BIOINFORMATICS ORIGINAL PAPER

Vol.22no.202006,pages2500–2506

doi:10.1093/bioinformatics/btl424 BIOINFORMATICS ORIGINAL PAPER

Gene expression

Group testing for pathway analysis improves comparability

of different microarray datasets

Theodora Manoli1,2,3,Norbert Gretz2,Hermann-Josef Gro¨ne3,Marc Kenzelmann3, Roland Eils1and Benedikt Brors1,?

1Theoretical Bioinformatics,German Cancer Reseach Center,69120Heidelberg,Germany,2Medical Research Center,University Hospital Mannheim,68167Mannheim,Germany and3Cellular and Molecular Pathology, German Cancer Research Center,69120Heidelberg,Germany

Received on March8,2006;revised on July22,2006;accepted on July28,2006

Advance Access publication August7,2006

Associate Editor:David Rocke

ABSTRACT

Motivation:The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However,comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes,e.g.metabolic or signal-ling pathways.This can be achieved by group testing procedures, e.g.over-representation analysis,functional class scoring(FCS),or global tests.

Results:Three public prostate cancer datasets obtained with the same microarray platform(HGU95A/HGU95Av2)were analyzed. Each dataset was subjected to normalization by either variance stabilizing normalization(vsn)or mixed model normalization(MMN). Then,statistical analysis of microarrays was applied to the vsn-normalized data and mixed model analysis to the data normalized by MMN.For multiple testing adjustment the false discovery rate was cal-culated and the threshold was set to0.05.Gene lists from the same method applied to different datasets showed overlaps between42 and52%,while lists from different methods applied to the same dataset had between63and85%of genes in common.A number of six gene lists obtained by the two statistical methods applied to the three data-sets was then subjected to group testing by Fisher’s exact test.Group testing by GSEA and global test was applied to the three datasets,as well.Fisher’s exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different datasets than the GSEA.However,all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer.Moreover,pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single dataset.

Contact:b.brors@dkfz.de

Supplementary Information:Supplementary Figure1and Supplementary Tables1–4are available at Bioinformatics online.1INTRODUCTION

DNA microarrays accelerated dramatically the gene expression pro?ling of cells,and became therefore an important tool in many biomedical applications.In cancer research,microarray exp-eriments are frequently performed for tumour classi?cation and identi?cation of marker genes(van’t Veer et al.,2002;Golub et al.,1999;Giltnane and Rimm,2004).When results obtained by different microarray studies examining the same biological con-ditions,e.g.differential expression between tumour and normal samples,are compared,the lists of differentially expressed genes hardly overlap(Ein-Dor et al.,2005).Moreover,numerous statis-tical methods that are being used for the processing of microarray data exist,and result in dissimilar lists of differentially expressed genes as well(Allison et al.,2006).

A recent addition to the repertoire of analysis methods is group testing,i.e.testing whether prede?ned lists of genes that belong to,e.g.a metabolic pathway,the same cellular function or cellular component are signi?cantly changed as a group in a microarray dataset(Curtis et al.,2005).Three approaches for this task are:(1) an over-representation analysis(ORA),where the genes in the pre-de?ned lists are analyzed to see which categories are represented more than expected by chance(Draghici et al.,2003);(2)a func-tional class scoring(FCS;Pavlidis et al.,2004;Mootha et al.,2003), where the genes are ranked based on the correlation between their expression and the given phenotype and(3)a global test looking for associations between gene expression in prede?ned gene sets and a target variable(Goeman et al.,2004).

Until now,results coming from different datasets and statistical methods have only been compared on the level of lists of differen-tially expressed genes.The goal of this study is to extend the com-parison of different statistical methods and datasets to the level of affected pathways,and to compare the output of three recent group testing methods used for pathway analysis.Three public prostate cancer datasets were subjected to two distinct statistical analyses of differential expression,the Statistical Analysis of Microarrays (SAM;Tusher et al.,2001)and Mixed Model Analysis(MMA; Hsieh et al.,2003;Chu et al.,2002).The six lists of genes obtained from these analyses were then subjected to ORA by Fisher’s exact test(Draghici et al.,2003).Normalized data of the three datasets

?To whom correspondence should be addressed.

ó2006The Author(s)

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(https://www.docsj.com/doc/a718053019.html,/licenses/ by-nc/2.0/uk/)which permits unrestricted non-commercial use,distribution,and reproduction in any medium,provided the original work is properly cited.

were subjected to FCS by GSEA(Mootha et al.,2003;Sweet-Cordero et al.,2005)and to global test(Goeman et al.,2004).

2METHODS

2.1Public dataset collection and pre-processing

Data for this study were downloaded from public websites(Table1)and were pre-processed by software packages included in the R-project (R Development Core Team,2004;Ihaka and Gentleman,1996),Biocon-ductor(Gentleman et al.,2004),and SAS Microarray1.3(SAS Institute Inc, 2004).In all cases raw data were imported from CEL?les.Normalization was carried out using the vsn(Huber et al.,2002)or MMN(Hsieh et al., 2003;Chu et al.,2002)algorithms with default parameters as implemented in the Bioconductor package vsn1.5.0or SAS Microarray1.3,respectively. Data were two-base log-transformed.Vsn-normalized data were sum-marized by the medianpolish method(Tukey,1977).To calculate fold changes for vsn-normalized expression values the robust estimator expehex1Tàhex2TTwas used(Huber et al.,2003).For MMN data,summa-rization of probes was only necessary to calculate fold changes;this was done by calculating the median of each probe-set.Fold changes were then obtained by dividing the two averages.

2.2Statistical analysis

For the identi?cation of differentially expressed genes,SAM(Tusher et al., 2001),as implemented in the siggenes1.2.11Bioconductor package,has been applied on vsn-normalized data and MMA(Hsieh et al.,2003;Chu et al.,2002),as implemented in SAS Microarray1.3,has been applied on MMN-normalized data.In all three datasets the same two classes,tumour versus normal,were compared.For multiple testing adjustment,the false discovery rate(FDR)was calculated,using the algorithm of Storey and Tibshirani(2003)for SAM and the algorithm of Benjamini and Hochberg(1995)for MMA.A threshold of0.05was used.

2.3Group testing for pathway analysis

To identify pathways that are likely to be affected by differential expression three approaches were used;an ORA approach using Fisher’s exact test as described by Draghici et al.(2003),an FCS approach using a modi?ed GSEA as described by Sweet-Cordero et al.(2005)and the global test approach as described by Goeman et al.(2004)and as implemented in the Bioconductor package globaltest3.0.4.We used a total number of 227pathway lists from which132were generated from the KEGG database (Kyoto Encyclopedia of Genes and Genomes,http://www.genome.ad.jp/ kegg/pathway.html)using the Bioconductor annotation package hgu95av2 1.8.4.A number of95pathways was generated manually(M.Kenzelmann).2.4Fisher’s exact test(Draghici et al.,2003)

We consider that there are N single-symbol-annotated genes on the micro-array(replicates were averaged by calculating the mean),which are either signi?cantly differentially expressed(S)or not(F),and either belong to a pre-de?ned pathway list(P)or not(NP),see Table2.If we pick randomly P genes,we would like to estimate the probability of having exactly a genes in S.The p-value of having a genes or fewer in S can be calculated by summing the probabilities of a random list of K genes having1,2,...,a genes in S:

p?1à

X a

i?0

S

i

F

Pài

N

P

e1T

This is a one-sided test in which the P values correspond to over-represented lists of genes.

A review about similar current tools used for group testing on the level of Gene Ontology(GO)terms was given by Khatri and Draghici (2005).

2.5GSEA(Mootha et al.,2003;Sweet-Cordero

et al.,2005)

An earlier version of this approach,called also gene set enrichment analysis (GSEA),has previously been described by Lamb et al.(2003)and Mootha et al.(2003).This procedure was extended by Sweet-Cordero et al.,2005to address the case of multiple gene sets as well as multiple datasets.A re?ne-ment of the GSEA methodology with a broader applicability along several kinds of datasets has been given by Subramanian et al.(2005).We use the basic GSEA procedure as described by Sweet-Cordero et al.(2005),apply-ing a phenotype permutation but no gene permutation.This is based on a maximum deviation statistic of two empirical distribution functions. First,the genes are ranked using the SNR(signal to noise ratio)with respect to their correlation with the phenotype of interest,in our case the comparison of tumour tissue versus normal tissue.The SNR is de?ned as SNR?j(m C–m T)j/(s C+s T),where,m C,m T,s C and s T are the mean expression values and the standard deviations of the control group C and the test group T,respectively.Second,an enrichment measure ES is calculated and assigned to each gene set as following.If L?{g1,...,g N}are the ranked genes,the two empirical cumulative distribution functions are de?ned as

P hiteG?D?iT?

Card?g j i2G

N H

P misseG?D?iT?

Card?g j i=2G

H

,

e2T

where G is the gene set that is to be tested,D the dataset under investigation, N H the number of genes in the gene set,Card?g j i2G the number of genes (cardinality)ranked above the i th gene that are in the gene set(‘hits’)and Card?g j i=2G the number of genes(cardinality)ranked above the i th gene that are not in the gene set(‘miss’).

Table1.Key characteristics of the microarray data used in this study Study Platform Sample

Welsh et al.(Welsh) (Welsh et al.,2001)HGU95A32:normal{8},

tumour{24}

Singh et al.(Singh) (Singh et al.,2001)HGU95Av2102:normal{50},

tumour{52}

Ernst et al.(Ernst) (Ernst et al.,2002)HGU95A26:normal{9},

tumour{17}

All datasets were consisting of Affymetrix oligonucleotide microarrays of the HGU95A/ HGU95Av2series with12,626probe sets.The numbers in curly brackets denote the number of samples in each category.Table2.Gene categorization for group testing with Fisher’s exact test approach

Significant Not significant Sum Genes in P a b P Genes not in P g d NP Sum S F N

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The enrichment score ES is the maximum difference between P hit and P miss,i.e.

ESeG?DT?signeP hiteG?D?iTàP misseG?D?iTT

·max

i j P hiteG?D?iTàP misseG?D?iTj:

e3T

Finally we calculate a nominal p-value to estimate the statistical signi?cance of the enrichment score ES.This is done by permuting the class labels999times to produce reshuf?ed datasets D(p).Then we re-compute ranked lists by calculating the SNR of D(p).After that we calculate the enrichment score of each gene set for the new ranked lists,w p?ESeG?DepTT.The nominal p-value of a gene set is determined by:

p?

Card?w peG?DT>ESeG?DT

p

:e4T2.6Global test(Goeman et al.,2004)

This test investigates whether samples with similar clinical outcomes tend to have similar gene expression patterns.For a signi?cant result,it suf?ces if many genes in the group are correlated with the outcome and not necessarily have similar expression patterns.The model used is

EeY i j bT?hà1at

X m

j?1x ij b j

!

;e5T

where Y i is the outcome of the sample i,h a link function(e.g.the logit function),a an intercept,x ij an element of the n·m data matrix of samples i and genes j,and b j the regression coef?cient for gene j (j?1,...,m).

To test whether there is a predictive effect of the gene expressions on the clinical outcome,the null hypothesis that all regression coef?cients are zero is tested,H0:b1?b2?...?b m?0.The statistic Q used for testing H0in equation(5)can be written as:

Q?

1

2X n

i?1

X n

j?1

R ijeY iàmTeY jàmT;e6T

where m?hà1eaTis the expectation of Y under H0,m2is the second central moment of Y under H0,R?e1/mTXX0the covariance matrix of the gene-expression patterns between the samples where X i(i?1,...,m)the n·1 vector of the gene expressions of gene i,andeYàmTeYàmT0the covariance matrix of the clinical outcomes of the samples.

3RESULTS

Three public datasets(Table1)were used to compare different statistical methods applied to these datasets on the level of apparently affected pathways.All three studies used the HGU95A(Welsh and Ernst)or HGU95Av2(Singh)microarray platform from Affymetrix,and were consisting of the same two sample classes,normal prostate and prostate cancer.Each dataset was subjected to normalization by either vsn or MMN.Then,SAM was applied to the vsn-normalized data and MMA to the data normalized by https://www.docsj.com/doc/a718053019.html,parisons were made between prostate tumour and normal prostate tissue to detect genes that are signi?-cantly differentially expressed under these conditions.For multiple testing adjustment the FDR was calculated using the algorithm of Storey and Tibshirani for SAM and the algorithm of Benjamini and Hochberg for MMA.A threshold of0.05was used to assign differentially expressed genes.All differentially expressed genes with their signi?cance values and fold-changes are to be found in Supplementary Table1.The numbers of these genes and their overlap with those obtained from other statistical methods and datasets are shown by Venn diagrams(Fig.1).Figure1a and b show the numbers of genes obtained by SAM or MMA applied to three datasets.The intersections of all three datasets contains 146or132genes,respectively,which represent only52or48%of the smallest sets of signi?cantly differentially expressed genes, which are from the data of https://www.docsj.com/doc/a718053019.html,paring the overlaps between SAM or MMA genes for the three datasets(Fig.1c–e),the dataset of Welsh shows the highest rate of common genes of84%,while the datasets of Singh and Ernst show rates of63or65%,respec-tively.Finally,the common differentially expressed genes between SAM and MMA were compared with each other (Fig.1f).Here,the overall overlap of76genes represents just the42%of the smallest set of signi?cantly differentially expressed genes(dataset of Ernst).

Although the overlaps between different methods or between different datasets appear to be rather small,these are better com-pared by concordance plots that allow us to examine how big these discrepancies really are(Fig.2,Supplementary Fig.1).These plots show the numbers of common genes between signi?cantly differ-entially expressed genes found by one analysis(y-axis)along the ranked list of all examined genes according to another analysis (x-axis).The area under the curve(AUC)determines the extent of concordance between the two analyses being compared.Hence, curves that are fast steeply increasing indicate a high

concordance Fig.1.Venn diagrams representing the numbers of significantly differen-tially expressed genes and the overlaps of sets obtained from two different statistical methods and three datasets.The upper magenta and cyan diagrams show the overlaps of SAM and MMA genes between the three datasets, respectively.The middle Venn diagrams show the overlaps between SAM and MMA genes for the three datasets separately.The bottom diagram shows the overlaps between the intersections of the middle row.

T.Manoli et al. 2502

of the genes presented on the y -axis with the second analysis pre-sented on the x -axis (e.g.Fig.2i),i.e.the genes on the x -axis get also high ranks in the second analysis.On the contrary,curves tending to the 45 diagonal line from the lower left corner to the upper right corner of the plot denote no similarity between the results of the two analyses (Fig.2h).The ?rst six plots of both Figure 2and Supplementary Figure 1are comparisons between different analyses performed on the three data sets using the same statistical method (a–c:SAM and d–f:MMA).The dataset of Ernst shows the highest concordance of differentially expressed genes (large AUCs in Fig.2c and f).The Welsh and Singh datasets give concordance plots that are close to the 45 diagonal line,and this denotes that the differentially expressed genes of these data sets show a low concordance along ranked genes of other datasets (Fig.2a,b,d and e).In contrast,the concordance between the two statistical methods for same data sets (Fig.2g–i,Supplementary Fig.1g–i)was higher.SAM and MMA showed very good concor-dance rates for the dataset of Ernst,where all signi?cantly differ-entially expressed genes found with one method were among the ?rst 3000top rated genes with the other method.For the dataset of Welsh,>90%of the signi?cantly differentially expressed genes found with one method were among the top 4000ranked genes with the other method.The dataset of Singh showed,however,much lower concordance.This is presented by the short and not steeply ascending ?rst phase of the concordance curve (Fig.2h).

As the next step,three approaches of group testing for pathway analysis have been applied.Fisher’s exact test was applied to the six lists of signi?cantly differentially expressed genes coming from three datasets and two statistical methods.GSEA and global test were applied to the three vsn-normalized datasets.For Fisher’s exact test and GSEA,a threshold of 0.05for the p -values was set to identify signi?cantly regulated pathways.Because almost all examined pathways with the global test approach were assigned a p -value of <0.05we took the top 20high-rated pathways for further investigation.The affected pathways found by each pathway analysis method and their p -values are presented in Supplementary Tables 2–4.Supplementary Table 2presents the results obtained by Fisher’s exact test,and is a dual comparison of the outputs of pathway analyses applied to SAM and MMA gene lists for the same dataset and pathway analysis method.Supplementary Tables 3and 4present the results obtained by GSEA and the global test for each data set,respectively.The numbers of affected gene groups and

(a)

020004000600080001000012000

5001000150020002500

Number of rated SAM genes for the dataset Singh et al 2579 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t W e l s h e t a l

(b)

02000400060008000100001200005001000150020002500

3000

Number of rated SAM genes for the dataset Ernst et al 2948 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t S i n g h e t a l

(c)

020004000600080001000012000

050100150200

250Number of rated SAM genes for the dataset Welsh et al

280 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t E r n s t e t a l

(d)

020004000600080001000012000

500100015002000

Number of rated MMA genes for the dataset Singh et al 2184 s i g n i f i c a n t M M A g e n e s f o r t h e d a t a s e t W e l s h e t a l

(e)

02000400060008000100001200005001000

1500

Number of rated MMA genes for the dataset Ernst et al 1752 s i g n i f i c a n t M M A g e n e s f o r t h e d a t a s e t S i n g h e t a l

(f)

020004000600080001000012000

050100150200

250Number of rated MMA genes for the dataset Welsh et al

277 s i g n i f i c a n t M M A g e n e s f o r t h e d a t a s e t E r n s t e t a l

(g)

020004000600080001000012000

5001000150020002500

Number of rated MMA genes for the dataset Welsh et al 2579 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t W e l s h e t a l

(h)

0200040006000800010000120000500100015002000

2500

3000Number of rated MMA genes for the dataset Singh et al 2948 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t S i n g h e t a l

(i)

020004000600080001000012000

050100150200

250Number of rated MMA genes for the dataset Ernst et al

280 s i g n i f i c a n t S A M g e n e s f o r t h e d a t a s e t E r n s t e t a l

Fig.2.Concordance plots presenting the numbers of common genes between significantly differentially expressed genes found by one analysis (y -axis)along the ranked list of all examined genes according to another analysis (x -axis).(a –c)SAM analyses of different datasets are compared;(d –f)MMA analyses of different datasets;(g –i)SAM and MMA analyses of same datasets.

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their overlaps for each group testing method are presented in Table 3.

Figure 3summarizes the occurrences of the signi?cantly regu-lated pathways found with Fisher’s exact test (Fig.3a),GSEA (Fig.3b),and global test (Fig.3c),respectively.A total number of six Fisher’s exact test analyses have been applied on lists of signi?cantly differentially expressed genes found with either SAM or MMA for the three public datasets of Welsh,Singh and Ernst,while for each GSEA and global test only three analyses have been applied directly to normalized data of these data sets,respec-tively.By Fisher’s exact testing (Fig.3a)one pathway,‘androgen and prostate cancer’,was found to be signi?cantly regulated in all six analyses.Two pathways,‘ribosome’and ‘glutathione metabo-lism’,were found to be signi?cantly affected in ?ve analyses,six pathways in four analyses,four pathways in three analyses,17pathways in two analyses and 17pathways in only one analysis.GSEA (Fig.3b)gave no affected pathways in more than one anal-ysis.A total number of 23pathways has been obtained by all analyses.By global testing eight pathways have been obtained by two analyses,while 44pathways have been obtained by only one analysis.

Finally,the coincidence of affected pathways between the three group testing methods,Fisher’s exact test,GSEA and global test is presented (Fig.4).For this purpose,pathways found to be signi?-cantly regulated in at least four of the six Fisher’s exact test analyses and two of the three GSEA or global test analyses,were used.Three pathways,‘androgen and prostate cancer’,‘pyrimidine metabolism’and ‘nucleotide metabolism’,were found to be affected by two group testing methods.A number of 11pathways was found by only one method.

4DISCUSSION

In this study we examined the discrepancies of different statistical methods and datasets on the level of affected pathways,and com-pared the output of three recent group testing methods used for pathway analysis.Data of three public prostate cancer datasets were pre-processed by vsn or MMN,and were then subjected to SAM or MMA,respectively.The FDR was calculated for multiple testing adjustment,and the threshold was set to 0.05to assign differentially expressed genes.Three approaches of group testing for pathway analysis,Fisher’s exact test,GSEA and global test,were applied to the three public datasets.

Comparing the overlaps of genes obtained from the two statistical methods and three datasets,we conclude that both different statistical methods and different datasets examining the same Table 3.Numbers of affected pathways found with three statistical approaches for group testing (Fisher’s exact test,GSEA and global test),obtained from the three datasets of Welsh,Singh and Ernst

Fisher’s exact test GSEA

Global test

SAM MMA

Overlap Welsh

191914(74%)1120Singh 212111(52%)620Ernst 9

12

6(67%)

6

20

Overlap

3(33%)

2(17%)

0(0%)

0(0%)WelshSAM WelshMMA SinghSAM SinghMMA ErnstSAM ErnstMMA

Ascorbate and aldarate metabolism (00053)

Gap junction proteins connexins

Signal transduction in cancer Fatty acid metabolism (00071)Tyrosine metabolism (00350)Camp calcium signalling Pantothenate and CoA biosynthesis (00770)

Glycolysis and gluconeogenesis

Selenoamino acid metabolism (00450)

Krebs tca cycle

Glycosphingolipid metabolism (00600)

ATP synthesis (00193)

Trna synthetases Alzheimer’s disease (05010)

G protein signaling

Glycogen metabolism Glycine, serine and threonine metabolism (00260)

BetaAlanine metabolism (00410)

Purine metabolism (00230)

Reductive carboxylate cycle (CO2 fixation) (00720)

Stress and toxicity

Proteasome (03050)Translation factors Oxidative phosphorylation (00190)

Electron transport chain

Fatty acid biosynthesis (path 2) (00062)

Stem cell

AminoacyltRNA biosynthesis (00970)

Bile acid biosynthesis (00120)

Protein export (03060)

Fructose and mannose metabolism (00051)

Osteogenesis

Aminosugars metabolism (00530)

Pentose phosphate pathway (00030)Propanoate metabolism (00640)Valine, leucine and isoleucine degradation (00280)

Urea cycle and metabolism of amino groups (00220)Prion disease (05060)

Nucleotide metabolism Pyrimidine metabolism (00240)

Hypoxia

Pentose phosphate pathway

Arginine and proline metabolism (00330)

Cholera Infection (05110)

Glutathione metabolism (00480)

Ribosome (03010)

Androgen and prostate cancer

Welsh Singh Ernst

Stem cell

Ovarian infertility genes

Sulfur metabolism (00920)MAPK signaling pathway (04010)

Tryptophan metabolism (00380)Histidine metabolism (00340)P53 signalling

Pentose phosphate pathway (00030)

Cell cycle (04110)

Androgen and estrogen metabolism (00150)

One carbon pool by folate (00670)

Vitamin B6 metabolism (00750)Cancer drug resistance

Glycine, serine and threonine metabolism (00260)

Fatty acid biosynthesis (path 2) (00062)

G protein receptor coupled signalling C21Steroid hormone metabolism (00140)

Hypoxia

OGlycans biosynthesis (00512)

Pentose and glucuronate interconversions (00040)

Monoamine gpcrs

Phospholipid degradation (00580)

C21 steroid hormone metabolism Insulin metabolism

1,1,1Trichloro2,2bis(4chlorophenyl)ethane (DDT) degradation (00351)

Androgen and estrogen metabolism

Fatty acid biosynthesis (path 1) (00061)

Prostaglandin and leukotriene metabolism (00590)

Eicosanoid synthesis

Alkaloid biosynthesis II (00960)

Neuroactive ligandreceptor interaction (04080)

Adhesion extracellular matrix

Glycolysis / Gluconeogenesis (00010)

Integrinmediated cell adhesion (04510)Starch and sucrose metabolism (00500)Arginine and proline metabolism (00330)Wnt signalling

Dendritic and antigen presenting

Galactose metabolism (00052)Pantothenate and CoA biosynthesis (00770)

Valine, leucine and isoleucine degradation (00280)

Tyrosine metabolism (00350)

Gpcrs class a rhodopsin like Estrogen and breast cancer Nucleotide metabolism Phenylalanine metabolism (00360)

Stress and toxicity

Pyrimidine metabolism (00240)

Purine metabolism (00230)Androgen and prostate cancer

Gap junction proteins connexins

Fructose and mannose metabolism (00051)

Welsh Singh Ernst

Neurodegenerative Disorders (01510)

Pyrimidine metabolism (00240)

Translation factors

Electron transport chain

Ethylbenzene degradation (00642)

Purine metabolism (00230)

Cholera Infection (05110)Glycosphingolipid metabolism (00600)

Ribosome (03010)

Fatty acid synthesis Glutathione metabolism (00480)

Nucleotide metabolism

Valine, leucine and isoleucine degradation (00280)

Fructose and mannose metabolism (00051)

Androgen and prostate cancer

Tyrosine metabolism (00350)Pentose phosphate pathway Urea cycle and metabolism of amino groups (00220)

Glycine, serine and threonine metabolism (00260)Chondroitin / Heparan sulfate biosynthesis (00532)Arginine and proline metabolism (00330)

Prion disease (05060)

Glycogen metabolism (a)

(b)

(c)

Fig.3.Coincidence of affected pathways found with (a)Fisher’s exact test,(b)GSEA or (c)the global test from six different lists of genes.Each colour box designates that the corresponding pathway is significantly regulated in the corresponding list,coming from a specific dataset (Welsh,Singh or Ernst)and,for the case of Fisher’s exact test,a specific statistical analysis (SAM or MMA).Significantly regulated pathways in 6lists of genes are demonstrated in magenta,significantly regulated pathways in 5,4,3,2or 1lists of genes are shown in red,blue,yellow or grey,respectively.The numbers in parenthesis following each pathway name denote the KEGG-ID (http://www.genome.ad.jp/kegg/pathway.html)for pathways coming from the KEGG pathway database.Pathways without number were complied and curated manually (M.Kenzelmann).

T.Manoli et al.

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biological condition (in our case prostate cancer)lead to signi?cant discrepancies.Different datasets showed higher dissimilarities in the obtained signi?cantly differentially expressed genes than dif-ferent statistical methods did.This observation was con?rmed by investigating the concordance of a list of differentially expressed genes found by one analysis along the ranked list of all examined genes in another analysis.In general,genes obtained from the data-set of Ernst showed a higher concordance because of the smaller numbers of differentially expressed genes found in this dataset.These genes are expected to be the highly ranked among prostate cancer genes.Genes obtained from the dataset of Singh showed,in contrary,the worst concordance.

The problem of having a small overlap between gene sets coming from different datasets has been also pointed out by Ein-Dor et al .(2005).In this study one single dataset was analyzed by a single method and it was shown that the resulting set of genes is strongly in?uenced by the subset of patients used for gene selec-tion.An explanation would be that there is a high number of dif-ferentially expressed genes,many of which are highly correlated.Which of them are chosen as the top-ranked genes is more or less arbitrary and depends on the analysis method or the set of samples from which the genes were inferred.One would expect,however,that these discrepancies are less pronounced when the genes are mapped to biological pathways.

Comparing the results of the three group testing methods (Fig.3),Fisher’s exact test followed by global test showed the highest over-lap between affected pathways inferred from different datasets.‘Androgen and prostate cancer’was the pathway found to be appar-ently affected in all six Fisher’s exact test analyses and in two global test analyses,and this supports the validity of these results.The GSEA gave no overlaps,which can be partly explained by the smaller numbers of apparently affected pathways gained by this method.However,most of the obtained pathways were also high rated pathways by the other two methods.As expected,results of group testing applied on different datasets were more discrepant

than results of group testing applied on lists of differentially expressed genes obtained by different statistical methods (Fig.3a).Examining the coincidence of affected pathways between the three group testing methods,Fisher’s exact test,GSEA and global test (Fig.4),we observed that different methods gave mainly diverging results.Three pathways,‘androgen and prostate cancer’,‘pyrimidine metabolism’and ‘nucleotide metabolism’,were found to be affected by two methods (Fisher’s exact test and global test).GSEA gave,as mentioned above,no overlaps at all.

Many of the pathways found to be affected by differential exp-ression are already known to be involved in prostate cancer patho-genesis.‘Androgen and prostate cancer’has a clear connection to prostate cancer as they were manually created from literature information on these diseases (M.Kenzelmann).Pathways from the group of nucleotide metabolism,amino acid metabolism,carbohydrate metabolism,as well as pathways like ‘ribosome’are characteristic for fast proliferating tumour cells.‘Glutathione metabolism’plays an important role in defense against reactive

oxygen species,xenobiotics and heavy metals (Mendoza-Co

′zatl et al .,2005),while glutathione S-transferase pi (GSTP1)is a characteristically down-regulated marker gene in prostate cancer (Nakayama et al .,2004).‘Gap junction proteins connexins’marks the increased cell communication in cancer for processes like apoptosis,differentiation and tissue homeostasis,and for activa-tion of calcium and MAPK signalling pathways (http://www.genome.ad.jp/kegg/pathway.html).‘Hypoxia’can be related to the increased intracellular redox state of prostate cancer cells,associated to the high oxidizing power of the fatty acid synthesis (FAS)pathway,that yields expression of hypoxia-regulated genes (Hochachka et al .,2002).An increased intracellular redox state yields also an increase in the expression of the intrinsic prion protein (PrPc),suggesting the possible participation of PrPc in antioxidative defense (Sauer et al .,1999)and explaining the high occurrence of ‘prion disease’in our results.

Even a pathway as remote as ‘Cholera-infection’yields some interesting results,as it contains genes of the adenylate cyclase signaling,phospholipase C and other factors that are also changed in tumour cells.

5CONCLUSION

In conclusion,group testing applied to different datasets yields interesting common results,diminishing the large discrepancies observed in direct comparisons of lists of differentially expressed genes obtained not only from different datasets,but also by different statistical methods.Moreover,the multiple microarray analyses performed in this study result in discriminative pathway regulation signatures that are found and validated by different laboratories and microarray analysis methods.Pathways obtained by these anal-yses are likely to be more robust than those generated by a single analysis on a single dataset.

The three group testing methods used in this study differed in their results.Fisher’s exact test showed the most consistent results with respect to the concordance between analyses on gene lists obtained by different methods from different datasets.Global test showed to a lesser extend consistent results between analyses applied to different datasets,while GSEA showed no overlaps between results coming from different datasets.All group

testing

Fig.4.Coincidence of affected pathways along the three different pathway analysis methods,Fisher’s exact test,GSEA and global test.Pathways found to be affected in at least four of six Fisher’s exact test analyses,or two of three GSEA or global test analyses,are presented.Yellow and grey boxes refer to significantly regulated pathways found with two or one pathway analysis methods,respectively.The numbers in parenthesis following each pathway name denote the KEGG-ID (http://www.genome.ad.jp/kegg/pathway).

Group testing for pathway analysis

2505

methods gave pathways that had already been described to be involved in the pathogenesis of prostate cancer. ACKNOWLEDGEMENTS

The authors acknowledge financial support by the BMBF (BioFuture;0311880A)and the National Genome Research Network(01GR0450).T.M.receives a stipend from the DFG Graduiertenkolleg886.Funding to pay the Open Access publication charges was provided by the German Federal Ministry of Research and Education(grant01GR0450).

Conflict of Interest:none declared.

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