds, with the goal of predicting or finding associations with disease prognosis [25], adverse response to a chemical [26], disease pathogenesis [27], or identification of key genes in a pathway that may serve as biomarkers [28]. Although useful, genebygene analyses may not account for the complex underlying biology where the transcriptional programs are distributed across an entire network of genes, and may involve only subtle changes among individual genes. Even for genebygene analysis, the presence of strong correlations is increasingly recognized as a potentially complicating feature. For example, surrogate variable analysis [29] attempts to reconstruct latent variables which explain variation in gene expression, which rely on genegene correlation in an essential manner.
Gene set testing, in which enrichment of significant differentially expressed genes is sought among gene sets, is by now a standard method to provide biological interpretation for gene expression data. Groups of genes with a common biological function, cellular localization, regulation, or chromosomal location may hold additional clues regarding underlying biology, or potentially improve prediction or classification. While many easytouse tools have been developed to facilitate pathway or gene setbased analysis, intergene correlation within gene sets violates the independence assumption that underlies many gene set analysis methods. Indeed, our metaanalysis demonstrates that the correlation patterns persist across a wide variety of mouse and human experiments. Thus, we argue that correlation patterns should largely be viewed in terms of their effects on false positives, as we find no evidence that correlation within the gene set confers additional plausibility to a gene set finding.
Though it may be viewed as a technical matter, violation of the independence assumption is not a mere statistical detail. It is a tangible phenomenon that increases the chance of falsely declaring a gene set significant. The false positive rates established by our study are very high  sometimes an order of magnitude beyond the intended false positive rate. Array resampling methods correctly handle interset correlation and are not subject to these high false positive rates. Barry et al. [24] describe investigations of power and provides context on the meaning of type I error control for resamplingbased methods. The null distribution induced by permutation here is a special case of the expanded definition in [24], because the null hypothesis holds for each gene. Bootstrap resampling methods were shown in [24] to have more power than array permutation methods in simulated and real data sets, and expansions beyond the existing resampling approaches are worthy of investigation. There are a wide number of available packages that implement these methods, such as GSEA [2], Catmap [16], SAFE [17], ErmineJ [10], and SAMGS [18].
Several methods have been proposed which use multivariate modeling of array data to perform gene set testing [19, 30, 31]. Although comparisons using such approaches were not a main focus, we briefly tested whether these methods correctly control the false positive rate, by permuting the sample labels for one data set (GDS266) 1,000 times and counting the number of permutations in which at least one GO BP category was called significant using a 5% FDR. The globaltest procedure [19] uses a scorebased test statistic to test for correlation of an entire set of genes with clinical outcome, and can be used for gene set testing. The parametric version of globaltest incorporates the observed correlation structure, and is compared to a χ^{2} distribution. However, we found that 94% of permutations called at least one GO BP category significant, with a median of 10 categories called significant in each permutation, suggesting difficulties in proper control of the false positive rate. It is worth noting that use of empirical covariance estimates is extremely challenging, especially when gene sets are larger than the sample size. GlobalANCOVA [30] fits a linear model between each gene and the experimental covariates, and uses permutation testing to obtain empirical pvalues. We found that GlobalANCOVA found no significant GO BP categories under permutation, suggesting that GlobalANCOVA properly controls the false positive rate, and is perhaps conservative. Several related methods have been proposed [31, 32] that test for differential expression or predictive ability of sets of genes. Many of these approaches attempt to incorporate genegene correlation structure parametrically, and are thus computationally appealing. However, until more comprehensive investigations have been performed for the multivariate procedures, it seems prudent to examine the false positive rate in 1,000 permutations of their data before accepting the results at face value. Other investigators have explored more complex procedures involving stochastic dependence [33], while deeper explorations of randomset methods [34] may be valuable, but still essentially rely on independence assumptions. Several other methods are reviewed and compared in [35–37]. In summary, despite the availability of software and a wide array of methods that address intergene correlation, the literature on microarray analysis is rife with studies that use independence assumption methods.
As the use of independence assumption methods in gene array studies is widespread, we conclude that published false gene set findings may be common. The precise impact of the overly optimistic statistical support for many gene set findings is difficult to assess, because we do not know the underlying truth; however, a basic requirement of a valid statistical test is that it controls false positives under the null hypothesis, and we have clearly demonstrated here that methods that rely on independence assumptions are therefore invalid in this sense.
It should be acknowledged that very small microarray studies may have too few samples to support permutation. For such studies, a strong statistical result from an independence assumptionbased method should be supported by corroborating biological evidence, and even then be interpreted with caution.
Conclusions
Here, we have demonstrated, using over 200 real experimental data sets that independence assumption methods for gene set enrichment suffer from such a high false positive rate that they should not generally be used. Array resampling methods, which correctly control the false positive rate, should be used by investigators, incorporated into existing routines and workflows, and insisted upon by reviewers of scientific manuscripts. We strongly encourage the use of tools such as GSEA [2], SAFE [17], Catmap [16], ErmineJ [10], SAMGS [18] or others which employ resamplingbased methods to determine gene set significance.
Methods
Compilation and Categorization of Pathway Tools
Using the list of pathway tools compiled by [7], we downloaded all citations for each tool from the ISI Web of Knowledge (http://apps.isiknowledge.com/, accessed March 31, 2010). The manuscripts (for the period from 2002 to 2009) were divided into those using resampling or independence assumptionbased methods as detailed in [7]. Reviews and manuscripts that did not explicitly state the pathway selection method were not counted.
Gene Expression Omnibus Datasets
Using the GEOmetadb [38] and GEOquery [39] packages from Bioconductor, Gene Expression Omnibus was queried in April 2009 for all datasets run on the Affymetrix (Santa Clara, CA) HGU133A, HGU95A, MGU74A and MOE430A that contained 20 or more samples. There were 74, 43, 59 and 26 datasets for each array, respectively. The data was used as normalized by the submitters. The datasets are listed in Additional file 2, Tables S1 through S4.
Correlation Analysis and Variance Inflation
where $\overline{\text{corr}}(I[{p}_{i}\le \alpha ],I[{p}_{j}\le \alpha ])$ is the average correlation of the significance indicators for different genes i and j in the gene set. Dividing through by the independence assumption variance (${\mathrm{var}}_{\text{IA}}={\displaystyle {\sum}_{i=1}^{m}\alpha (1\alpha )}$), we obtain $\mathrm{var}(O)/{\mathrm{var}}_{\text{IA}}(O)=1+(m1)\overline{\text{corr}}(I[{p}_{i}\le \alpha ],I[{p}_{j}\le \alpha ])$. The average correlation of significance indicators has a nonlinear relationship with the original withinset genegene mean correlation ρ. However, observed variance inflation values are roughly proportional to mρ, as illustrated in Additional file 1, Figure S4. The concept of variance inflation is described more extensively in [24].
Independence Assumption Analysis
GO Biological Process categories and KEGG pathways with between 25 and 5000 genes were used to satisfy the assumptions of the χ^{2} test. Based on the experimental annotation provided in Gene Expression Omnibus for each dataset, either a Student's Ttest or analysis of variance (ANOVA) was carried out on each gene to select differentially expressed genes between the experimental classes provided in the annotation. Significant genes were selected using a nominal pvalue of 0.05. A onesided standard χ^{2} test (i.e. to test for enrichment only) was performed on each gene set, and significant gene sets were selected at a Bonferroni corrected pvalue of 0.05. Both GO Biological Process categories and KEGG pathways were analyzed using this approach.
Permutation Analysis
For each dataset, either a Student's Ttest or ANOVA was carried out on each gene to select differentially expressed genes between experimental classes at a nominal pvalue of 0.05. The onesided χ^{2} test statistic was used for gene set testing. The sample labels on the arrays were then permuted, and the entire analysis was repeated 10,000 times, resulting in a matrix of gene set statistics with one row for each gene set and one column for each permutation.
Resampling Analysis
The data was permuted as above and the permutation matrices containing gene set statistics were retained. Empirical pvalues for each gene set were calculated as the proportion of permutations for which the χ^{2} statistic was greater than or equal to the observed χ^{2} statistic. A gene set was called significant if its Benjamini & Hochberg FDR adjusted pvalue was 0.05 or 0.10. GO Biological Process categories and KEGG pathways were both analyzed using this approach.
Statistical Analyses were conducted using R, version 2.8.1 [40] and array annotation from Bioconductor, version 2.3 [41].
Abbreviations
 GO:

Gene Ontology
 KEGG:

Kyoto Encyclopedia of Genes and Genomes
 FDR:

False Discovery Rate
 ANOVA:

Analysis of Variance
Declarations
Acknowledgements
Financial support for these studies was provided, in part, by grants from the National Institutes of Health (R01 ES015241) and the United States Environmental Protection Agency (RD833825; RD832720; and F08D20579). However, the research described in this article has not been subjected to each Agency's peer review and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. DG was also supported by the UNC Environmental Sciences & Engineering Interdisciplinary Fellowship.
Authors’ Affiliations
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