Functional Analysis: Evaluation of Response Intensities  Tailoring ANOVA for Lists of Expression Subsets
 Fabrice Berger†^{1}Email author,
 Bertrand De Meulder†^{1},
 Anthoula Gaigneaux^{1},
 Sophie Depiereux^{1},
 Eric Bareke^{1},
 Michael Pierre^{1},
 Benoît De Hertogh^{1},
 Mauro Delorenzi^{2} and
 Eric Depiereux^{1}
DOI: 10.1186/1471210511510
© Berger et al; licensee BioMed Central Ltd. 2010
Received: 30 March 2010
Accepted: 13 October 2010
Published: 13 October 2010
Abstract
Background
Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, coregulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms.
Results
As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a twoway ANOVA to examine these criteria. The significance of the results was evaluated according to the selfcontained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three realworld datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets.
Conclusions
The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that twostep data reduction allowed us to significantly improve the performance of geneset analysis using a modified twoway ANOVA procedure, and to detect genesets that current methods fail to detect.
Background
Introduction
The major issue when studying datasets with many tests and few replicates in general and microarray datasets in particular is the decreased power of the analysis. Indeed, to reduce the risk of detecting false positives due to the lack of a sufficient number of replicates, statistical tests allow for higher threshold levels as the number of replicates decreases. This implies an unavoidable increase of the number of false negatives and, thereby, a decrease in power.
To compensate for this lack of information about expression data generated by microarrays, some authors have tried to work at the probe level, breaking down probesets into smaller components. Nonetheless, most researchers prefer to work at the probeset level where data can be analyzed in different layers (individual analysis, geneset analysis, coexpression studies, clustering) [1].

group discovery: empirical data is used to identify new criteria to discover new genesets. This task uses several different approaches such as data clustering [6–9] and the study of regulatory sequences [10–14],

geneset analysis: previously defined genesets are used to guide empirical data analysis, to determine whether the expression values of known genesets are correlated with the conditions compared in the experiments [15–17].
The research presented here focuses on statistical geneset analyses. Our initial motivation was to answer the seemingly trivial question (Q_{0}): "Which known genesets are associated with different expression profiles under the two conditions compared?". We discuss here the ability of current methods to provide an answer to this question.
This main question Q_{0} implies several hypotheses related to the nature/composition of genesets tested: the individual members (genes) may be expressed at various levels, over or underexpressed or correlated (coordinated response of member genes).
To provide an answer to question Q_{0}, we present the FAERI methodology (Functional Analysis: Evaluation of Response Intensities), an ANOVA2like procedure tailored to detect genesets with differing expression profiles, regardless of the nature of the individual responses.
State of the art
Several geneset analysis methods have been published recently. Each available method provides an answer to question Q_{0}. The methods discussed below were selected to provide an overview of the main strategies followed by the authors, or due to their pioneering approach.
Overrepresentation analysis methods are not considered in this paper, as they focus on the top list obtained by individual analysis and aim to identify only those groups of genes that are represented by the most differentially expressed probesets. This approach thus facilitates the interpretation of individual analyses, by giving annotation clues about biological meaning. However, such methods require the ad hoc selection of an individual statistic, and the definition of an arbitrary threshold for the detection of individual genes, whatever the individual statistic used. Thus, overrepresentation analysis does not allow for the detection of genesets associated with subtle individual changes according to the individual statistic chosen [18–21]. For this reason, other authors describe improved ORA strategies. As an example, Yi and Stephens, in SLEPR, first select samplelevel differentiated genes from each individual sample, before computing an enrichment score characterizing the geneset [22].
Formulation of the different questions asked by the different genesets analysis methods
Question  Formulation 

Q_{0}  Which known genesets are associated with different expression profiles under the two conditions compared? 
Q_{comp}  Which geneset definitions are associated with the biggest difference in expression profiles observed under each condition? 
Q_{self}  Which genesets are associated with diverging expression profiles between conditions compared with random definitions of phenotype? 
Q_{cor}  Which genesets are defined by members associated with correlated expression changes? 
Q_{uni}  Which genesets are associated with an increase or decrease of all member expression values? 
Q_{bidir}  Which genesets are defined by differentially expressed genes, regardless of the direction of the regulation? 
Q_{int}  Which genesets are associated with variable individual expression changes? 
Q_{u}^{c}  Which unidirectional groups have members that are associated with a correlated answer? 
More recent functional class scoring (FCS) methods can be classified based on their competitive or selfcontained null hypothesis. Competitive methods sort the groups focusing on question Q_{comp} (Table 1) [23–28]. Such methods provide a list of genesets that are the most significantly associated with the phenotype compared with other geneset definitions. The definition of the null hypothesis of the competitive methods is theoretically associated with the genesampling strategy for significance evaluation (data permutations driven by random definition of equal sized genesets) [23, 29]. This statement, however, as shown by Effron and Tibshirani, does not take into account the pattern of correlations between genes, which is destroyed by genesampling [29]. Conversely, selfcontained methods study question Q_{self} (Table 1). The significance of the results, for those methods, is evaluated by label sampling (data permutations driven by random definition of phenotypes for a given group definition). Each geneset score is thus evaluated against its putative range of geneset score values, maintaining the geneset definition [23, 28, 30–34].
The GSEA methodology and its derivatives make use of a hybrid strategy providing results that are harder to interpret. The null model used to develop the methods relies on the competitive approach, but is later evaluated by label sampling, thus mixing both strategies [24, 25, 27].
The second methodological difference between the methods concerns definition of the geneset score evaluated. Methods using two steps first compute an individual statistic that is then used in a second step to compute a geneset statistic [23–28, 33]. Conversely, global methods perform the analysis in a single step, evaluating geneset statistics from raw expression values associated with the group members (GlobalTest, GlobalAncova) [30, 31, 34]. SAMGS is a particular case of a global method where the final formulation of the statistic is similar to a twostep strategy using the SAM tlike individual statistic [30].
Questions studied by the methods
Current methods do not provide equivalent answers to question Q_{0}. Therefore, we propose to reformulate the hypothesis used with the available methods with respect to the biological properties of the sets (Table 1).
Comparison of the properties of several geneset differential expression analysis methods
Author  Year  Hypothesis  Data used  Group statistic  Significance  Name  Properties  Individual statistic 

Mootha et al  2003  Competitive  Individual statistic  ES (Running Sum)  Sample permutations  GSEA  Hybrid  Signal/noise Ratio 
Subramanian et al  2005  Competitive  Individual statistic  ES (Running Sum)  Sample permutations  GSEA  Hybrid, asymmetrical  Individual correlation (r) 
Keller et al  2007  Competitive  Individual statistic  ES (Running Sum)  Competitive theoretical model  Variant GSEA  Hybrid, symmetrical  * 
Effron & Tibshirani  2007  Competitive  Individual statistic  ES (Running Sum)  Sample permutations  GSA  Restandardizaton  * 
Pavlidis et al  2004  Competitive  Individual statistic  Log (p(g)) = mean[log[p(i)]]  Genes permutations  Depends on the geneset size  Pearson correlation coef.  
Tian et al  2005  Competitive  Individual statistic  Weighted mean  Genes permutations  Standardization  Student t  
Tian et al  2005  Selfcontained  Individual statistic  Weighted mean  Sample permutations  Standardization  Student t  
Kim & Volsky  2005  Selfcontained  Individual statistic  Mean  Normal distribution  PAGE  Central Limit Theorem  Foldchange 
Effron & Tibshirani  2007  Selfcontained  Individual statistic  Mean  Sample permutations  GSA  Unidirectional + Restandardization  Student t 
Effron & Tibshirani  2007  Selfcontained  Individual statistic  Maxmean  Sample permutations  GSA  Unidirectional (directional subset) + Restandardization  Student t 
Effron & Tibshirani  2007  Selfcontained  Individual statistic  Absmean  Sample permutations  GSA  Unidirectional (absolute value) + Restandardization  Student t 
Dinu et al  2007  Selfcontained  Expression data  Sum (d^{2})  Sample permutations  SAMGS  Determination of S_{0}  SAM d statistic 
Goeman et al  2004  Selfcontained  Expression data  Q(g) = mean(Q(i))  Permutation/Gamma/Asymptotic  GlobalTest  P (YX)  Q(i) 
Mansmann & Meister  2005  Selfcontained  Expression data  F  Sample permutations  GlobalAncova  P (XY)  
Berger et al  Unpublished  Selfcontained  Expression data  F  Fisher F  ANOVA2  Unidirectional  
Berger et al  Unpublished  Selfcontained  Expression data  F*  Sample permutations/random data  FAERI  Bidirectional 
 (a)
It is important to study expression levels as highly expressed probesets may mask differences observed for probesets with lower expression levels. Twostep methods such as the SAMGS are not influenced by this criterion. For example, the Student t or SAMGS d statistics characterize expression differences, regardless of the individual expression levels [30]. Moreover, global methods that rely on the i.i.d. condition may be affected as expression levels vary between probesets, meaning that the probesets are not identically distributed.
 (b)
Individual expression values may reveal up or down regulation of the genes depending on the conditions tested. One way to take the direction of expression changes into account and deliver results with no dependence on this criterion is to avoid studying mixed groups with a global null effect (50% over and 50% under expressed). Twostep methods may be adapted to use the absolute value of a unidirectional individual statistic (Student t [35]), or its squared value (SAMGS [30]). Alternatively, the geneset statistic may be defined to be representative of the directional subgroup associated with the biggest expression change (absmean), optionally with regard to the size of the directional subgroup (maxmean) [23]. Among global methods, the SAMGS and GlobalTest are independent of the direction as the null hypothesis is tested respectively on squared statistic (d^{ 2 } ) and expression change variability (τ^{ 2 } ) [30, 31].
 (c)
Genes may be associated with correlated or variable expression changes. Question Q_{0} takes both possibilities into account. Twostep methods based on a sum or a mean, like the SAMGS, do not need further adaptation since they explicitly rely on the sum of individual pieces of information [5, 23, 28, 30]. The total difference is then evaluated independently of the variability of the individual differences. Among global methods, the GlobalTest is suited for the study of uncorrelated genesets as it assesses the variability of the individual answers. On the contrary, the GlobalTest is not intended to detect correlated groups since the variability of the individual answers in such sets is null (question Q_{u} ^{C}, see Table 1) [31].
It appears that only twostep methods, as well as the SAMGS, may simultaneously provide an answer to the three criteria considered and thus evaluate more completely question Q_{0}.
Software packages developed thus far are not always able to finetune the appropriate combination of steps with regards to the criteria. Moreover, it is not systematic to take the direction of the answer into account or to rely on partial genesets (the maxmean statistic, for example, mainly takes the directional subgroup with the strongest expression change into account) [23, 28, 30, 33].
Global methods, on the other hand, ignore the possible bias associated with expression level. The SAMGS procedure involves a preliminary step of variance stabilization based on the SAM individual analysis method, which requires that the expression values of all probesets be estimated [30, 36–38].
Competitive procedures aim to identify groups with bigger differences and ignore groups associated with the phenotype but with a moderate answer compared to the others. The selfcontained null hypothesis thus provides a more appropriate answer, where each set is evaluated according to its possible distribution range [23–28, 33].
Objectives
There are several possible strategies to evaluate question Q_{0} as completely as possible. Several methodologies may be selected to answer complementary questions. Gentleman et al. previously suggested to separately study subgroups defined by the direction of expression changes and to define core sets of genes common to several genesets (thus avoiding that the same genes lead to the detection of several genesets). As a perspective of the GlobalAncova methodology, Mansmann and Meister proposed to study the interaction between the effect of the condition and group composition, to take the variability of the individual responses into account. The hypothesis associated with this test complements the unidirectional null hypothesis, but also provides a partial answer to question Q_{0} since it ignores unidirectional correlated groups (no interaction).
The work reported below proposes an alternative strategy to answer question Q_{0} based on a single selfcontained test. To optimally analyze expression data, our model relies on a global strategy because twostep methods suffer from a loss of information (correlation) caused by the substitution of data with individual statistics. We introduce the FAERI method that was tailored from the 2factor ANOVA procedure. Our aim was to group the information associated with individual differences into one single statistic taking direction, correlation and expression level into account and to combine the advantages of the GlobalTest (groups with a variable answer), GlobalAncova (unidirectional groups) and SAMGS (independence with expression level) models whilst avoiding their respective limitations.
Methods
Datasets
Hypoxia datasets
Bosco et al. (2006) described the EMEXP445 dataset, available in the ArrayExpress repository. The experiment compares samples extracted from human monocytes. RNA was hybridized on 6 Affymetrix HGU133a microchips, among which 3 normoxia samples and 3 samples from cultures grown under hypoxic conditions [39].
Vengellur et al. (2005) studied cellular response to hypoxia on human Hep3b hepatocytes cell lines, considering 3 additional treatments known to mimic hypoxic conditions: cobalt chloride (100 μM), nickel chloride (100 μM), and DFO (100 μM). mRNA was extracted separately from two biological replicates, and cRNA was hybridized on Affymetrix GeneChip HGU95Av1 microchips. We used part of this dataset to compare samples grown under normoxic and hypoxic conditions, and did not use the other samples (used by the authors to compare hypoxia with molecules that mimic hypoxic conditions). This dataset is available on GEO (GSE1056) [40].
Kim et al. (2006) described the GSE4086 dataset, available on GEO. The authors studied cellular response to hypoxia on human B lymphocytes (P4936 cell line). Two biological replicates were used to extract mRNA from separate cultures. cRNA was hybridized on Affymetrix GeneChip HGU133A microchips [41].
In the work reported here, the three hypoxia datasets were preprocessed using GCRMA to study the effect of hypoxia and compare the ability of each geneset analysis method to detect common pathways from several datasets featuring a limited number of replicates.
Xiao et al. (2008) report a study of osteoporosis using microarrays hybridized with 10 low and 10 high BMD samples (postmenopausal woman, aged 5460). Blymphocyte mRNAs were isolated from 70 ml of blood, and cRNA was hybridized on Affymetrix GeneChip HGU133A microchips. This dataset is available both on GEO (GSE7429) and ArrayExpress (EGEOD7429) [42]. We used this dataset, preprocessed with GCRMA, to illustrate the development of the FAERI methodology.
Introduction to FAERI
As several genes are associated with multiple probesets, a bias could be introduced during the analysis at the genesetlevel. In the work reported below, we used only one probeset for each gene studied on the microchip, by selecting the probeset associated with the largest variability, to avoid giving too much weight to a gene due to the number of associated probesets. As a result of this selection, the words "probesets" and "genes" are synonyms in this text. The multiplicity of the probesets is not considered in this publication.
The analysis of variance or ANOVA relies on the study of the variability of the data with respect to one or more criteria to determine if that(those) criterion(a) is(are) responsible for the values observed. The simplest model, relying on a single criterion is equivalent to the Student t test [43]. When analyzing differential expression, a oneway ANOVA can evaluate the involvement of probesets under two or more conditions. The ANOVA with two classification criteria allows for study of the differential expression of a set of probesets (the first and second criteria respectively relate to the members of the geneset and to the definition of the experimental conditions compared).
The model that we chose to test the validity of this approach states that the expression data observed can be explained by dependence of the intensities measured on the probesets, the condition and the interdependence between those two parameters, since each probeset can potentially be associated with a different response under each of the tested conditions.
Where μ is the general mean, a_{i} is the contribution of factor a, b_{j} is the contribution of factor b, ab_{ij} is the contribution of the interaction of factors a and b and E_{(ij)k} is the residual contribution.
Where F_{x} is the F score associated with factor x, MS_{x} is the mean square value associated with factor x, n_{x} is the number of levels for factor x.
It is important to note that when the effect of the interaction is significant, no decisions can be made for the other criteria. The study of the effect associated with the condition is close to the GlobalAncova method [31, 44]. The study of the interaction is similar to the GlobalTest methodology and to the perspectives formulated by Mansmann and Meister (GlobalAncova), since the genesets within which probesets present a different answer are biologically interesting [44]. We state that this test must be complemented with a test on the condition because it is impossible to detect unidirectional and correlated changes by studying the interaction alone. To circumvent this limitation, we present a strategy in the next section for analysis of the FAERI methodology designed to simultaneously answer all possible configurations in a single test. For more details on the procedures of the FAERI methodology, see the Additional File 1 (Section 1).
FAERI
Geneset analysis of differential expression is influenced by the direction of the expression changes of the members and by possible interactions between the probesets and the conditions [29, 31, 44–46].
Expression level
Where X_{ij} is the expression value associated with the i^{th} probeset in the j^{th} microchip, M_{pool, i} and S_{pool, i} are the means and standard deviations computed for probeset i from all experiments (step 1). This procedure is repeated for each probeset in the dataset.
Direction of differential expression
Several probesets may be activated or repressed at the same time, but may also be associated with a variable expression change, depending on the biological context studied and the definition of the genesets. To provide a full answer to question Q_{0}, with no dependence on the direction of differential expression, two approaches can be proposed: up and downregulated probesets may be studied separately or simultaneously by computing a cumulative value for the absolute difference. The first solution consists of defining "activated" or "repressed" genesets to conduct a proper analysis. This approach relies on current knowledge that is incomplete, but is useful for diagnosis purposes to determine whether a patient has a pathology by analyzing signature genesets. The second solution is addressed by several methods by defining an individual statistic independent of the direction (absolute or squared value of the Student t statistic) [23, 37, 47, 48].
Where D_{i} = M_{i}^{B}  M_{i}^{A}, the difference in means between conditions A and B for the i^{th} probeset, Z_{ij} = Zscore associated with the i^{th} probeset in the j^{th} microchip, and Z^{D}_{ij} the directionally reduced Zscore associated with the i^{th} probeset in the j^{th} microchip.
It may be interesting to consider the median instead of the mean to evaluate the direction of differential expression. However, the performances tests we conducted previously in individual expression analyses show a reduction in the performance of the Student t test when using the median as the estimator [49] on biological data (and on spike in datasets, data not shown).
After this 2step reduction of the data, FAERI evaluates an F statistic as is done in the ANOVA2 procedure (Equation 1). The FAERI F* statistic associated with the condition effect is representative of the differences found based on biological reasoning. Each gene provides information on its differential expression, independently of the direction of that difference, and the absolute value of the individual t statistic remains unchanged.
Null distribution
Due to the dependence on geneset size introduced by directional reduction, the significance of the results cannot be evaluated using Fisher's theoretical distribution, which is used by the ANOVA procedure. We will refer to the statistic computed using the FAERI methodology as F*. We tested two solutions to determine the null distribution of the FAERI F* statistic, relying either on permutations or the use of random data.
The permutation strategy associated with a selfcontained null hypothesis relies on label sampling [23, 29]. Given the null hypothesis that no probeset is involved under the conditions compared, the permutations of the labels are performed independently for each probeset, independently of the correlation between the members of the geneset.
Due to the Z value reduction step, a second strategy may be used to evaluate the null distribution of the FAERI F* statistic. Indeed, as the null hypothesis states that there are no differences between the two conditions compared and as we postulate that individual expression values follow a normal distribution, Z standardization provides the possibility to establish the null distribution using random data from a normal distribution, and then apply the same 2step reduction to compute the reference F*_{0} values. Under the null hypothesis, the F* statistic computed on reduced test data should follow the same distribution as the F*_{0} statistic computed from reduced random data. Conversely, if the expression values of the geneset differ between two conditions, the F* statistic distribution leads to higher values than the F*_{0} values computed from random data.
This possibility to compute a reference distribution based on random data frees the FAERI method from the limited number of available permutations. Moreover, for each experimental strategy (number of replicates) and each geneset size, the null distribution can be computed once and stored for future analysis.
Results and Discussion
The effects of twostep reduction
In the second step of FAERI, we used a directional reduction to obtain a cumulated value of absolute expression changes in the genesets. We chose to use an empirical determination of the direction, easier to assess from the data than from prior incomplete knowledge. As the sign of the reduced values is not a feature of the biological mechanisms involved within genesets, the description of the mechanisms involved should be associated with individual analysis of the member genes based on raw data.
Mansmann and Meister recommend assessing the significance from permutations instead of the Fisher distribution when analyzing microarray data with a multivariate procedure. Their GlobalAncova procedure evaluates the significance of the test using the labelsampling strategy instead of the classic Fisher distribution [34]. The GlobalAncova procedure does not include a Zreduction step, and the iid requirement is not met due to genespecific expression levels, genespecific variability, and gene correlation patterns.
Performance evaluation
Performance evaluation on simulated data
Definition of the series of measurements used to evaluate the performances of the geneset differential expression analysis methods
Difference of expression  Correlation  Diff. Expressed  OverExpressed  UnderExpressed  Design  

Set 1  0.75  0.6  20  20  0  Unidirectional 
Set 2  0.75  0  20  20  0  Unidirectional 
Set 3  0  0  0  0  0  H_{0} 
Set 4  0.75  0.6  10  10  0  Unidirectional 
Set 5  0.75  0  10  10  0  Unidirectional 
Set 6  1  0.6  20  10  10  Bidirectional 
Set 7  1  0  20  10  10  Bidirectional 
Set 8  1  0.6  10  5  5  Bidirectional 
Set 9  1  0  10  5  5  Bidirectional 
Comparison of geneset differential expression analysis method performances, based on the simulation model proposed by M Ackerman
CutOff  a2.fixed  faeri. fixed.null  faeri. fixed.perms  GSA. mean*  GSA. absmean*  GSA. maxmean*  Globaltest. asymptotic  Globaltest. gamma  Globaltest. permutations  Gsea. pval  Gsea. fdr  Samgs. pval  Samgs. qval  

0.001  Set3  0  0  0  0  0  0  0  0  0  0  0  0  1  
H _{ 0 }  0.01  Set3  1  0  0  0  0  0  0  0  0  0  0  0  2  
0.1  Set3  3  3  3  12  0  0  2  5  3  2  1  2  27  
0.001  Set2  100  96  97  15  25  12  54  96  95  69  20  91  94  
100% DE  0.01  Set2  100  99  99  15  25  12  95  99  97  99  77  98  99  
Uni  0.1  Set2  100  100  100  78  72  64  99  99  99  100  100  99  100  
0.001  Set5  72  40  36  0  2  0  3  64  49  14  2  39  56  
50% DE  0.01  Set5  89  75  78  0  2  0  42  89  84  42  25  81  84  
0.1  Set5  100  94  94  7  17  3  93  99  98  93  84  96  99  
0.001  Set1  86  62  60  1  2  2  1  10  8  2  0  7  13  
100% DE  0.01  Set1  89  69  70  1  2  2  20  35  25  16  3  23  41  
Uni+Cor  0.1  Set1  95  80  80  11  23  11  58  59  58  46  47  56  73  
0.001  Set4  55  43  39  0  0  0  0  15  7  4  3  4  11  
50% DE  0.01  Set4  65  52  54  0  0  0  18  30  24  13  14  23  43  
0.1  Set4  81  65  65  1  13  1  56  60  57  43  47  56  73  
0.001  Set7  0  100  100  1  67  40  100  100  100  1  0  100  100  
100% DE  0.01  Set7  0  100  100  1  67  40  100  100  100  10  1  100  100  
Bidir  0.1  Set7  6  100  100  22  98  82  100  100  100  69  32  100  100  
0.001  Set9  0  84  82  0  7  2  32  95  93  0  0  86  93  
50% DE  0.01  Set9  0  95  95  0  7  2  93  100  100  1  0  100  100  
0.1  Set9  9  100  100  16  48  15  100  100  100  27  8  100  100  
0.001  Set6  0  84  84  0  11  8  5  28  17  6  1  15  25  
100% DE  0.01  Set6  0  88  89  0  11  8  39  50  43  17  8  43  61  
Bidir+Cor  0.1  Set6  2  93  93  35  49  54  79  79  78  57  42  78  88  
0.001  Set8  0  63  62  0  2  1  3  30  14  0  0  13  23  
50% DE  0.01  Set8  0  75  76  0  2  1  36  50  42  3  0  40  58  
0.1  Set8  3  86  86  28  27  12  75  78  75  31  15  76  85 
Comparison of the results illustrates the effect of correlations between members of the sets. For each method tested, the presence of intraset correlations reduces the performance of the analysis. For the simplest simulated sets (unidirectional), ANOVA2 provides the best results, followed by FAERI (sets 1, 2 and 4) and GlobalTest, as evaluated using a gamma distribution or permutations (set 5).
Moreover, the ANOVA2 and FAERI procedures are both associated with higher performance compared to all other methods based on the simplest simulation scenario (sets 1, 2 and 4). Indeed, FAERI detects set 1 in 62% of the cases, with a threshold of 0.001 and no false positives (set 3: 0%). At this threshold, FAERI is able to detect roughly 5 times more genesets than the best performing method currently available (SAMGS with qvalue: 13%). As a comparison, SAMGS and GlobalTest require a threshold that is 100 times higher to detect only 60% of set 1. Comparison of sets 4 and 5 highlights the method's abilities to detect genesets where half of the members are not differentially expressed. In this case, FAERI detects 43% of the genesets, compared to 15% when applying GlobalTest with a gamma distribution. The ANOVA2 and FAERI procedures thus appear to be the most appropriate to analyze unidirectional genesets.
The lower part of Table 4 concerns sets simulated with mixed differences in two directions (sets 6, 7, 8 and 9). The behavior of the methods differs: the ANOVA2 and GSA methodologies (using the mean as the geneset statistic), as well as GSEA, are not appropriate to analyze such sets. For each bidirectional set tested, the FAERI methodology performs best, with the exception of set 9 where SAMGS provides better results for a threshold below 0.05 (values were checked for this threshold, but not included in Table 4). The presence of a correlation between the members reduces the performance, as shown for the unidirectional scenarios.
Characterization of each method's performances
a2.fixed  faeri. fixed.null  faeri. fixed.perms  GSA. mean*  GSA. absmean*  GSA. maxmean*  Globaltest. asymptotic  Globaltest. gamma  Globaltest. perm  Gsea. pval  Gsea. fdr  Samgs. pval  

100% uni  100%  99%  99%  57%  62%  56%  97%  99%  98%  99%  88%  99% 
50% uni  94%  87%  89%  50%  51%  50%  71%  9'%  92%  71%  62%  90% 
100%uni + cor  94%  84%  85%  50%  51%  51%  60%  67%  62%  58%  51%  61% 
50%uni + cor  82%  76%  77%  50%  50%  50%  59%  65%  62%  56%  57%  61% 
100% bidir  50%  100%  100%  50%  83%  70%  100%  100%  100%  55%  50%  100% 
50% bidir  50%  97%  97%  50%  53%  51%  96%  100%  100%  50%  50%  100% 
100% bidir + cor  50%  94%  94%  50%  55%  54%  69%  75%  71%  58%  54%  71% 
50% bidir + cor  50%  87%  88%  50%  51%  50%  68%  75%  71%  51%  50%  70% 
Mean  71%  90%  91%  51%  57%  54%  77%  84%  82%  62%  58%  81% 
Rank  7  2  1  12  10  11  6  3  4  8  9  5 
Table 5 reveals that the best performing methods, independently of the structure of the set, are all based on a "global" strategy. The ranking of the methods is consistent with the models considered by these methods:

ANOVA2 is the most appropriate method for unidirectional groups;

GlobalTest, which uses a statistic representative of response variability, is suited for the study of both uni and bidirectional non correlated genesets;

SAMGS, using an individual statistic, appears to be less appropriate for the analysis of correlated genesets, since this information is lost during the procedure;

FAERI, developed to detect sets independently of their mathematical structure, is appropriate for each type of set tested;

ANOVA2 and FAERI provide an appropriate analysis of sets with correlated members, respectively using unidirectional and bidirectional definition of sets;

Methods making use of a twostep strategy provide lower performance levels (GSA and GSEA).
Real data: study of hypoxia
Analysis of a genuine biological dataset offers the opportunity to qualitatively evaluate the results and provide a biological validation of geneset analysis methods through comparison of the results with current biological knowledge. We chose to use three datasets related to response under hypoxic conditions. The definition of the genesets relies on the MSIGDB databank (version 2.5), published by the authors of the GSEA methodology [25]. MSIGDB consists of several libraries of genesets (categories). The analysis reported below focuses on the C2.kegg category.
List of genesets in the C2.KEGG category detected significantly by the FAERI.perms method (detection threshold of 0.05 for the pvalues)
faeri. fixed.perms  faeri. fixed.null  a2.fixed  GSA. mean  GSA. absmean  GSA. maxmean  Globaltest. asymptocic  Globaltest. gamma  Globaltest. perm  Gsea. pval  Gsea. fdr  Samgs. pval  Samgs. qval  Gse1056  Gse4086  

hsa00010_glycolysis_and_gluconeogenesis  1.8E03  0.64  1.2E13  0.96  0.95  0.98  0.020  7.8E03  0.10  0.11  0.17  0.10  0.08  0.047  8.0E04 
hsa00020_citrate_circle  6.9E03  0.38  1.7E05  0.19  0.65  0.10  0.08  0.035  0.10  0.22  0.40  0.19  0.08  0.020  1.9E03 
hsa00030_pentose_phosphate_pathway  8.4E03  0.41  9.5E04  0.95  0.90  0.97  0.024  8.8E03  0.10  0.23  0.28  0.10  0.08  3.0E03  0.019 
hsa00051_fructose_and_mannose_metabolism  0.011  0.97  6.0E04  0.92  0.68  0.97  0.043  0.014  0.10  0.24  0.38  0.10  0.08  0.025  3.9E03 
hsa00100_biosynthesis_of_steroids  4.0E04  0.94  2.5E04  0.15  0.75  0.0E+00  0.14  0.10  0.30  0.11  0.32  0.10  0.08  2.0E04  1.0E04 
hsa00190_oxidative_phosphorylation  0.036  0.99  4.5E07  0.23  0.48  0.27  0.30  0.35  0.50  0.40  0.53  0.49  0.08  0.0E+00  0.0E+00 
hsa00230_purine_metabolism  0.047  1.00  0.12  0.69  0.43  0.63  0.13  0.10  0.30  0.23  0.51  0.49  0.08  7.0E04  1.0E04 
hsa00480_glutathione_metabolsim  0.025  1.00  1.0E03  0.19  0.52  0.10  0.27  0.30  0.50  0.22  0.37  0.30  0.08  9.0E04  0.012 
hsa00511_n_glycan_degradation  9.5E03  0.90  0.020  0.19  0.59  0.08  0.15  0.14  0.30  0.29  0.48  0.30  0.08  0.06  0.21 
Hsa00530_aminosugars_metabolism  0.031  0.19  0.040  0.19  0.55  0.07  0.10  0.06  0.30  0.18  0.48  0.30  0.08  0.026  0.12 
Hsa00620_pyruvate_metabolism  0.011  1.00  2.6E05  0.09  0.68  0.045  0.06  0.031  0.10  0.11  0.19  0.10  0.08  8.2E03  5.0E04 
Hsa00710_carbon_fixation  0.022  1.00  3.3E03  0.95  0.90  0.97  0.045  0.017  0.10  0.23  0.35  0.10  0.08  0.019  0.034 
Hsa00720_reductive_caboxylate_cycle  0.031  1.00  4.2E04  0.10  0.75  0.0E+00  0.07  0.034  0.10  0.11  0.30  0.10  0.08  4.6E03  0.038 
Hsa00900_terpenoid_biosynthesis  0.018  0.047  0.010  0.25  0.77  0.025  0.06  0.019  0.10  0.48  0.63  0.10  0.08  0.07  0.035 
Hsa01032_glycan_structures_degradation  4.7E03  0.83  3.9E03  0.19  0.55  0.13  0.14  0.13  0.30  0.10  0.43  0.40  0.08  0.05  0.08 
Hsa01510_neurodegenerative_diseases  0.010  1.00  0.21  0.92  0.92  0.97  0.09  0.043  0.10  0.71  0.72  0.10  0.08  0.08  9.0E03 
Hsa03010_ribosome  5.0E04  0.91  1.1E10  0.78  0.57  0.86  0.24  0.26  0.40  0.11  0.22  0.29  0.08  0.27  2.0E04 
Hsa03320_ppar_signaling_pathway  0.014  1.00  3.9E03  0.17  0.55  0.035  0.06  0.039  0.10  0.11  0.27  0.40  0.08  0.017  6.9E03 
Hsa04010_mapk_signaling_pathway  0.020  0.90  0.57  0.59  0.30  0.33  0.22  0.22  0.50  0.30  0.54  0.49  0.08  0.0E+00  0.0E+00 
Hsa04130_snare_interactions_in_vesicular_transport  8.6E03  0.97  0.11  0.18  0.56  0.11  0.16  0.10  0.40  0.61  0.71  0.40  0.08  0.48  2.8E03 
Hsa4150_mtor_signaling_pathway  0.038  0.23  4.8E03  0.81  0.47  0.75  0.039  0.010  0.10  0.12  0.32  0.38  0.08  0.017  0.039 
Hsa04210_apoptosis  3.7E03  0.05  0.39  0.41  0.31  0.41  0.20  0.16  0.40  0.81  0.76  0.49  0.08  1.0E04  9.0E04 
Hsa04370_vegf_signaling_pathway  0.030  0.08  2.6E03  0.87  0.29  0.83  0.047  0.017  0.10  0.11  0.23  0.10  0.08  7.0E04  0.026 
Hsa04510_focal_adhesion  0.035  0.19  0.025  0.73  0.26  0.48  0.07  0.034  0.20  0.11  0.24  0.19  0.08  4.3E03  0.0E+00 
Hsa04620_toll_like_receptor_signaling_pathway  5.7E03  0.047  0.020  0.61  0.47  0.66  0.049  0.018  0.10  0.11  0.44  0.10  0.08  0.033  4.0E04 
Hsa04650_natural_killer_cell_mediated_cytotoxicity  0.016  0.10  0.58  0.29  0.43  0.17  0.20  0.17  0.50  0.60  0.72  0.72  0.08  7.0E03  0.0E+00 
Hsa04660_t_cell_receptor_signaling_pathway  0.036  0.10  0.65  0.95  0.55  0.97  0.07  0.025  0.10  0.11  0.41  0.41  0.08  1.6E03  7.1E03 
Hsa04662_b_cell_receptor_signaling_pathway  1.5E03  0.97  0.78  0.40  0.48  0.13  0.06  0.011  0.10  0.53  0.63  0.63  0.08  2.2E03  2.0E04 
Hsa04664_fc_epsilon_ri_signaling_pathway  0.039  0.97  0.05  0.91  0.55  0.97  0.032  6.9E03  0.10  0.11  0.25  0.25  0.08  8.9E03  0.022 
Hsa04810_regulation_of_actin_cytoskeleton  0.024  0.52  0.023  0.30  0.40  0.38  0.29  0.32  0.50  0.42  0.52  0.52  0.08  1.0E04  0.0E+00 
Hsa05040_huntingtons_disease  3.0E04  0.50  6.6E03  0.78  0.95  0.98  0.13  0.06  0.10  0.29  0.45  0.45  0.08  0.83  1.1E03 
Hsa05150_cholera_infection  0.019  1.00  2.9E03  0.37  0.73  0.11  0.045  0.015  0.10  0.37  0.51  0.51  0.08  0.12  9.4E03 
Hsa05120_epithelial_cell_signaling_in_helicobacter_pylorii_infection  0.036  1.00  1.1E03  0.11  0.55  0.045  0.15  0.09  0.30  0.21  0.48  0.48  0.08  1.3E03  1.3E03 
Hsa05130_pathogenic_escherishia_coli_infection_ehec  0.047  1.00  0.044  0.26  0.47  0.18  0.20  0.17  0.40  0.18  0.29  0.29  0.08  4.9E03  3.8E03 
Hsa05131_pathogenic_escherishia_coli_infection_epec  0.047  1.00  0.044  0.26  0.47  0.18  0.20  0.17  0.40  0.18  0.29  0.29  0.08  4.9E03  3.8E03 
Hsa05210_colorectal_cancer  0.041  7.1E03  0.36  0.67  0.29  0.41  0.16  0.09  0.40  0.22  0.35  0.35  0.08  0.011  8.8E03 
Hsa05211_renal_cell_carcinoma  0.017  7.1E03  5.3E04  0.91  0.55  0.97  0.036  9.9E03  0.10  0.12  0.24  0.24  0.08  8.2E03  0.021 
Hsa05212_pancreatic_cancer  0.013  9.5E03  0.44  0.47  0.29  0.34  0.09  0.041  0.20  0.43  0.54  0.54  0.08  4.3E03  3.0E03 
Hsa05216_thyroid_cancer  4.0E03  0.86  0.91  0.53  0.55  0.20  0.17  0.15  0.30  0.30  0.54  0.54  0.08  1.0E03  0.10 
Hsa05219_bladder_cancer  0.048  0.47  0.60  0.57  0.55  0.61  0.11  0.08  0.30  0.91  0.80  0.80  0.08  9.1E03  0.08 
Hsa05220_chronic_myeloid_leukemia  0.028  1.00  0.73  0.37  0.31  0.41  0.15  0.09  0.40  0.61  0.65  0.65  0.08  1.0E04  7.9E03 
Hsa05221_acute_myeloid_leukemia  6.0E04  1.00  0.24  0.24  0.31  0.26  0.15  0.11  0.30  0.60  0.72  0.72  0.08  0.0E+00  0.018 
Table 6 shows that currently available methods fail to detect the genesets selected by FAERI (permutations). Most of the genesets selected by FAERI are also detected by at least one other global method, the ANOVA2 or the GlobalTest. The twostep GSA.maxmean method seems to also be able to detect a few of the same sets. GSEA, SAMGS, GSA.mean and GSA.absmean do not find any significant genesets. Finally, the pvalues obtained for datasets GSE1056 and GSE4086 are mostly significant or highly significant, validating the relevance of these genesets with regard to the biological question studied.
This list can be split into 3 categories. First, genesets concerning several metabolic pathways suggest metabolic adaptations occur as a cellular response to oxygen deprivation. Second, several genesets address signaling pathways and are known to be involved in the hypoxic stress response. Genesets of the third category are related to pathologies, mostly involving cancers, for which a hypoxic environment has been observed [52–54]. These results illustrate the ability of FAERI to detect groups known to be involved in hypoxic responses or hypoxiarelated pathologies, and to detect those groups independently in 3 datasets related to hypoxia. In particular, we should point out the strong coherence between genesets detected by FAERI on a small number of replicates (3*3), either within the top list or between top lists associated with the three datasets.
List of the genesets of the C2.KEGG category detected with a highly significant threshold (0.01) by the other methods
Faeri. fixed.null  Faeri. fixed.perms  GSA. mean  GSA. absmean  GSA. maxmean  Globaltest. symptoti  Globaltest. gamma  Globaltest. permutations  Gsea. pval  Gsea. fdr  Samgs. pval  Samgs. qval  

Hsa00010_glycolysis_and_neoglucogenesis  0.64  1.8E03  0.96  0.95  0.98  0.020  7.8E03  0.1  0.11  0.17  0.1  0.08 
Hsa00030_pentose_phosphate_pathway  0.41  8.4E03  0.95  0.9  0.97  0.024  8.8E03  0.1  0.23  0.28  0.1  0.08 
Hsa00052_galactose_metabolism  0.73  0.08  0.95  0.77  0.97  0.018  8.3E03  0.1  0.11  0.13  0.1  0.08 
Hsa00053_ascorbate_and_aldarate_metabolism  0.97  0.18  0.0E+00  0.72  0.06  0.16  0.16  0.3  0.11  0.42  0.19  0.08 
Hsa00071_fatty_acid_metabolism  0.71  0.15  0.0E+00  0.52  0.045  0.2  0.18  0.5  0.11  0.3  0.49  0.08 
Hsa00100_biosynthesis_of_steriods  0.94  4.0E04  0.15  0.75  0.0E+00  0.14  0.1  0.3  0.11  0.32  0.1  0.08 
Hsa00272_cysteine_metabolism  0.94  0.25  0.34  0.62  0.1  0.028  6.7E03  0.1  0.21  0.46  0.19  0.08 
Hsa00340_histidine_metabolism  1  0.17  0.0E+00  0.36  0.11  0.35  0.41  0.5  0.11  0.21  0.59  0.9 
Hsa00500_starch_and_sucrose_metabolism  1  0.08  0.95  0.6  0.97  0.020  6.7E03  0.1  0.11  0.18  0.1  0.08 
Hsa00512_o_glycan_biosynthesis  0.2  0.22  0.0E+00  0.44  0.0E+00  0.36  0.42  0.5  0.64  0.71  0.1  0.08 
Hsa00521_streptomycin_biosynthesis  0.94  0.1  0.95  0.96  0.97  0.016  8.7E03  0.1  0.11  0.17  0.1  0.08 
Hsa00640_propanoate_metabolism  0.92  0.06  0.15  0.6  0.0E+00  0.1  0.07  0.3  0.11  0.31  0.19  0.08 
Hsa00720_reductive_carboxylate_pathway  1  0.031  0.1  0.75  0.0E+00  0.7  0.034  0.1  0.11  0.3  0.1  0.08 
Hsa04664_fc_epsilon_ri_signaling_pathway  0.97  0.039  0.91  0.55  0.97  0.032  6.9E03  0.1  0.11  0.25  0.1  0.08 
Hsa0521_renal_cell_carcinoma  7.1E03  0.017  0.91  0.55  0.97  0.036  9.9E03  0.1  0.12  0.24  0.1  0.08 
Comparison of the number of groups detected in the C2.KEGG category and of the number of common detections in three datasets
0.010  A2. fixed  Faeri. fixed.null  Faeri. fixed.perms  GSA. mean*  GSA. absmean*  GSA. maxmean*  Globaltest. asymptotic  Globaltest. gamma  Globaltest. permutations  Gsea. pval  Gsea. fdr  Samgs. pval  Samgs. qval 

Emexp445  62  7  14  14  5  19  0  8  0  0  0  0  0 
GSE1056  36  9  51  5  0  8  1  15  0  0  0  0  0 
GSE4086  93  89  94  49  38  62  0  0  0  0  0  NA  NA 
Emexp445GSE1056  5  0  6  1  0  0  0  1  0  0  0  0  0 
Emexp445GSE4086  24  5  9  8  0  11  0  0  0  0  0  NA  NA 
GSE1056GSE4086  21  7  37  4  0  4  0  0  0  0  0  NA  NA 
Emexp445GSE1056GSE4086  4  0  3  1  0  0  0  0  0  0  0  NA  NA 
0.050  A2. fixed  Faeri. fixed.null  Faeri. fixed.perms  GSA. mean*  GSA. absmean*  GSA. maxmean*  Globaltest. asymptotic  Globaltest. gamma  Globaltest. permutations  Gsea. pval  Gsea. fdr  Samgs. pval  Samgs. qval 
Emexp445  62  18  42  14  5  23  26  45  0  0  0  0  0 
GSE1056  51  27  80  8  2  12  33  70  40  11  0  15  69 
GSE4086  119  119  142  49  38  62  32  185  0  0  0  NA  NA 
Emexp445GSE1056  15  6  34  1  0  0  4  20  0  0  0  0  0 
Emexp445GSE4086  48  14  37  8  0  13  13  44  0  0  0  NA  NA 
GSE1056GSE4086  31  23  71  4  1  6  7  69  0  0  0  NA  NA 
Emexp445GSE1056GSE4086  13  4  31  1  0  0  2  20  0  0  0  NA  NA 
Setting a significance threshold of 1% for the pvalues, the ranking of the methods detecting more genesets common to the two experiments is the following: FAERI.permutations (6 common genesets for at least 14 genesets detected), ANOVA2 (5/35), GlobalTest.gamma (1/8) and GSA.mean (1/14). The other methods do not intersect. When the significance threshold is set to 5%, the ranking of the methods is the following: FAERI.permutations (34/42), GlobalTest.gamma (20/45), FAERI.null (6/18), ANOVA2 (15/51), GlobalTest.asymptotic (4/26) and GSA.mean (1/8). The other methods do not intersect. These results suggest that the bidirectional genesets, detected by FAERI and GlobalTest, are an important part of the genesets simultaneously involved in both datasets, compared with the ANOVA2 and GSA.mean procedures.
To quantify the abilities of the methods to provide the same result from different datasets, we propose to compute Pearson's coefficients of correlation on the ranks associated with the genesets analyzed, by pairwise comparison of the three datasets, for each method and each category of genesets defined (See Additional File 1, additional table 1).
For all the categories tested, the FAERI.permutations methodology is associated with a higher coefficient of correlation. Conversely, the ANOVA2, GSEA and GSA.mean provide poorly correlated results. The coefficients of correlation obtained for the other methods are intermediary between these two extremes and depend on the geneset category.
Conclusion
Conclusions
Geneset differential expression analysis is a far more complex task than the individual analysis of expression changes. The diversity of the biological criteria involved and prior definition of the genesets has an impact on the mathematical properties of the expression subsets to be analyzed. Furthermore, the diversity of available analysis procedures, each based on specific strategies and null hypotheses, must address these properties. Thus, design of the analysis strategy is not that simple. Current methods involve overrepresentation analysis (ORA, not considered here), and functional class scoring (FCS). This last category of methods relies either on 2step (posthoc) or global strategies (using raw data).
In this paper, we address this question by considering the biological properties of genesets with regard to the underlying mathematical properties of the associated expression values. Focusing on expression levels, the direction of regulation and potential correlations between geneset members, we developed the FAERI methodology (Functional Analysis: Evaluation of Response Intensities). FAERI is a global methodology tailored from a 2factor ANOVA procedure by a 2step reduction of the data, and is evaluated with respect to the selfcontained nullhypothesis (using label sampling or random data).
Evaluations performed on random data reveal the ability of each method to detect sets simulated according to 8 scenarios, relying on mathematical properties. The ANOVA2 procedure performs best when analyzing unidirectional genesets and FAERI performs similarly. Both methods outperform each currently available method. When the definition of the sets involves members regulated in both directions, ANOVA2 performance drops, as expected, and our proposed FAERI methodology outperforms all tested methods. Both methods provide an appropriate solution for the analysis of correlated data (exclusively on unidirectional genesets in the case of ANOVA2). FAERI thus constitutes an improvement over all tested methods, and turns out to be the optimal method for testing question Q_{0} ("Which known genesets are associated with different expression profiles under the two conditions compared?").
A realworld example of analysis is reported for three datasets on cellular response to hypoxia. The results obtained from analysis of the EMEXP445 dataset illustrate that FAERI (evaluated using permutations) is able to detect relevant results when a strict cutoff is used, compared to the other methodologies. The genesets detected by FAERI are related to three categories, respectively metabolic perturbations, hypoxia signaling/response, and hypoxiarelated pathologies. The results of other global methods confirm these results, and posthoc methods fail to detect any significant genesets. The results provided by FAERI from analysis of the two additional datasets reveal its ability to detect the same sets using several related datasets. Focusing on the top list obtained with several datasets, we showed that FAERI not only detects more sets, but also presents a larger intersection of the results.
We proposed to score this last assessment on the whole list of sets by computing the Pearson correlation coefficient on the ranked list of genesets, for each method, using pairwise comparisons between the three datasets. Among all methods tested, FAERI provides the best correlated results between related datasets, regardless of the source of geneset definition used (each category of the MSIGDB databank was used for this purpose). FAERI thus outperforms all methods tested for its ability to attribute similar scores to each geneset from several datasets.
Several recommendations emerge from this work. First, we suggest that differential expression analyses of microarray data be studied first by performing a geneset analysis with FAERI, and then that the results be further characterized using either an individual analysis or mathematical characterization of the sets detected. This approach eases interpretation of the results with regard to the correlation and direction of probesetspecific expression changes encountered for each geneset. Alternatively, genesets can be first classified with regard to the mathematical properties of the geneset expression values, and then the data can be analyzed using distinct methodologies (for example, applying the ANOVA2 procedure on unidirectional sets).
Perspectives
As a perspective for future work, we suggest to adapt FAERI so it would be able to test unequal sample datasets. Several adaptations to the ANOVA2 procedure have been suggested to this end. To best adapt FAERI for this purpose, performance evaluations are required. Another interesting development would be to develop robust variants of FAERI based on the study of the impact of extreme values.
An additional comment may be formulated about the empirical definition of the direction from the difference in means, used in the second step of the FAERI procedure. Indeed, genes slightly differing in one direction may empirically seem to be involved in the opposite direction, as sampling a small number of replicates can lead to a smaller or larger distribution than the population. Thus, the directional reduction step may lead to an incorrect definition of the sign of the expression change with regards to the biological structure of the data. An alternative would be to characterize first the direction associated with each gene based on current knowledge of the biological processes. Similarly, another alternative adaptation of the procedure would be to determine which genes are associated with an expression change, and to use the directional reduction step only on those genes. However, the use of prior knowledge on the expression and directional changes for each gene would be associated with important biases: incorrect prior information would reduce the significance of the geneset analysis, and this bias is more probable for lessstudied genesets/biological mechanisms. Thus, the evaluation of the null distribution with realistic background noise would be geneset specific, depending on the number of errors in the annotation. In the future, we also plan to develop a nonparametric equivalent to FAERI, by applying a rankbased multivariate analysis procedure after the 2step reduction of data. At least three options may be considered for further developments: ranking expression values at the level of the probesets, at the level of the genesets, or at the level of the whole dataset.
The evaluation of significance may also lead to several adaptations. First, it would be interesting to add a third significance evaluation strategy based on simultaneous label sampling, where permutations would be performed without breaking the association with the cel file. Such a null distribution would provide an appropriate pvalue for the evaluation of highly correlated genesets. However, the small number of available permutations would not allow for discrimination between genesets when the number of replicates is small. Both approaches introduce a bias during the pvalue evaluation procedure: (i) the independent model assumes that the genes are not correlated, underevaluating the pvalue of cooccurring events; and (ii) the correlated model accordingly overevaluates the pvalue when the correlation between genes is not perfect. As the correlation pattern between genes is complex, the most appropriate pvalue evaluation procedure should be somewhere in between the independent and correlated permutation models, with a genesetspecific balance between these two models.
Another interesting perspective for evaluation of the pvalue would be to improve the model used to generate the null distribution from random data. By studying several datasets first using individual and geneset analyses, it would be possible to characterize the correlation between genes in several situations. Then the network of associations could be used to count the average number of connections, to catch patterns of association, etc. Using such a strategy, the evaluation of the null hypothesis would consist in generating random networks with similar patterns of association and similar distributions of connectivity, and to use these patterns using multivariate random data built from the simulated correlation matrices. As an extension of this model, it would also be possible to include a supplemental parameter during simulation of the null distribution: the correlation pattern between genes may differ between the two groups.
Performance evaluation of geneset analysis methods is not a trivial task, as each method is based on distinct null hypotheses. Previously published methods have been validated following several simulation schemes (using a limited number of scenarios) or running analyses on specific biological datasets (featuring more than 10 replicates for each tested condition). Nevertheless, those validations usually focused on the method developed by the authors, compared with only one method (usually GSEA), if any, and a limited number of genesets. Conversely, publication of individual analysis methods requires extensive validations on realworld, simulated and spikein datasets. The validations reported here reproduce the simulation scheme used by Ackerman et al. (2009) as it represents the most comprehensive scenario we could find in the scientific literature (in a paper that compares the parameterization of current methods). The example analysis reported here was performed on small datasets (3 replicates associated with each condition), as this kind of dataset is far more frequent and more difficult to analyze. We hope that the validation strategy reported here will be reproduced by authors of future methodologies to compare their findings with current methods and to provide a comprehensive evaluation of their methods with regards to several biological scenarios and the underlying mathematical properties of the expression values.
In a previous work, we reported a new benchmarking strategy for individual expression analysis on realworld data involving several datasets [55]. In future work, this procedure will be tailored for a similar evaluation of geneset analysis methods with regard to the mathematical structure of the sets, as depicted in this paper on simulated data, and in agreement with the benchmarking study reported by Song et al. [56].
Availability
FAERI and the ANOVA2 procedure were implemented using R language, and the source code is included in PEGASE, an R package we designed for differential expression analysis [49]. URL: (http://urbmcluster.urbm.fundp.ac.be/phoenix)
Notes
Declarations
Acknowledgements
The authors wish to thank the DGTRE, FNRSFRS Télévie, FRIA, GlaxoSmithKline Biologicals, Darlene Goldstein, Marcel Remon, Christophe Lambert, JeanLouis Ruelle and the members of the BCF (SIB).
Authors’ Affiliations
References
 Hatfield GW, Hung SP, Baldi P: Differential analysis of DNA microarray gene expression data. Mol Microbiol 2003, 47: 871–877. 10.1046/j.13652958.2003.03298.xView ArticlePubMedGoogle Scholar
 Trajkovski I, Lavrac N, Tolar J: SEGS: search for enriched gene sets in microarray data. J Biomed Inform 2008, 41: 588–601. 10.1016/j.jbi.2007.12.001View ArticlePubMedGoogle Scholar
 Watson M: CoXpress: differential coexpression in gene expression data. BMC Bioinformatics 2006, 7: 509. 10.1186/147121057509View ArticlePubMedPubMed CentralGoogle Scholar
 Altman RB, Raychaudhuri S: Wholegenome expression analysis: challenges beyond clustering. Curr Opin Struct Biol 2001, 11: 340–347. 10.1016/S0959440X(00)002128View ArticlePubMedGoogle Scholar
 Kim SY, Kim YS: A gene sets approach for identifying prognostic gene signatures for outcome prediction. BMC Genomics 2008, 9: 177. 10.1186/147121649177View ArticlePubMedPubMed CentralGoogle Scholar
 Farmer P, Bonnefoi H, Becette V, TubianaHulin M, Fumoleau P, Larsimont D, Macgrogan G, Bergh J, Cameron D, Goldstein D, et al.: Identification of molecular apocrine breast tumours by microarray analysis. Oncogene 2005, 24: 4660–4671. 10.1038/sj.onc.1208561View ArticlePubMedGoogle Scholar
 Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531–537. 10.1126/science.286.5439.531View ArticlePubMedGoogle Scholar
 Huber BR, Bulyk ML: Metaanalysis discovery of tissuespecific DNA sequence motifs from mammalian gene expression data. BMC Bioinformatics 2006, 7: 229. 10.1186/147121057229View ArticlePubMedPubMed CentralGoogle Scholar
 Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, Glover E, Silander T, Craven MW, Reddy JK, et al.: Identification of toxicologically predictive gene sets using cDNA microarrays. Mol Pharmacol 2001, 60: 1189–1194.PubMedGoogle Scholar
 Brazma A, Jonassen I, Vilo J, Ukkonen E: Predicting gene regulatory elements in silico on a genomic scale. Genome Res 1998, 8: 1202–1215.PubMedPubMed CentralGoogle Scholar
 Bucher P: Regulatory elements and expression profiles. Curr Opin Struct Biol 1999, 9: 400–407. 10.1016/S0959440X(99)800542View ArticlePubMedGoogle Scholar
 Bussemaker HJ, Li H, Siggia ED: Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc Natl Acad Sci USA 2000, 97: 10096–10100. 10.1073/pnas.180265397View ArticlePubMedPubMed CentralGoogle Scholar
 Hughes JD, Estep PW, Tavazoie S, Church GM: Computational identification of cisregulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 2000, 296: 1205–1214. 10.1006/jmbi.2000.3519View ArticlePubMedGoogle Scholar
 Kim SY, Kim Y: Genomewide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data. BMC Bioinformatics 2006, 7: 330. 10.1186/147121057330View ArticlePubMedPubMed CentralGoogle Scholar
 AlShahrour F, DiazUriarte R, Dopazo J: Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics 2005, 21: 2988–2993. 10.1093/bioinformatics/bti457View ArticlePubMedGoogle Scholar
 Barry WT, Nobel AB, Wright FA: Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005, 21: 1943–1949. 10.1093/bioinformatics/bti260View ArticlePubMedGoogle Scholar
 Zahn JM, Sonu R, Vogel H, Crane E, MazanMamczarz K, Rabkin R, Davis RW, Becker KG, Owen AB, Kim SK: Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet 2006, 2: e115. 10.1371/journal.pgen.0020115View ArticlePubMedPubMed CentralGoogle Scholar
 Cho RJ, Huang M, Campbell MJ, Dong H, Steinmetz L, Sapinoso L, Hampton G, Elledge SJ, Davis RW, Lockhart DJ: Transcriptional regulation and function during the human cell cycle. Nat Genet 2001, 27: 48–54.PubMedGoogle Scholar
 Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, Tainsky MA: OntoTools, the toolkit of the modern biologist: OntoExpress, OntoCompare, OntoDesign and OntoTranslate. Nucleic Acids Res 2003, 31: 3775–3781. 10.1093/nar/gkg624View ArticlePubMedPubMed CentralGoogle Scholar
 Khatri P, Done B, Rao A, Done A, Draghici S: A semantic analysis of the annotations of the human genome. Bioinformatics 2005, 21: 3416–3421. 10.1093/bioinformatics/bti538View ArticlePubMedPubMed CentralGoogle Scholar
 Man MZ, Wang X, Wang Y: POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 2000, 16: 953–959. 10.1093/bioinformatics/16.11.953View ArticlePubMedGoogle Scholar
 Yi M, Stephens RM: SLEPR: a samplelevel enrichmentbased pathway ranking method  seeking biological themes through pathwaylevel consistency. PLoS One 2008, 3: e3288. 10.1371/journal.pone.0003288View ArticlePubMedPubMed CentralGoogle Scholar
 Efron B, Tibshirani R: On testing the significance of sets of genes. Ann Appl Stat 2007, 1: 107–129. 10.1214/07AOAS101View ArticleGoogle Scholar
 Keller A, Backes C, Lenhof HP: Computation of significance scores of unweighted Gene Set Enrichment Analyses. BMC Bioinformatics 2007, 8: 290. 10.1186/147121058290View ArticlePubMedPubMed CentralGoogle Scholar
 Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, et al.: PGC1alpharesponsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003, 34: 267–273. 10.1038/ng1180View ArticlePubMedGoogle Scholar
 Pavlidis P, Qin J, Arango V, Mann JJ, Sibille E: Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res 2004, 29: 1213–1222. 10.1023/B:NERE.0000023608.29741.45View ArticlePubMedGoogle Scholar
 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledgebased approach for interpreting genomewide expression profiles. Proc Natl Acad Sci USA 2005, 102: 15545–15550. 10.1073/pnas.0506580102View ArticlePubMedPubMed CentralGoogle Scholar
 Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ: Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 2005, 102: 13544–13549. 10.1073/pnas.0506577102View ArticlePubMedPubMed CentralGoogle Scholar
 Goeman JJ, Buhlmann P: Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 2007, 23: 980–987. 10.1093/bioinformatics/btm051View ArticlePubMedGoogle Scholar
 Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, Einecke G, Famulski KS, Halloran P, Yasui Y: Improving gene set analysis of microarray data by SAMGS. BMC Bioinformatics 2007, 8: 242. 10.1186/147121058242View ArticlePubMedPubMed CentralGoogle Scholar
 Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004, 20: 93–99. 10.1093/bioinformatics/btg382View ArticlePubMedGoogle Scholar
 Hummel M, Meister R, Mansmann U: GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics 2008, 24: 78–85. 10.1093/bioinformatics/btm531View ArticlePubMedGoogle Scholar
 Kim SY, Volsky DJ: PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 2005, 6: 144. 10.1186/147121056144View ArticlePubMedPubMed CentralGoogle Scholar
 Mansmann U, Meister R: Testing differential gene expression in functional groups. Goeman's global test versus an ANCOVA approach. Methods Inf Med 2005, 44: 449–453.PubMedGoogle Scholar
 Student: The Probable Error of a Mean. Biometrika 1908, 1–25.Google Scholar
 Larsson O, Wahlestedt C, Timmons JA: Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinformatics 2005, 6: 129. 10.1186/147121056129View ArticlePubMedPubMed CentralGoogle Scholar
 Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98: 5116–5121. 10.1073/pnas.091062498View ArticlePubMedPubMed CentralGoogle Scholar
 Zhang S: A comprehensive evaluation of SAM, the SAM Rpackage and a simple modification to improve its performance. BMC Bioinformatics 2007, 8: 230. 10.1186/147121058230View ArticlePubMedPubMed CentralGoogle Scholar
 Bosco MC, Puppo M, Santangelo C, Anfosso L, Pfeffer U, Fardin P, Battaglia F, Varesio L: Hypoxia modifies the transcriptome of primary human monocytes: modulation of novel immunerelated genes and identification of CCchemokine ligand 20 as a new hypoxiainducible gene. J Immunol 2006, 177: 1941–1955.View ArticlePubMedGoogle Scholar
 Vengellur A, Phillips JM, Hogenesch JB, LaPres JJ: Gene expression profiling of hypoxia signaling in human hepatocellular carcinoma cells. Physiol Genomics 2005, 22: 308–318. 10.1152/physiolgenomics.00045.2004View ArticlePubMedGoogle Scholar
 Kim JW, Tchernyshyov I, Semenza GL, Dang CV: HIF1mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 2006, 3: 177–185. 10.1016/j.cmet.2006.02.002View ArticlePubMedGoogle Scholar
 Xiao Y, Segal MR, Rabert D, Ahn AH, Anand P, Sangameswaran L, Hu D, Hunt CA: Assessment of differential gene expression in human peripheral nerve injury. BMC Genomics 2002, 3: 28. 10.1186/14712164328View ArticlePubMedPubMed CentralGoogle Scholar
 Thomas JG, Olson JM, Tapscott SJ, Zhao LP: An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 2001, 11: 1227–1236. 10.1101/gr.165101View ArticlePubMedPubMed CentralGoogle Scholar
 Nilsson R, Pena JM, Bjorkegren J, Tegner J: Detecting multivariate differentially expressed genes. BMC Bioinformatics 2007, 8: 150. 10.1186/147121058150View ArticlePubMedPubMed CentralGoogle Scholar
 Breitling R, Amtmann A, Herzyk P: Graphbased iterative Group Analysis enhances microarray interpretation. BMC Bioinformatics 2004, 5: 100. 10.1186/147121055100View ArticlePubMedPubMed CentralGoogle Scholar
 Liu Q, Dinu I, Adewale AJ, Potter JD, Yasui Y: Comparative evaluation of geneset analysis methods. BMC Bioinformatics 2007, 8: 431. 10.1186/147121058431View ArticlePubMedPubMed CentralGoogle Scholar
 Edelman E, Porrello A, Guinney J, Balakumaran B, Bild A, Febbo PG, Mukherjee S: Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genomewide expression profiles. Bioinformatics 2006, 22: e108–116. 10.1093/bioinformatics/btl231View ArticlePubMedGoogle Scholar
 Pavlidis P, Furey TS, Liberto M, Haussler D, Grundy WN: Promoter regionbased classification of genes. Pac Symp Biocomput 2001: 151–163.Google Scholar
 Berger F, De Hertogh B, Bareke E, Pierre M, Gaigneaux A, Depiereux E: PHOENIX: a webinterface for (re)analysis of microarray data. Centr Eur J Biol 2009, 4: 603–618. 10.2478/s1153500900558Google Scholar
 Lewin A, Grieve IC: Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data. BMC Bioinformatics 2006, 7: 426. 10.1186/147121057426View ArticlePubMedPubMed CentralGoogle Scholar
 Ackermann M, Strimmer K: A general modular framework for gene set enrichment analysis. BMC Bioinformatics 2009, 10: 47. 10.1186/147121051047View ArticlePubMedPubMed CentralGoogle Scholar
 Ke Q, Costa M: Hypoxiainducible factor1 (HIF1). Mol Pharmacol 2006, 70: 1469–1480. 10.1124/mol.106.027029View ArticlePubMedGoogle Scholar
 Ruan K, Song G, Ouyang G: Role of hypoxia in the hallmarks of human cancer. J Cell Biochem 2009, 107: 1053–1062. 10.1002/jcb.22214View ArticlePubMedGoogle Scholar
 Semenza GL: HIF1: mediator of physiological and pathophysiological responses to hypoxia. J Appl Physiol 2000, 88: 1474–1480.PubMedGoogle Scholar
 De Hertogh B, De Meulder B, Berger F, Pierre M, Bareke E, Gaigneaux A, Depiereux E: A benchmark for statistical microarray data analysis that preserves actual biological and technical variance. BMC Bioinformatics 2009, 11: 17. 10.1186/147121051117View ArticleGoogle Scholar
 Song S, Black MA: Microarraybased gene set analysis: a comparison of current methods. BMC Bioinformatics 2008, 9: 502. 10.1186/147121059502View ArticlePubMedPubMed CentralGoogle Scholar
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