Research Article Microarray-Based RNA Profiling of Breast...

Research ArticleMicroarray-Based RNA Profiling of Breast Cancer Batch EffectRemoval Improves Cross-Platform Consistency

Martin J Larsen12 Mads Thomassen12 Qihua Tan23

Kristina P Soslashrensen12 and Torben A Kruse12

1 Department of Clinical Genetics Odense University Hospital Sdr Boulevard 29 5000 Odense C Denmark2Human Genetics Institute of Clinical Research University of Southern Denmark Winsloslashwvej 19 5000 Odense C Denmark3 Epidemiology Biostatistics and Biodemography Institute of Public Health University of Southern DenmarkJB Winsloslashws Vej 9B 5000 Odense C Denmark

Correspondence should be addressed to Martin J Larsen martinlarsenrsyddk

Received 22 January 2014 Revised 17 April 2014 Accepted 9 June 2014 Published 3 July 2014

Academic Editor Federico Ambrogi

Copyright copy 2014 Martin J Larsen et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Microarray is a powerful technique used extensively for gene expression analysis Different technologies are available but lackof standardization makes it challenging to compare and integrate data Furthermore batch-related biases within datasets arecommon but often not tackled We have analyzed the same 234 breast cancers on two different microarray platforms One datasetcontained known batch-effects associated with the fabrication procedure used The aim was to assess the significance of correctingfor systematic batch-effects when integrating data fromdifferent platformsWehere demonstrate the importance of detecting batch-effects and how tools such as ComBat can be used to successfully overcome such systematic variations in order to unmask essentialbiological signals Batch adjustment was found to be particularly valuable in the detection of more delicate differences in geneexpression Furthermore our results show that prober adjustment is essential for integration of gene expression data obtained frommultiple sources We show that high-variance genes are highly reproducibly expressed across platforms making them particularlywell suited as biomarkers and for building gene signatures exemplified by prediction of estrogen-receptor status and molecularsubtypes In conclusion the study emphasizes the importance of utilizing proper batch adjustment methods when integrating dataacross different batches and platforms

1 Introduction

The microarray technology has been extensively used forgenome-wide gene expression analysis Microarray is a well-established cost-effective high-throughput technology ableto simultaneously measure the expression levels of thousandsof genes and hereby offers an efficient way to generatea snapshot of the entire transcriptome Several differentmicroarray platforms are commercially available differingwith respect to the fabrication methodologies and lengthof the oligonucleotide probes Some manufactures use pho-tolithography (light directed) methods while others rely onink-jet technology for in situ synthesis of oligonucleotidesonto a solid array surface The probes lengths typically varyfrom 25-mer to 60-mer In addition to these commerciallyavailable platforms spotted arrays with either cDNA or

synthesized oligonucleotide probes deposited onto the arraysurface comprise a cost effective alternative Spotted arrayscan be either commercially manufactured or in-house man-ufactured Because of their relatively low cost and flexibilitythe spotted microarray technology has been widely used inmany academic laboratories

The workflow of a microarray gene expression analysesconsists of multiple steps First RNA is extracted amplifiedand either biotin-labeled or labeled with fluorescent dyes(Cy3Cy5) depending on the platform Subsequently thelabeled RNA is hybridized to the array typically overnightAfter hybridization the arrays are washed and scannedFinally the scanned images are quantified by specializedsoftware All abovementioned steps comprise a potentialsource of systematic variation In addition there are multipletechnical issues that must be controlled in the fabrication and

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014 Article ID 651751 11 pageshttpdxdoiorg1011552014651751

2 BioMed Research International

use of spotted arrays Spotted arrays are often manufacturedin relatively small batches under semistandardized condi-tions This often introduces systematic differences betweenthe measurements of different batches often termed ldquobatcheffectsrdquo [1] Furthermore lack of standardization makes itchallenging to compare and integrate data from the differentmicroarray technologies available as it introduced platform-dependent systematic variation Several studies have demon-strated that pooling data derived from different platforms is acomplex task with numerous pitfalls [2 3] Additional factorsare known to introduce systematic variation including whenthe analysis is conducted at different sites by alternatingpersonnel in different experiments or simple due to day-to-day variation

Combining data from different experimentsplatformbatches without reducing possible systematic variant cangive rise to misleading results that can be strong enough tomask or even confound true biological signals and lead tomisinterpretation of the data For unmasking it is necessaryto identify and remove the batch effects before proceedingSeveral approaches have been developed for removal ofsystematic batch effects Single value decomposition andprincipal component analysis (PCA) have been used bysubtracting the component representing the batch effect fromthe data [4] Distance-weighted discrimination (DWD) usesa modified version of the support vector machine (SVM)algorithm to correct for batch effect [5] ComBat proposedby Johnson et al applies an empirical Bayes approach bypooling information across genes and shrinks the batch effectparameter toward the overall mean of the batch estimatesacross genes [6] Other commonly used batch effect methodsinclude mean centering standardization and ratio-basedmethods [7]

Due to its extensive use thousands of gene expres-sion microarray datasets have been deposited to publicdatabases making these repositories valuable data sourcesMicroarray gene expression data has been widely used inmedical decision-making research For clinical use of array-based diagnostics significant numbers of patient samplesare needed for training and evaluation Therefore reusepooling and integration of multiple microarray dataset areattractive approaches Because of the heterogeneous natureof microarray datasets it is important to address the issue ofcross-platform variation as demonstrated in several studieswhen combining different dataset from different platformsanalyzed in different laboratories for example medicaldecision-making purposes [7ndash9] Furthermore as the RNAsequencing technology develops and matures another levelof batch effects needs to be taken into consideration whendatasets of different origin are to be pooled

In the current study we analyzed the same 234 breastcancers on two different microarray platforms our in-house29K array platform and Agilent SurePrint G3 microarrayplatform The 29K dataset contained known batch effectsassociatedwith the fabrication procedure Using these uniquedatasets the aim was to assess the significance of removingsystematic batch effects existing within a dataset and whenintegrating datasets of different platforms

2 Materials and Methods

21 Ethics Statement The study was carried out as a retro-spective register study and in accordance with the HelsinkiDeclaration The study has been approved by The NationalCommittee on Health Research Ethics of Denmark (S-VF-20020142) waiving the requirement for informed consent forthe study

22 PatientMaterial This study was performed on a series of243 frozen primary breast tumors obtained from the biobanksof the Department of Pathology Odense University Hospitaland the Danish Breast Cancer Cooperative Group (DBCG)The tumor samples comprise a subset of a larger series of253 tumor samples previously reported [10 11] Breast tumortissues from 120 patients with germline mutations in BRCA1(119899 = 33) or BRCA2 (119899 = 22) or familial non-BRCA12 caseswith no detectable germline mutation in BRCA1 or BRCA2(119899 = 65) were included in the study In addition 123 primarytumor samples from sporadic breast cancers were includedin the study In order to determine the tumor cell contentslides of the frozen tumor biopsies were haematoxylin-eosin-stained and examined by a pathologist at the Departmentof Pathology Odense University Hospital Samples analyzedin the present study contained at least 50 tumor cellsImmunohistochemistry determined estrogen receptor (ER)expression data was obtained from the Danish Breast Can-cer Cooperative Group (DBCG) Gene expression analyseswere performed using two different microarray platformsthe in-house manufactured 29K oligonucleotide microarrayplatform and Agilent SurePrint G3 platform

23 Microarray Fabrication For manufacturing of the in-house spotted arrays a human oligonucleotide library con-sisting of 28919 DNA oligonucleotide probes (60-mer) waspurchased from Compugen-Sigma-Genosys (The Wood-lands) The oligonucleotide probes were solubilized in150mM sodium phosphate buffer (pH 85) to a final concen-tration of 20 pmol120583L and spotted onto CodeLink HD (Sur-Modics) activated glass slides by a high-precision spottingrobot (Virtek ChipWriter Pro ESI) The slides have an activepolymer coating of amine-reactive groups permitting the 51015840-amino modified DNA oligos to covalently attach under highrelative humidity The microarray fabrication was carriedout in a controlled environment at 38 humidity and thetemperature was held constant at 23∘C SMP 25 stealth pins(TeleChem International) was used to deposit the oligosonto surface of the slides Up to 85 arrays were spotted perbatch After printing slides were incubated overnight at 70humidity and blocked as recommended by the manufacturer

24 Gene Expression Analysis Total RNAwas extracted fromfreshly frozen tumor tissue RNA extraction was carried outusing Trizol Reagent (Invitrogen) followed by RNeasy MicroKit (Qiagen) including DNase treatment RNA concentrationwas determined using aNanoDrop Spectrophotometer (Nan-oDrop Technologies) and the quality assessed by the Agilent2100 Bioanalyzer using Agilent RNA 6000 Nano Kit (Agilent

BioMed Research International 3

Technologies) RNA integrity numbers (RIN) were calcu-lated and RNA was amplified and labeled using the AminoAllyl MessageAmp II aRNA Amplification Kit (Ambion)according to themanufacturerrsquos protocol startingwith 750 ngof total RNA Amplified aRNA from the tumor sampleswere labeled with Cy5 Universal Human Reference RNA(Stratagene) was labeledwithCy3 and used as reference RNADye incorporation efficiency was measured by NanoDropSpectrophotometer Labeled sample aRNA correspondingto 200 pmol Cy5 and reference aRNA 130 pmol Cy3 werehybridized to the in-house spotted arrays whereas 20 pmolCy5 sample aRNA and 20 pmol Cy3 reference aRNA wereused for hybridization to Agilent SurePrint G3 Human GE8 times 60KMicroarrays (Agilent Technologies) Fragmentationhybridization and washing for both spotted arrays and Agi-lent arrays were carried out using the Agilent Gene Expres-sion Hybridization Kit and Gene Expression Wash BufferKit (Agilent Technologies) according to the manufacturerrsquosprotocol in a low ozone environment Subsequently thearrays were scanned using an Agilent G2565CA Microarrayscanner (Agilent Technologies) Microarray data have beendeposited into the Gene Expression Omnibus (GSE54275)

25 Data Preprocessing The scanned images of the spottedarrays were quantified by Gene Pix Pro 60 (MolecularDevices) whereas images fromAgilent arrays were processedby Agilent Feature Extraction Software 10731 (Agilent Tech-nologies) In the subsequent data preprocessing raw intensitydata from the spotted arrays and Agilent arrays were treatedequally Raw intensity data were background corrected usingnormexpmethod and bad quality features flagged during fea-ture extraction were removed from further analysis [12] Datawere then within-array normalized by loess normalizationmethod and quantile normalization method was used forbetween-array normalization [13 14] The normalized valueswere used to calculate log

2transformed Cy5Cy3 ratios Data

preprocessing was performed using the R package limma[15 16] Replicate probes were collapsed by calculating themedianMissing expression valueswere imputed by k-nearestneighbors averaging (119896 = 10) The Agilent dataset contained0012 missing data points and the 29K dataset contained1144 missing data points

26 Reannotation of Probe Sets To obtain the overlappinggene set measured on both platforms and to perform anunbiased cross-platform comparison reannotation of theprobes was performed For both the platforms the probeswere reannotated to gene symbols by Agilent eArray tool(httpearraychemagilentcomearray) using the originalprobe sequences provided by the manufacturer Based on theoverlapping gene set a data set was created for each platformIn cases of multiple probes per gene symbol only the probewith the maximum mean intensity (based on Cy5 intensityvalues) was kept

27 Systematic Bias Adjustments The ComBat method wasused for adjustment of the batch effects observed across the

different spotting batches and to adjust for systematic andtechnical differences between the two platforms [6]

28 Unsupervised Methods Principal component analysis(PCA) plots were generated using data where the expressionlevel of each gene had been standardized to zero mean andunit variance by Qlucore Omics Explorer 23 (Qlucore)

29 Correlation and Agreement Analysis To assess the cor-relation between sample pairs and between gene pairs fromthe two platforms we calculated Pearsonrsquos correlation coeffi-cients To evaluate cross-platform reproducibility after pool-ing the two datasets the intraclass correlation coefficientsICC(11) were calculated Wilcoxon rank sum test was usedwhen comparing the correlation coefficients

210 Detection of Differential Expressed Genes Differentialexpressed gene analysis was carried out using significanceanalysis of microarrays (SAM) method using the implemen-tation found in the R package samr

211 Prediction of Estrogen Receptor Status ER status waspredicted by the gene expression data using the probestargeting the ESR1 transcript For each platform the opti-mal sensitivity-specificity cutoff was determined based onreceiver operating characteristic (ROC) curves performedby the R package pROC [17] The statistical significance wasassessed by Fisherrsquos exact statistic

212 Molecular Subtype Classification The tumors were clas-sified into five intrinsic molecular subtypes using the 50-gene subtype classifier (PAM50) described by Parker et al[18] Distances to each of the subtype centroids defined bythe PAM50 classifier were calculated using Spearmanrsquos rankcorrelation hereby the subtype classification was assignedbased on the nearest of the centroids All 50 genes comprisingPAM50 could be mapped to the Agilent platform whereas4950 genes were found on the spotted platform Subtypeprediction was performed using the R package genefu [19]

Cramerrsquos 119881 coefficient was used as a measure of therelative strength of the associations ranging from 0 to 1(perfect association) using the implementation found in theR package vcd [20]

3 Results and Discussion

31 Experimental Setup The aim of the present study wasto assess the significance of removing systematic batcheffects existing within a dataset when integrating datasets ofdifferent platforms and perform a systematic cross-platformcomparison of the 29K gene expression microarray platformand Agilent SurePrint G3 Human GE 8 times 60K Microarrays[21] A series of 243 primary breast tumors (33 BRCA1 22BRCA2 65 non-BRCA12 and 123 sporadic)were analyzed onboth the 29K microarrays and Agilent microarrays The geneexpression analyses were conducted as two color analyseswith a common reference sample cohybridized to all arraysWe sought to control other possible sources of variations


19191

Agilent SurePrint G3

22171

42706 probes

29K array platformSigma-Compugene oligo set

28919 probes

18424

7673747

Human GE 8 times 60K

Figure 1 Venn-diagram illustrating the distribution of shared genetargets among the Agilent SurePrint G3 platform and the 29K arrayplatform 18424 gene symbols were found to be represented on bothplatforms

that could influence the comparison and introduce additionalnonplatform dependent differences Thus identical RNAamplificationlabeling method and hybridizationwashingconditions were used for both analyses To avoid that day-to-day or batch-to-batch variation influenced the biologicalinterpretation of the results of the RNA extraction theRNA amplification steps and array hybridizations steps wereperformed in randomized sample orders To provide themostup-to-date annotations we reannotated all probe sequencesand mapped them to gene symbols To enable comparisonof the two platforms we created data subsets with only theshared gene set In total 22171 and 19191 unique gene symbolswere represented on the Agilent arrays and spotted arraysrespectively of which 18424 gene symbols are available onboth arrays (Figure 1)Mapping to EntrezGene IDs identifiedonly 16673 overlapping Entrez Gene IDsThus gene symbolswere used for generation of the data subsets

32 Uncovering and Eliminating Within-Experiment BatchEffects The 29K arrays utilized in the present study weremanufactured in 6 different batches (Table 1) Visualizationof the 29K dataset by principal component analysis (PCA)revealed a high level of interbatch variations in the geneexpression profiles (Figure 2(a)) To adjust for the batch-to-batch variations observed in the spotted dataset the ComBatmethod proposed by Johnson et al [6] was applied ComBatapplies an empirical Bayes approach by pooling informationacross genes and shrinking the batch effect parameter towardthe overall mean of the batch estimates across genes Thismethod has been claimed to be especially robust whenhandling multiple batches and batches of small samples sizes(lt25) and has recently been shown to outperform othercommonly used batch-adjustment methods [22]

After intraplatform ComBat batch adjustment the sam-ples fromdifferent batcheswere now intermingled indicatingthat the ComBat method was successful in eliminating thebatch effects (Figure 2(b)) To test if the preserved variation

Table 1 Overview of fabrication batches of 29K arrays

Batch Number of arraysBatch 03 46Batch 05 8Batch 06 67Batch 07 12Batch 08 50Batch 09 60

243

was related to true biological differences across tumor sam-ples molecular subtypes were classified according to PAM50intrinsicmolecular breast cancer subtypes Numerous studieshave shown that the main signals in gene expression datasetsof breast cancers are associatedwith the five clinically relevantsubtypes termed intrinsic molecular breast cancer subtypesbasal-like HER2-enriched luminal A (lumA) and luminalB (lumB) [8 18 23ndash26] In the 29K dataset prior to batchadjustments no subgroupings related to these molecularsubtypes were apparent After ComBat adjustment themolecular breast cancer subtypes were now clearly the maincontributor to the overall variation (Figures 2(c) and 2(d))This emphasizes the importance of distributing samples fromdifferent sampletreatment groups across batches otherwiseadjustment for batch-to-batch variation would not have beenpossible

For a more quantitative evaluation on the effect of theadjustment of the 29K dataset correlations between samplepairs and gene pairs from the two platforms were assessedby Personrsquos correlation coefficient prior to and after thebatch adjustment (Figures 3(a) and 3(b)) Personrsquos correlationprovides a measure of the strength and the linear relationshipbetween two variables even though they are notmeasured onthe same quantitative scaleThe pair-wise sample correlationsbetween the unadjusted 29K dataset and the Agilent datasetranged from 04 to 075 (median = 058) Adjusting the 29Kdataset for batch effects introduced by the manufacturingprocedure using ComBat as described above significantlyimproved the sample correlations (median = 062 119875 = 29119890 minus11) indicating that the overall gene expression patterns hadbecomemore alike Similarly correlations between gene pairsacross all samples were calculated (Figure 3(b)) Correlationsbetween the Agilent dataset and the unadjusted 29K datasetranged from minus04 up to 098 (median = 040) A significantimprovement was gained when adjusting for intraplatformbatch-to-batch differences introduced by the manufacturingprocedure (median = 046 119875 lt 22119890 minus 16)

33 Pooling Microarray Datasets from Different PlatformsPooling and integration of multiple microarray dataset areattractive to increase the number of sample in order toenhance statistical power Because of the heterogeneousnature of microarray datasets it is important to address theissue of cross-platform variation when combining datasetfrom multiple sources In the current study we were in theexceptional situation that a large number of samples had been


Batch 03Batch 05Batch 06Batch 07Batch 08Batch 09 Prior to batch effect adjustment

(7)

(5)

(16)

(a)

After batch effect adjustment

Batch 03Batch 05Batch 06Batch 07Batch 08Batch 09

(5)

(7)

(11)

(b)

Basal-likeHER2-enrichedLuminal ALuminal BNormal-like Prior to batch effect adjustment

(7)

(5)

(16)

(c)


Basal-likeHER2-enrichedLuminal ALuminal BNormal-like

(7)

(5)(11)

(d)

Figure 2 PCA plots showing all 243 samples analyzed using the 29K array platform prior to ((a) and (c)) and after batch-effect adjustmentusing ComBat method ((b) and (d)) Colors in (a) and (b) correspond to the different fabrication batches Colors in (c) and (d) correspondto the molecular breast cancer subtypes classified using PAM50 subtype classifier

analyzed on two different microarray platforms This gave usa unique possibility to evaluate the significance of adjustingfor interplatform differences by assessing the cross-platformagreement of the gene expression measurements We choseto use the ComBat method for standardization of the twodatasets Intraclass correlation coefficients ICC(11) wereused as a measure of cross-platform agreement of the geneexpression levels measured on the two platforms prior to andafter interplatform adjustments ICC is scale sensitive andprovides an assessment of the consistency and reproducibilityof quantitative measurements

Interplatform adjustment led to a highly significantincrease in ICC coefficients (median (prior to interplatformadj only intraplatform adj) = 01 median (after interplat-form adj) = 046 119875 lt 22119890 minus 16) To test if filtering of geneswith low variance would improve correlation even furtherwe created a data subset retaining only genes with a standarddeviation gt03 across samples (2125 genes) When using onlythe high-variance genes we gained additional improvementICC coefficient (median = 084119875 lt 22119890minus16) demonstratingthat high-variance genes are more reproducibly measuredacross different microarray platforms An ICC coefficient

matrix between all possible sample pairs was generated Truefor all sample pairs the highest ICC coefficients were alwaysobserved when comparing the measurements of the samesample measured on the two platforms (data not shown)

A number of studies have been carried out to evaluate thecorrelation between data produced by different microarrayplatforms Reports of both low and high reproducibilitybetween different platforms exist [2 27ndash29] In a study byKuo et al they analyzed two RNA samples and exam-ined gene pair correlations among five different platformsboth commercial and spotted They found that correlationsbetween platforms were in general good for most platformswith correlation coefficients ranging from 063 to 092 andthe highest within platforms of the same type (single colorplatforms and dual color platforms) In addition they showedthat when probe sequences mapped to the same exon for agiven gene the measurements were found to be more similaracross platforms [30]

Our results demonstrated that both intraplatform adjust-ment for batch effects and between-platform adjustmentsimprove the reproducibility of the gene expression measure-ments In addition the analysis showed that by filtering out


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(c)

Figure 3 Correlation and ICC analysis Box plots illustrating the pair-wise sample-sample correlation coefficients when no adjustment wasapplied and after intraplatform adjustment for batch effects using ComBat for all 243 sample pairs using the 18424 shared genes (a) andpair-wise gene-gene correlation coefficients for all 18424 gene-pairs across all 243 samples (b) Box plots illustrating effects of intraplatformbetween-platform adjustment and high-variance genes on the pair-wise gene-gene ICC coefficient measured for all 18424 gene-pairs acrossall 243 samples (c)

low-variance genes we were able to enrich for genes thatwere highly reproducibly expressed across platforms makinghigh-variance genes particularly well suited as biomarkers orfor building gene signatures

34 Detection of Differentially Expressed Genes A commonapplication of gene expression profiling studies is to detectcandidate genes that are differentially expressed between twoor more groupsWith the two datasets available derived fromthe same set of cancer samples we were in a position to assess

the agreement of differentially expressed genes across differ-ent platforms For the purpose we applied the significanceanalysis of microarrays (SAM) algorithm frequently used toidentify differentially expressed genes between two groupsThe sampleswere divided into estrogen receptor (ER) positiveand negative samples ER status was selected as it is a clinicalused biomarker that is able to divide breast tumors intotwo clinically important and clearly distinguishable groupsof cancers The biological differences between ER+ and ERminussamples are very strong and known to have a profound


200 300Size of list

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Figure 4 Detection of differentially expressed genes between ER-positive and ER-negative breast cancer samples (a) and between luminalA and luminal B samples (b) respectively Significance analysis of microarrays (SAM) algorithm was applied to the Agilent dataset and tothe 29K dataset both with and without ComBat intraplatform batch-effect adjustment respectively The resulting gene lists were then rankedaccording to their statistics The plot shows the proportion of shared top genes between Agilent versus 29K unadj (blue) Agilent versus 29Kintraplatform adj (red) and Agilent versus 29K intraplatform adj using only high-variance genes (green) respectively

influence on the gene expression pattern The SAM testwas applied to the Agilent dataset and to the 29K datasetboth with and without ComBat intraplatform batch-effectadjustment respectively The gene lists were then rankedaccording to their statistics The proportion of shared topgenes comparing Agilent versus 29K unadjusted and Agilentversus 29K intraplatform adjusted respectively is visualizedin Figure 4(a) to provide a qualitative illustration of the sizeof overlap Only minor effects of the ComBat adjustmentwere seen in the number of shared genes discriminatingER+ and ERminus samples To investigate whether more delicatedifferences in gene expression could be more sensitive tobatch effects we conducted the SAM analysis in order toidentify genes discriminating between luminal A and luminalB subtypes (Figure 4(b)) Clearly stronger improvementscould be seen here Larger overlaps in top genes discriminat-ing between luminal A and luminal B samples were observedafter the ComBat batch adjustment

Furthermore we sought to evaluate whether the high-variance genes also were among the most consistentlyexpressed genes across platforms By only including high-variance genes the proportion of shared genes were clearlyhigher both in the ER+ERminus comparison and in the luminalAB comparison respectively (Figures 4(a) and 4(b)) Ourresults indicate that by filtering out low-variance genes priorto differential expression analyses it was possible to enrichfor genes that were consistently differentially expressed acrossplatforms Selecting valid and robust differential expressedgenes is essential when identifying candidate genes forpossible use as clinical biomarkers and gene signatures

35 Prediction of Estrogen Receptor Status For a detailedcomparison of the gene expression pattern of a single gene wechoose the estrogen receptor (ESR1) due to its known clinicalimportance and the availability of immunohistochemical(IHC) ER expression data for the vast majority of the samples(231 out of 243) Density plots of the gene expression levelsof the ESR1 probe showed very clear bimodal distributionswhenmeasured on theAgilent platform representing tumorswith low and high expression of ESR1 respectivelyThe ESR1-low peaks show large overlap with the IHC ER negativetumors whereas the ESR1-high peaks cover the majority ofthe IHC ER positive tumors In the unadjusted 29K datasetthe ESR1measurement also displayed a bimodal distributionassociated with the IHC ER status although the ESR1-high peak was less distinct ComBat intraplatform batchadjustment of the 29K corrected this resulting in a bimodaldistribution curve very similar to the Agilent measurementsThe ESR1 expression measurements on the Agilent and the29K platform were highly correlated (Figure 5) Betweenthe Agilent and 29K unadjusted datasets the correlationcoefficient obtained from ESR1 was 119903 = 093 ComBat batchadjustment further improved the correlation coefficient (119903 =096) To evaluate the predictive power of ESR1 expression inrelation to IHC ER status we calculated the area under curve(AUC) of ROC curve analyses leading to very comparableresults in the two datasets (AUCagilent = 0955 AUC29K =0948) Using ESR1 cutoff values obtained from the bimodaldistribution (16 for all datasets) we obtained mean balancedaccuracies of 89 and 88 for the Agilent dataset and the29K datasets respectively (Table 2) No differences between


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(h)

Figure 5 Density plot of ESR1 gene expression levels and receiver operating characteristic (ROC) curves generated from samples withavailable immunohistochemical data measured on the Agilent platform ((a) and (b)) 29K (no adjustments) ((c) and (d)) 29K (intraplatformadj) and the 29K platform ((f) and (g)) Scatter plot visualizing the correlation between ESR1 expression levels measured on the Agilentplatform compared to 29K unadjusted and intraplatform adjusted measurements respectively ((e) (h))


Basal-likeHER2-enrichedLuminal A

Luminal BNormal-like

Agilent dataset


PAM50 molecular breast cancer subtypes

29K dataset (intraplatform adj)

120593c = 0768

120593c = 0782

Figure 6 Comparison of the molecular subtype classificationmethods PAM50 Between Agilent and 29K (unadjusted) datasets 203 sampleswere found to be concordant and 40 discordant (Cramerrsquos 119881 association coefficient = 0768) Between Agilent versus 29K (intraplatformadjusted) the classifications resulted in 208 concordant and 35 discordant subtype predictions (Cramerrsquos 119881 association coefficient = 0782)

Table 2 Prediction of ER status by ESR1 expression levels in the Agilent dataset and the 29K dataset

Platform Number of samples Cutoff Sensitivity (TP) Specificity (TN) AccuracyAgilent dataset 177 vs 54 16 082 (44) 096 (169) 08929K dataset (no adjustment) 177 vs 54 16 082 (44) 094 (167) 08829K dataset (intraplatform adj) 177 vs 54 16 082 (44) 094 (167) 088

Table 3 Association between ER prediction results by the twoplatforms

Agilent29K ERminus ER+ERminus 54 1ER+ 3 185

the batch adjusted and unadjusted datasets were observedFifty-four tumors were predicted as ERminus and 185 tumors asER+ in both datasets Prediction results were discordant foronly four tumors indicating a strong association between thetwo prediction results (119875 lt 22119890 minus 16 Fisherrsquos exact Cramerrsquos119881 association coefficient = 0954) (Table 3)

The high levels of agreement between IHC-based ERstatus and ER status prediction and the high cross-platformconcordance rate indicate that predictions of estrogen recep-tor status by microarray gene expression analysis are veryrobust Because of its high discriminatory power in a studyby Li et al the authors suggested that gene expressionanalysis might even be a better choice for assessing ER statuscompared to IHC-based analysis as it might bemore sensitivecompared to traditional IHC analysis [31] They showedthat up to 21 of patients of IHC-based negative ER statuswould have been classified as ER positive breast cancers bygene expression Furthermore in tamoxifen-treated cohortsthe gene expression measurements improved prediction ofclinical outcome Usually IHC-based ER-negative tumorsare defined by staining in less than 10 of the cells Geneexpression profiles indicate that this cutoff should be adjustedin order to avoid false negative ER classification Tumor het-erogeneity might also contribute to the discordance betweenIHC and gene expression measurements

36 Molecular Subtype Classification For comparisons ofmolecular subtype classifications we applied a commonlyused classifier designed for single sample predictions the50-gene subtype classifier (PAM50) developed by Parker etal [18] PAM50 classifications were performed on the Agilentdataset the unadjusted 29K dataset and the intraplatformComBat adjusted 29K dataset (Figure 6 Tables 4-5) Aspredictors based on centroids and correlation can be verysensitive heatmaps using the PAM50 genes were generatedin order to visually confirm that the subtype classificationwas reasonably correct (Supplementary Material Figure S1-2 available online at httpdxdoiorg1011552014651751)A strong association between subtype classifications wasachieved when sample pairs across platformswere comparedAgilent versus 29K (unadjusted) classifications resulted in203 concordant and 40 discordant subtype predictions(Cramerrsquos 119881 association coefficient = 0768) and Agilentversus 29K (intraplatform adjusted) classifications resultedin 208 concordant and 35 discordant subtype predictions(Cramerrsquos 119881 association coefficient = 0782) A high con-cordance rate was observed among tumors predicted asbasal-like whereas distinguishing luminal B from HER2-enriched and luminal A involves more disagreements In astudy by Waddell et al they compared gene expression datafrom frozen breast tumor biopsies and FFPE tumor blocksIn 12 out of 15 cases the FFPEfrozen sample pairs wereconcordant in relation to the molecular subtypes classifiedusing the PAM50 classifier [32] Our results indicate thatPAM50 classification is a very robust method for predictionof molecular subtypes The PAM50 genes were originallyselected among genes that showed high variance acrosstumor As shown above high-variance genes tend to be morestable expressed across platforms which may explain therobustness of our PAM50 subtype classification results acrossplatforms supporting its validity as a clinical assay


Table 4 Association between PAM50 molecular subtype classifications in the Agilent and the 29K dataset (unadjusted)

Agilent29K Basal-like HER2-enriched Luminal A Luminal B Normal-likeBasal-like 42 0 1 1 0HER2-enriched 0 27 0 1 0Luminal A 0 3 83 4 6Luminal B 0 7 10 43 0Normal-like 3 0 4 0 8

Table 5 Association between PAM50 molecular subtype classifications in the Agilent and the 29K dataset (intraplatform adjusted)


4 Conclusion

Using data from 243 breast cancer samples analyzed ontwo different microarray platforms our in-house 29K arrayplatform and Agilent SurePrint G3 microarray platformwe demonstrated the importance of detecting and tacklingbatch-related biases within datasets prior to data analysisWe have demonstrated how tools such as ComBat aresuitable to successfully overcome systematic technical varia-tions in order to unmask essential biological signals Batchadjustment was found to be particularly valuable in thedetection of more delicate differences in gene expressionFurthermore the study shows that prober adjustment isessential for optimal integration of gene expression dataobtained from multiple sources The data also demonstratethat high-variance genes are highly reproducibly expressedacross platforms making them particularly well suited asbiomarkers or for building gene signatures Predictions ofestrogen-receptor status and molecular breast cancer sub-types were found to be highly concordant In conclusion thestudy emphasizes the importance of utilizing proper batchadjustment methods to reduce systematically technical biaswhen analyzing and integrating data from different batchesand microarray platforms and by selecting high-variancegenes it is possible to enrich for highly reproducible genes

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] T Han C D Melvin L Shi et al ldquoImprovement in thereproducibility and accuracy of DNAmicroarray quantificationby optimizing hybridization conditionsrdquo BMC Bioinformaticsvol 7 supplement 2 article S17 2006

[2] P K Tan T J Downey E L Spitznagel Jr et al ldquoEvaluationof gene expressionmeasurements from commercial microarray

platformsrdquoNucleic Acids Research vol 31 no 19 pp 5676ndash56842003

[3] R Kothapalli S J Yoder S Mane and T P Loughran JrldquoMicroarray result How accurate are theyrdquo BMC Bioinformat-ics vol 3 article 22 2002

[4] O Alter P O Brown and D Botstein ldquoSingular value decom-position for genome-wide expression data processing andmodelingrdquo Proceedings of the National Academy of Sciences ofthe United States of America vol 97 no 18 pp 10101ndash101062000

[5] M Benito J Parker Q Du et al ldquoAdjustment of systematicmicroarray data biasesrdquo Bioinformatics vol 20 no 1 pp 105ndash114 2004

[6] W E Johnson C Li and A Rabinovic ldquoAdjusting batch effectsin microarray expression data using empirical Bayes methodsrdquoBiostatistics vol 8 no 1 pp 118ndash127 2007

[7] J Luo M Schumacher A Scherer et al ldquoA comparison ofbatch effect removal methods for enhancement of predictionperformance usingMAQC-IImicroarray gene expression datardquoPharmacogenomics Journal vol 10 no 4 pp 278ndash291 2010

[8] Z Hu C Fan D S Oh et al ldquoThe molecular portraits ofbreast tumors are conserved acrossmicroarray platformsrdquoBMCGenomics vol 7 article 96 2006

[9] X Fan E K Lobenhofer M Chen et al ldquoConsistency of pre-dictive signature genes and classifiers generated using differentmicroarray platformsrdquo The Pharmacogenomics Journal vol 10no 4 pp 247ndash257 2010

[10] M J Larsen T A Kruse Q Tan et al ldquoClassifications withinmolecular subtypes enables identification of BRCA1BRCA2mutation carriers by RNA tumor profilingrdquo PLoS ONE vol 8no 5 Article ID e64268 2013

[11] M J LarsenMThomassenQ Tan et al ldquoRNAprofiling revealsfamilial aggregation of molecular subtypes in non-BRCA12breast cancer familiesrdquo BMC Medical Genomics vol 7 article9 2014 httpwwwncbinlmnihgovpubmed16792514

[12] M E Ritchie J Silver A Oshlack et al ldquoA comparison ofbackground correction methods for two-colour microarraysrdquoBioinformatics vol 23 no 20 pp 2700ndash2707 2007

[13] Y H Yang S Dudoit P Luu et al ldquoNormalization for cDNAmicroarray data a robust composite method addressing single


and multiple slide systematic variationrdquo Nucleic acids researchvol 30 no 4 article e15 2002

[14] Y H Yang and N P Thorne ldquoNormalization for two-colorcDNA microarray datardquo Lecture Notes Monograph Series vol40 pp 403ndash418 2003

[15] The R Development Core Team R A Language and Environ-ment for Statistical Computing R Foundation for StatisticalComputing Vienna Austria 2011 httpwwwR-projectorg

[16] G K Smyth ldquoLimma linear models for microarray datardquo inBioinformatics andComputational Biology SolutionsUsing R andBioconductor R Gentleman V Carey S Dudoit R IrizarryandW Huber Eds pp 397ndash420 Springer New York NY USA2005

[17] X Robin N Turck A Hainard et al ldquopROC an open-sourcepackage for R and S+ to analyze and compare ROC curvesrdquoBMC Bioinformatics vol 12 article 77 2011

[18] J S Parker M Mullins M C U Cheang S Leung and DVoduc ldquo Supervised risk predictor of breast cancer based onintrinsic subtypesrdquo Journal of Clinical Oncology vol 27 no 8pp 1160ndash1167 2009

[19] B Haibe-Kains M Schroeder G Bontempi C Sotiriouand J Quackenbush ldquogenefu Relevant Functions for GeneExpression Analysis Especially in Breast Cancerrdquo 2011httpcompbiodfciharvardedu

[20] D Meyer A Zeileis and K Hornik ldquoThe strucplot frameworkvisualizing multi-way contingency tables with vcdrdquo Journal ofStatistical Software vol 17 no 3 pp 1ndash48 2006

[21] M Thomassen V Skov F Eiriksdottir et al ldquoSpotting andvalidation of a genome wide oligonucleotide chip with dupli-cate measurement of each generdquo Biochemical and BiophysicalResearch Communications vol 344 no 4 pp 1111ndash1120 2006

[22] C Chen K Grennan J Badner et al ldquoRemoving batch effectsin analysis of expression microarray data an evaluation of sixbatch adjustment methodsrdquo PLoS ONE vol 6 no 2 Article IDe17238 2011

[23] T Soslashrlie R Tibshirani J Parker et al ldquoRepeated observationof breast tumor subtypes in independent gene expression datasetsrdquo Proceedings of the National Academy of Sciences of theUnited States of America vol 100 pp 8418ndash8423 2003

[24] K Yu C H Lee P H Tan and P Tan ldquoConservation of breastcancer molecular subtypes and transcriptional patterns oftumor progression across distinct ethnic populationsrdquo ClinicalCancer Research vol 10 no 16 pp 5508ndash5517 2004

[25] T Soslashrlie C M Perou R Tibshirani et al ldquoGene expressionpatterns of breast carcinomas distinguish tumor subclasses withclinical implicationsrdquo Proceedings of the National Academy ofSciences of theUnited States of America vol 98 no 19 pp 10869ndash10874 2001

[26] C M Perou T Soslashrile M B Eisen et al ldquoMolecular portraits ofhuman breast tumoursrdquoNature vol 406 no 6797 pp 747ndash7522000

[27] M D Kane T A Jatkoe C R Stumpf J Lu J D Thomas andS J Madore ldquoAssessment of the sensitivity and specificity ofoligonucleotide (50mer) microarraysrdquo Nucleic Acids Researchvol 28 no 22 pp 4552ndash4557 2000

[28] C L Yauk andM L Berndt ldquoReview of the literature examiningthe correlation amongDNAmicroarray technologiesrdquo Environ-mental and Molecular Mutagenesis vol 48 no 5 pp 380ndash3942007

[29] L Shi W Tong H Fang et al ldquoCross-platform compara-bility of microarray technology intra-platform consistency

and appropriate data analysis procedures are essentialrdquo BMCBioinformatics vol 6 no 2 article S12 2005

[30] W P Kuo F Liu J Trimarchi et al ldquoA sequence-orientedcomparison of gene expression measurements across differenthybridization-based technologiesrdquo Nature Biotechnology vol24 no 7 pp 832ndash840 2006

[31] Q Li A C Eklund N Juul et al ldquoMinimising immunohisto-chemical false negative ER classification using a complementary23 gene expression signature of ER statusrdquo PLoS ONE vol 5 no12 Article ID e15031 2010

[32] N Waddell S Cocciardi J Johnson et al ldquoGene expressionprofiling of formalin-fixed paraffin-embedded familial breasttumours using the whole genome-DASL assayrdquo Journal ofPathology vol 221 no 4 pp 452ndash461 2010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


use of spotted arrays Spotted arrays are often manufacturedin relatively small batches under semistandardized condi-tions This often introduces systematic differences betweenthe measurements of different batches often termed ldquobatcheffectsrdquo [1] Furthermore lack of standardization makes itchallenging to compare and integrate data from the differentmicroarray technologies available as it introduced platform-dependent systematic variation Several studies have demon-strated that pooling data derived from different platforms is acomplex task with numerous pitfalls [2 3] Additional factorsare known to introduce systematic variation including whenthe analysis is conducted at different sites by alternatingpersonnel in different experiments or simple due to day-to-day variation

Combining data from different experimentsplatformbatches without reducing possible systematic variant cangive rise to misleading results that can be strong enough tomask or even confound true biological signals and lead tomisinterpretation of the data For unmasking it is necessaryto identify and remove the batch effects before proceedingSeveral approaches have been developed for removal ofsystematic batch effects Single value decomposition andprincipal component analysis (PCA) have been used bysubtracting the component representing the batch effect fromthe data [4] Distance-weighted discrimination (DWD) usesa modified version of the support vector machine (SVM)algorithm to correct for batch effect [5] ComBat proposedby Johnson et al applies an empirical Bayes approach bypooling information across genes and shrinks the batch effectparameter toward the overall mean of the batch estimatesacross genes [6] Other commonly used batch effect methodsinclude mean centering standardization and ratio-basedmethods [7]

Due to its extensive use thousands of gene expres-sion microarray datasets have been deposited to publicdatabases making these repositories valuable data sourcesMicroarray gene expression data has been widely used inmedical decision-making research For clinical use of array-based diagnostics significant numbers of patient samplesare needed for training and evaluation Therefore reusepooling and integration of multiple microarray dataset areattractive approaches Because of the heterogeneous natureof microarray datasets it is important to address the issue ofcross-platform variation as demonstrated in several studieswhen combining different dataset from different platformsanalyzed in different laboratories for example medicaldecision-making purposes [7ndash9] Furthermore as the RNAsequencing technology develops and matures another levelof batch effects needs to be taken into consideration whendatasets of different origin are to be pooled

In the current study we analyzed the same 234 breastcancers on two different microarray platforms our in-house29K array platform and Agilent SurePrint G3 microarrayplatform The 29K dataset contained known batch effectsassociatedwith the fabrication procedure Using these uniquedatasets the aim was to assess the significance of removingsystematic batch effects existing within a dataset and whenintegrating datasets of different platforms

2 Materials and Methods

21 Ethics Statement The study was carried out as a retro-spective register study and in accordance with the HelsinkiDeclaration The study has been approved by The NationalCommittee on Health Research Ethics of Denmark (S-VF-20020142) waiving the requirement for informed consent forthe study

22 PatientMaterial This study was performed on a series of243 frozen primary breast tumors obtained from the biobanksof the Department of Pathology Odense University Hospitaland the Danish Breast Cancer Cooperative Group (DBCG)The tumor samples comprise a subset of a larger series of253 tumor samples previously reported [10 11] Breast tumortissues from 120 patients with germline mutations in BRCA1(119899 = 33) or BRCA2 (119899 = 22) or familial non-BRCA12 caseswith no detectable germline mutation in BRCA1 or BRCA2(119899 = 65) were included in the study In addition 123 primarytumor samples from sporadic breast cancers were includedin the study In order to determine the tumor cell contentslides of the frozen tumor biopsies were haematoxylin-eosin-stained and examined by a pathologist at the Departmentof Pathology Odense University Hospital Samples analyzedin the present study contained at least 50 tumor cellsImmunohistochemistry determined estrogen receptor (ER)expression data was obtained from the Danish Breast Can-cer Cooperative Group (DBCG) Gene expression analyseswere performed using two different microarray platformsthe in-house manufactured 29K oligonucleotide microarrayplatform and Agilent SurePrint G3 platform

23 Microarray Fabrication For manufacturing of the in-house spotted arrays a human oligonucleotide library con-sisting of 28919 DNA oligonucleotide probes (60-mer) waspurchased from Compugen-Sigma-Genosys (The Wood-lands) The oligonucleotide probes were solubilized in150mM sodium phosphate buffer (pH 85) to a final concen-tration of 20 pmol120583L and spotted onto CodeLink HD (Sur-Modics) activated glass slides by a high-precision spottingrobot (Virtek ChipWriter Pro ESI) The slides have an activepolymer coating of amine-reactive groups permitting the 51015840-amino modified DNA oligos to covalently attach under highrelative humidity The microarray fabrication was carriedout in a controlled environment at 38 humidity and thetemperature was held constant at 23∘C SMP 25 stealth pins(TeleChem International) was used to deposit the oligosonto surface of the slides Up to 85 arrays were spotted perbatch After printing slides were incubated overnight at 70humidity and blocked as recommended by the manufacturer

24 Gene Expression Analysis Total RNAwas extracted fromfreshly frozen tumor tissue RNA extraction was carried outusing Trizol Reagent (Invitrogen) followed by RNeasy MicroKit (Qiagen) including DNase treatment RNA concentrationwas determined using aNanoDrop Spectrophotometer (Nan-oDrop Technologies) and the quality assessed by the Agilent2100 Bioanalyzer using Agilent RNA 6000 Nano Kit (Agilent


















19191


22171

42706 probes


28919 probes

18424

7673747








243






(7)

(5)

(16)

(a)



(5)

(7)

(11)

(b)


(7)

(5)

(16)

(c)



(7)

(5)(11)

(d)








Pear

sonrsquos

corr

elat

ions

coeffi

cien

t


P = 29e minus 11

04

05

06

07

08

09

(a)


05

00

Pear

sonrsquos

corr

elat

ions

coeffi

cien

t

P = 22e minus 16

(b)

No

adj

Intr

apla

tform

adj

for b

atch

effec

ts

Hig

h-va

rianc

ege

nes

Betw

een-

plat

form

adju

stmen

t

minus05

minus10

ICC

coeffi

cien

t

00

10

05


(c)






200 300Size of list

100

0

1Pr

opor

tion

in co

mm

on

01

02

03

04

05

06

07

08

09


(a)

200 300Size of list

100

0

1

Prop

ortio

n in

com

mon

01

02

03

04

05

06

07

08

09


(b)






0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


















19191


22171

42706 probes


28919 probes

18424

7673747








243






(7)

(5)

(16)

(a)



(5)

(7)

(11)

(b)


(7)

(5)

(16)

(c)



(7)

(5)(11)

(d)








Pear

sonrsquos

corr

elat

ions

coeffi

cien

t


P = 29e minus 11

04

05

06

07

08

09

(a)


05

00

Pear

sonrsquos

corr

elat

ions

coeffi

cien

t

P = 22e minus 16

(b)

No

adj

Intr

apla

tform

adj

for b

atch

effec

ts

Hig

h-va

rianc

ege

nes

Betw

een-

plat

form

adju

stmen

t

minus05

minus10

ICC

coeffi

cien

t

00

10

05


(c)






200 300Size of list

100

0

1Pr

opor

tion

in co

mm

on

01

02

03

04

05

06

07

08

09


(a)

200 300Size of list

100

0

1

Prop

ortio

n in

com

mon

01

02

03

04

05

06

07

08

09


(b)






0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



(7)

(5)

(16)

(a)



(5)

(7)

(11)

(b)


(7)

(5)

(16)

(c)



(7)

(5)(11)

(d)








Pear

sonrsquos

corr

elat

ions

coeffi

cien

t


P = 29e minus 11

04

05

06

07

08

09

(a)


05

00

Pear

sonrsquos

corr

elat

ions

coeffi

cien

t

P = 22e minus 16

(b)

No

adj

Intr

apla

tform

adj

for b

atch

effec

ts

Hig

h-va

rianc

ege

nes

Betw

een-

plat

form

adju

stmen

t

minus05

minus10

ICC

coeffi

cien

t

00

10

05


(c)






200 300Size of list

100

0

1Pr

opor

tion

in co

mm

on

01

02

03

04

05

06

07

08

09


(a)

200 300Size of list

100

0

1

Prop

ortio

n in

com

mon

01

02

03

04

05

06

07

08

09


(b)






0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Pear

sonrsquos

corr

elat

ions

coeffi

cien

t


P = 29e minus 11

04

05

06

07

08

09

(a)


05

00

Pear

sonrsquos

corr

elat

ions

coeffi

cien

t

P = 22e minus 16

(b)

No

adj

Intr

apla

tform

adj

for b

atch

effec

ts

Hig

h-va

rianc

ege

nes

Betw

een-

plat

form

adju

stmen

t

minus05

minus10

ICC

coeffi

cien

t

00

10

05


(c)






200 300Size of list

100

0

1Pr

opor

tion

in co

mm

on

01

02

03

04

05

06

07

08

09


(a)

200 300Size of list

100

0

1

Prop

ortio

n in

com

mon

01

02

03

04

05

06

07

08

09


(b)






0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


200 300Size of list

100

0

1Pr

opor

tion

in co

mm

on

01

02

03

04

05

06

07

08

09


(a)

200 300Size of list

100

0

1

Prop

ortio

n in

com

mon

01

02

03

04

05

06

07

08

09


(b)






0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


0

3

6

9

25 75

Cou

nt


Cutoff = 16

minus25 5000

IHC ERminusIHC ER+

(a)

Specificity

Sens

itivi

ty

10

10

04

04

AUC 0955

Agilent dataset

08

08

06

06

02

02

0000

95 CI 0923ndash0983

(b)

0

3

6

9

0 4

Cou

nt


Cutoff = 16

2 6ESR1 expression

IHC ERminusIHC ER+

(c)


AUC 094895 CI 0917ndash0979


Sens

itivi

ty

10

04

08

06

02

00

(d)

01234567

1 3 5 7 9

29K

data

set (

no ad

j)

minus1minus3

minus2

minus1


r = 093

ESR

1 ex

pres

sion

(e)


00

25

50

75

100

0 2 4 6

Cou

nt

Cutoff = 16

IHC ERminusIHC ER+

(f)

AUC 094895 CI 0917ndash0978



Sens

itivi

ty

10

04

08

06

02

00

(g)

01234567

1 3 5 7 929K

data

set (

intr

apla

tform

adj)

minus1

minus2minus1minus3

ESR

1 ex

pres

sion

r = 096


(h)





Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Agilent dataset




120593c = 0768

120593c = 0782














4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






4 Conclusion




References












































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

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