We describe a novel approach to capturing the covariance structure of peripheral blood gene expression that relies on the identification of highly conserved Axes of variation. Starting with a comparison of microarray transcriptome profiles for a new dataset of 189 healthy adult participants in the Emory-Georgia Tech Center for Health Discovery and Well-Being (CHDWB) cohort, with a previously published study of 208 adult Moroccans, we identify nine Axes each with between 99 and 1,028 strongly co-regulated transcripts in common. Each axis is enriched for gene ontology categories related to sub-classes of blood and immune function, including T-cell and B-cell physiology and innate, adaptive, and anti-viral responses. Conservation of the Axes is demonstrated in each of five additional population-based gene expression profiling studies, one of which is robustly associated with Body Mass Index in the CHDWB as well as Finnish and Australian cohorts. Furthermore, ten tightly co-regulated genes can be used to define each Axis as “Blood Informative Transcripts” (BITs), generating scores that define an individual with respect to the represented immune activity and blood physiology. We show that environmental factors, including lifestyle differences in Morocco and infection leading to active or latent tuberculosis, significantly impact specific axes, but that there is also significant heritability for the Axis scores. In the context of personalized medicine, reanalysis of the longitudinal profile of one individual during and after infection with two respiratory viruses demonstrates that specific axes also characterize clinical incidents. This mode of analysis suggests the view that, rather than unique subsets of genes marking each class of disease, differential expression reflects movement along the major normal Axes in response to environmental and genetic stimuli. Gene expression profiling of human tissues typically reveals a complex structure of co-regulation of gene expression that has yet to be explored with regard to the genetic and environmental sources of covariance or its implications for quantitative and clinical traits. Here we show that peripheral blood samples from multiple studies can be described by nine common axes of variation that collectively explain up to one half of all transcriptional variance in blood. Specific axes diverge according to environmental variables such as lifestyle and infectious disease exposure, but a strong genetic component to axis regulation is also inferred. As few as 10 “blood-informative transcripts” (BITs) can be used to define each axis and potentially classify individuals with respect to multiple aspects of their blood and immune function. The analysis of longitudinal profiles of one individual shows how these change relative to clinical shifts in metabolic profile following viral infection. The notion that gene expression diverges along genetic paths of least resistance defined by these axes has important implications for interpreting differential expression in case-control studies of disease. The promise of personalized medicine is predicated on the assumption that people differ with respect to their disease susceptibility. To date, medical genomics has contributed primarily by defining genotypic risk based on common variants associated with disease and rare mutations predicted to disrupt proteins A promising approach is gene expression profiling. Most of the focus of GWAS for gene expression has been on local regulatory polymorphisms that explain over a quarter of the variance of individual transcripts A major problem with such methods is that they have relatively low repeatability, and hence it is not clear that they truly reflect common regulatory mechanisms. The first principal component of whole blood gene expression is highly conserved across multiple studies that we have conducted, in that the loadings of thousands of transcripts significantly correlated with PC1 are similar, although a fraction of the transcriptome seems susceptible to methodological differences . The nexOne such strategy was adopted by Chaussabel et al Axes of common peripheral blood gene expression variation , despite being enriched for platelet and red blood cell functions. We also asked whether transcripts known to be enriched in these two cell types are over-represented in Axis 2 genes, but they are not, from which we conclude that variation in Axis 2 is not due to variation in the abundance of these two anuclear cell types. It is possible that other sub-classes of immune cells contribute to the observed variation . Neutrophils, monocytes and lymphocytes are the predominant white blood cells by number in peripheral blood, so more minor cell types constituting less than 5% of the cells  may also contribute, though it is mathematically unlikely for them to account for the fold changes at either end of the axis distributions, especially for hundreds of genes. Nevertheless, the pervasiveness of the axis associations and the impact of sampling method on the relationship between the axes documented in The existence of inter-individual variation in gene expression along defined axes must reflect the activity of regulatory networks that have evolved to ensure that thousands of transcripts co-vary in a conserved manner. We present here evidence for both genetic and environmental influences, and provide the relevant data in SNORD and nucleolar component enrichment previously noted In southern Morocco, expression of over a third of the peripheral blood transcriptome was previously shown to differ between residents of rural Berber villages and of the city of Agadir Another likely class of environmental influence is infectious disease. Since the Axes were derived from modules that were previously shown to diverge between immunological conditions, we asked whether the Axes differ according to infection status in the tuberculosis study −8; BMI data was not available for the Morocco study).One other potential source of differentiation is overall health status. A complete description of the relationship between gene expression and clinical covariates in the CHDWB will be presented elsewhere, but we note that body mass index (BMI) and biomarkers of inflammation do correlate with specific Axes of peripheral blood gene expression . BMI is −4)during the high blood glucose phase (red points) with time of onset after day 301 (green diamond), apparently with the change occurring one or two weeks earlier for Axis 6, before the second viral infection (green points) had cleared. Axis 5, the inflammatory axis, shows the opposite trend, possibly simply reflecting negative covariance with Axis 1 which is strong in these 19 samples. Axis 7 is particularly noteworthy for the elevation  during the two to four day acute phase of both respiratory viral infections, as expected given the enrichment for interferon signaling and viral response genes. These changes to some extent mirror spikes and trends in the autocorrelation analysis reported in In order to demonstrate the potential utility of the BIT axis scores for revealing changes in personal genomic response to disease, we assessed the axis scores derived from the BIT in the so-called “Snyderome” longitudinal RNA-Seq dataset There are three aspects of the axes of gene expression variation described here that elevate their potential importance above those of modules identified by unsupervised clustering methods. First, they are highly repeatable across studies in different locations and profiling different subsets of peripheral blood cells. This robustness is further indicated by the ability to capture the major axes of variation with as few as 10 BITs. Second, they are enriched for gene activities that are clearly related to biological functions, in particular inflammation, viral response, and signaling within B and T lymphocytes as well as neutrophils. The axes do not just reflect abundance of the cell types, but also the state of activity within them. Third, they are differentially expressed under a wide variety of blood disease conditions An illustration of this principle is provided in We now discuss four key questions that arise from recognition of the potential for axes of covariance to shape individual responses to genetic and environmental challenges that tend to push them away from good health.How generally are the axes associated with disease? Chaussabel et al identified 28 modules of gene expression that are contained within our axes, and proposed that different disease states may reflect differential activation of these modules To what extent does a person's healthy expression profile predispose them to blood diseases? It is typical when considering differential gene expression to assume that the most strongly up- or down-regulated genes are causally related to the disease. However, an alternative view is that they are part of a programmed protective molecular response, in which case it is the genes in the axis that are not induced that are more responsible for ill health. This view is encapsulated in the notion gaining traction How heritable are the axes of variation? Family-based profiling of whole blood gene expression will answer this question. In CEPH lymphoblast cell lines, it was previously noted that the heritability of individual transcripts is highly significant for over half the transcriptome Finally, how does the existence of these major axes of variation constrain gene expression in biochemical pathways? It appears that the axis of variation of clearly defined pathways tend to be embedded within one of more of the Axes. For example, as shown in All datasets considered here were generated on Illumina HT12 v2 or v3 bead arrays, and unless otherwise indicated we used the raw gene expression measures reported in the published studies. For the Atlanta CHDWB study (described here for the first time), 189 individuals of mixed ethnicity and gender, between the ages of 26 and 75, donated 10 ml of blood during the course of their first visit to the Center for Health Discovery and Wellbeing. The whole blood samples were stored frozen in Tempus tubes, and RNA was extracted and hybridized at the Emory Biomarker Service Center under the direction of Dr Carlos Moreno. This dataset has been deposited in NCBI GEO under accession number GSE35846. The raw log base 2 expression profiles for 14,343 probes that are consistently expressed in the Morocco, Brisbane Red Cross, and CHDWB studies, were normalized by probe-level ANOVA fitting technical effects of date of hybridization and RNA quality, resulting in standardized normal transcript abundances, which were subsequently mean-centered across individuals. For direct comparison, the Morocco study values were also standard normalized and mean centered. Various other normalization procedures including analysis on the raw data, quantile normalization, and SNM http://www.gibsongroup.biology.gatech.edu/supplementary-data.The other studies considered here utilized whole blood in Tempus tubes  of the full dataset of 14,343 transcripts. −6) while adjusting for the other axes in the model. All probes that were significant in both the CHDWB and Morocco datasets are listed in jk = μ+Σai(PC1i)+ε where the expression Y of the jth gene in the kth individual is a function of the sum over all i = 1 to 7 Axis PC1 scores, and the error ε is assumed to be normally distributed with mean of zero. . ai from the two studies plotted against one another, demonstrating that the association of each transcript with each axis is remarkably highly conserved between the two studies, in the case of Axes 1, 3, 4, 5 and 7 transcriptome-wide (whereas Axes 2 and 6 associated genes form more distinct clusters).ai from the two studies were cross-matched to generate the probe lists that define each Axis, with the additional constraint that only positive associations are included . We then performed a multiple regression using the ANOVA routine in JMP Genomics, generating the list of probes and genes that are highly significantly correlated with each axis , so the above process was repeated giving rise to Axes 8 and 9. That is, we performed multiple regression on the original datasets with the first 7 axis scores plus residual PC2 and PC3, then cross-matched probes that were Bonferroni significant with residual PC2 in CHDWB and residual PC3 in Morocco to obtain the Axis 8 genes, and with residual PC3 in CHDWB and residual PC2 in Morocco to obtain the Axis 9 genes. CHDWB residual PC1 loadings were mildly correlated with Morocco residual PC4, but very few genes were significantly cross-matched in a similar analysis and this potential axis was ignored. There may be additional axes but since Axis 8 and Axis 9 only explain an extra few percent of the total variation, these nine axes appear to be the major conserved axes in our datasets. Axes 8 and 9 are noticeably weaker (explain less of the covariance of associated transcripts) so are not presented in all figures and analyses.http://toppgene.cchmc.org/enrichment.jsp, with similar results. Only the top 500 probes were included for each analysis, so as to ensure comparability across Axes with different numbers of genes, but modification of the number of genes included did not meaningfully affect the core results. Since some probes are duplicated while others are not annotated to genes, the numbers listed are less than 500. ToppFun computes gene set enrichment relative to the full set of annotated genes in the genome, and since the database is updated regularly specific results change subtly over time. Detailed results can be viewed as All statistical manipulations, as well as correlation analyses leading to the findings reported in the figures were performed in JMP Genomics v5.0 . See Figure or Supporting Information Legends for details of individual analyses, and For the Synderome analysis All samples reported for the first time in this study were obtained under written informed consent for participation in the Center for Health Discovery and Well Being study with the approval of the Institutional Review Boards of Emory University and the Georgia Tech.Dataset S1List of genes associated with each of the 9 axes in both Atlanta and Morocco. The 9 sets of columns show the Illumina Probe identifier, the Gene name, and the negative logarithm of the p-value (NLP) for the association of the transcript with the axis score (PC1) in the multiple regression with all 9 axes in Atlanta (NLP_ATL) and Morocco (NLP_MOR). Only probes significant at the Bonferroni adjusted significance level (NLP>5.53) in both studies are included, and they are sorted by average NLP.(XLSX)Click here for additional data file.Dataset S2List of covariates and Axis scores for five studies. Each sheet shows the individual identifier and coariates including Age, BMI, Percent Body Fat, Gender, Ethnicity, Location, and 8 types of cell count for the CHDWB; Location, Gender, Ethnicity and Age for Morocco (13); Gender, Age and BMI for Brisbane Red Cross (14); Study ID and PC1 for the TB status signature reported in the Berry et al study (XLSX)Click here for additional data file.Dataset S3ToppFun gene ontology analysis of each of the 9 Axes. The top 500 genes listed in Dataset S1 were entered into ToppFun (XLSX)Click here for additional data file.Dataset S4Axis scores based on 5, 10, 20 or up to 250 BIT. The two spreadsheets show the PC1 scores based on the 5, 10, 20 or up to 250 most strongly associated probes listed in Dataset S1, for each axis. Within an axis, all four scores are highly correlated.(XLSX)Click here for additional data file.Figure S1(A) Scatterplot of Eigenvalues for each of 14,343 transcripts on PC1 in the CHDWB and Morocco studies. While there is a strong correlation between the loadings for 90% of the transcripts, 10% all have higher values in the CHDWB study. This PC is highly correlated with Axis 4. (B) Lack of orthogonality of PC across studies. Each of PC2 through 5 in the CHDWB study is as strongly correlated with two PC in the Morocco study  reducing the utility of study-specific components of variation for comparative purposes.(PDF)Click here for additional data file.Figure S2Independent evidence that the first 7 Axes are the major axes of covariance. For any set of covarying transcripts, some individuals will be expected to have low values of expression for multiple transcripts in the set, and these will be enriched in the low-expression transcripts of that individual. We thus reasoned that clustering of the variance components of low-expression genes should independently identify the major axes of covariance. For each individual in the Morocco study, up to 100 transcripts that are outliers for low expression  were identified. PC1 was computed for these transcripts, two-way hierarchical clustering of the 189 scores (rows) across the 189 individuals (columns) is shown in the upper plot (A). Nine distinct clusters are observed, seven of which uniquely cluster with the BIT score for one of the first seven Axes (indicated by arrowheads). The rows marked Axis 2b appear to be a subset of Axis 2, and the small unmarked cluster of rows between Axes 3 and 5 may define a new Axis, but further analysis indicates that the scores are driven by very low expression in just the indicated individuals. The lower panel (B) shows the heat map generated by only including the two individuals whose low-expression PC1 most strongly correlates with the respective BIT. Only one individual is close to BIT 7 (which was not uniquely represented by one of the Chaussabel modules), and no individuals are close to BIT 8 or BIT 9. These results are consistent with the first 7 Axes being the major axes of variation, though not necessarily the only ones.(PDF)Click here for additional data file.Figure S3Blood Informative Axis scores. (A) Each plot shows the PC1 loadings of the 10 Bit transcripts in the CHDWB study on the right, and the individual PC1 and PC2 scores on the left. The same result for a typical random set of 10 probes is included as well. Panel J shows a histogram of the percent variance explained by PC1 for 100 random sets of 10 transcripts, relative to that observed for each of the BIT Axes, which are unambiguous outliers. (B) Pairwise comparison of the multiple regression coefficient for each transcript in the CHDWB/Atlanta and Morocco studies for each Axis, showing transcriptome-wide similarity for Axes 1, 3, 4, 5 and 7, whereas a subset of transcripts are clearly more strongly associated with Axes 2 and 6. (C) Principal component analysis of the 175 probes most strongly associated with each Axis, compared with a similar plot for a typical Chaussabel module, 3.5. In each case, the histogram of eigenvalues to the left shows how PC1 (the Axis score) dominates the covariance. The amount of variation explained by Axes and Modules in each study is compared in (PDF)Click here for additional data file.Figure S4The covariance of BIT Axes is somewhat study-specific. Panels A and D show the correlation between the BIT Axis scores Click here for additional data file.Figure S5Similarity of correlation structure among Axis scores (PC1 for each of the 10 BIT for each axis) across 6 independent whole blood datasets. In each of the Atlanta CHDWB, Morocco, Brisbane Red Cross (our studies), Celiac Disease, Tuberculosis, and Finland (DILGOM) (performed by others), Axes 1 and 3, and Axes 5 and 7, are to some extent positively correlated. Negative correlations arise in some instances . The reason for the generally more positive correlations in the Celiac study is unclear, but it is noteworthy that all BIT tend to show stronger internal correlations in that study as well .(PDF)Click here for additional data file.Figure S6Cell counts correlate with specific Axes, but do not explain the axes. (A) T-lymphocyte count is positively correlated with Axis 1, but in part due to high scores of individuals with large T-cell counts. (B) Neutrophil counts are correlated with Axis 5, explaining 34% of the variance. Red females, blue males. (C) However, removing the effect of cell counts during normalization with the SNM algorithm (23) has very little effect on axis definition, as the Axis scores match one-to-one with those derived without such normalization [29].(PDF)Click here for additional data file.Figure S710 of the p-value) against estimated BMI effect (the slope of the regression of BMI on Axis score) is also show how Axis 2 (D), but not Axes 1 or 6 , is strongly skewed to up-regulation of genes in Axis 2 in higher BMI individuals. The smoother shape of the curves relative to those in the CHDWB in Replication of the association of BMI with Axis 2 in the Brisbane Red Cross study. Panels A and B show that BIT 2 correlates with both BMI and %BF in Atlanta CHDWB (%BF differs significantly between men and women). The same association is observed in Brisbane (C), where %BF data was not gathered. The volcano plots of significance Click here for additional data file.Figure S8Heritability and differentiation of LCL and Peripheral Blood (PB) in the Brisbane twin study (18). PC1 scores for the 10 BIT per axis were computed for each dataset and are provided in Dataset S2. The percent variation of these BIT explained by PC1 is indicated in panel A, along with the significance  of the correlation between individual scores for LCL and PB. Six of the first seven BIT are also observed in lymphoblast cell lines (LCL), the exception being Axis 2. Nevertheless, there is no correlation between the axis scores for LCL and PB, as indicated in the heat map in panel B. Also, each of Axes 1,3,4,5 and 7 are highly correlated in LCL suggesting that they represent a single shared covariance structure. These results suggest that the whole blood profiles may arise by summation of contributions of different blood cell types. Although the LCL axes are different from PB ones, they also show significant heritability since the twin-twin phenotypic correlations are high for five of the axes as indicated in panel C. These were computed on the PC1 scores for the respective Axes, after removal of 2 outliers (and their twin partners) who are more than two standard deviations from the mean for multiple axes, indicated in italics on Dataset S2. The strongest twin-twin correlation is shown in panel D. These results imply that genetic factors contribute to the establishment of the shared covariance in individual cell types, and in peripheral blood as a whole.(PDF)Click here for additional data file.Table S1List of Blood Informative Transcripts including the Probe ID for the Illumina Human_HT12 bead chips.(DOCX)Click here for additional data file.Table S2Percent variance explained by PC1 for the 10 Blood Informative Transcripts for each Axis, in each of the 7 studies, showing high replication of their co-regulation.(DOCX)Click here for additional data file.Table S3List of 9 studies referred to in the paper, showing the Location of the population, number of samples, source of blood RNA, GEO or ArrayExpress accession number, and reference in this paper (CHDWB is reported for the first time).(DOCX)Click here for additional data file.Table S4Percent variance explained by PC1 for the entire set of probes annotated to the Chaussabel module genes , also showing the number of probes and genes, the Axis each module associates with, and the average for each measure. The bottom rows show the same values for the 175 most strongly associated axis genes for the first 7 axes from Dataset S1, and their averages.(DOCX)Click here for additional data file.Table S5Correlation of Overall transcriptome PCA with Meta-Modules PC1 in Atlanta and Morocco. The values are the correlation coefficients between the first Principal Component for each of the indicated Meta-Modules and for Axis 6, and the first 5 Principal Components (Prin1-5) of the entire gene expression dataset for both the CHDWB Atlanta, and Morocco, studies separately. Bold values are consistent between the two studies, noting that Prin3 and Prin4 negatively correlate with Axis 6 respectively.(DOCX)Click here for additional data file.


The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach.N > 2 groups.We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies. There is a wealth of information enclosed in the massive amount of microarray data so far accumulated in public repositories. The variety of data sets generated from the assortment of experiments is a major obstacle in the path leading from these data to information. Specific issues relating to data heterogeneity across microarray studies include differences across platforms, differences within and between labs, and/or differences among experimental factors such as treatments and tissues ,2. FurthInappropriate integration of microarray data from public repositories could lead to equivocal results . The “Siet al.[et al.[et al.[et al.[et al.[Combining statistical results  of independent studies that address similar questions has been a standard procedure in classic meta-analyses ,13. Thiset al., in a qul.[et al. and Rhodl.[et al. developel.[et al. and Borol.[et al. proposedet al.[Arabidopsis thaliana based on parameters of co-expression similarity that were estimated from a large pool of microarray data downloaded from public repositories. Mentzen and Wurtele [et al.[Arabidopsis. Mentzen and Wurtele [et al.[Arabidopsis. In both works, GO enrichment analysis showed that networks based on gene-to-gene correlations estimated from pooling data from multiple microarray studies were not random. A similar approach has been used to obtain regulon information from a human transcriptomic network derived from almost 20,000 microarrays [Another approach combines data from multiple microarray studies (termed “pooled data”) in a melting pot of data and analyzes them as a single data set. Kim and Webster  used pubet al. have cre Wurtele  pooled de [et al. pooled d Wurtele  analyzede [et al. used cluroarrays . This anFrom a statistical standpoint, combining data from independent microarray studies into a large pool as a single set can be acceptable if data homogeneity can be ensured across studies. Yet, this condition is nearly impossible to ensure considering that significant data heterogeneity is reported even for completely replicated microarray experiments carried out by the same lab . NeverthThe objective of this study was to perform a comprehensive analysis of Pearson correlation coefficients estimated from pooling heterogeneous groups of data (melting pot approach) in a large-scale gene expression analysis of publicly available Affymetrix microarrays and compare it to the analysis (of the same data) that combines statistical results of individual groups (mosaic approach). Our study included two specific objectives: (a) to determine the specific effect of different means and heteroskedasticity across the many groups comprising a pool of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of data, and (b) to quantify the extent of bias in gene-to-gene Pearson correlation coefficients obtained from a pool of heterogeneous groups of microarray data.N > 2 groups, and the validity of our methodology when groups have a small number of elements. In the section “Application to experimental microarray data” we illustrate the results predicted by both theory and simulation with data from 10 microarray experiments. At the end of this section, we provide an assessment of the bias of correlation coefficients estimated across a pool of heterogeneous groups of microarray data. We discuss our results and summarize our conclusions in the last section.In the “Methods” section of this article, we describe the statistical theory that we applied to analyze the components of Pearson correlation coefficients obtained from a pool of heterogeneous microarray groups. The “Simulation study” section provides results of a study that further tests the specific effect on Pearson correlation coefficients of only mean differences, and only heteroskedasticity across Hassler and Thadewald  developeN heterogeneous groups  is provided in Equation 1. For the purpose of applying their theoretical work to analyze correlation coefficients obtained from a pool of microarray data, we consider N heterogeneous groups of gene expression data measured through microarrays. Each group of data can be described as a matrix Mi of g genes by ni columns (each column of the matrix Mi corresponds to the expression of g genes measured through one microarray). We assume that expression levels of any given gene pair xy within each group, i.e. x,y ∈ Mi, are bivariate random normal variables that are identically distributed with means μxy,i =  and variance-covariance matrix i = 1,N. Therefore, heterogeneities across N groups of microarray data are characterized by μxy,i ≠ μxy,j and/or Σxy,i ≠ Σxy,i, for i ≠ j.The generalization of Hassler and Thadewald’s  asymptotx and y, rxy, obtained from a pool of N heterogeneous groups, as ni → ∞, is given by the expression in Equation 1:The limit in probability of the Pearson correlation coefficient between expressions of genes ni plus the weighted average of the square of mean differences across N groups for genes x and y, respectively (Equations 2 and 3). Hence, the limit in probability of gene-to-gene Pearson correlation coefficients obtained from combining heterogeneous groups of microarray data is a mixture of the weighted average of all covariances across N groups plus the weighted average of the cross product of the mean differences of genes x and y across N groups. Both terms are divided by a combination of the average of variances of genes x and y and the mean differences of genes x and y across N groups.where N groups under the following specific conditions: (a) occurrence of only mean differences across N > 2 groups and (b) occurrence of only heteroskedasticity across N > 2 groups. Our study also evaluated the validity of using estimates of the asymptotic expression given in Equation 1 to explain the components of the pooled correlation coefficient when groups comprising the pool of data have a small number of elements ni.This section presents results of a study using simulated data that had the purpose of further investigating correlation coefficients obtained from a pool of xy following the procedure detailed in 1–4: We performed simulations in R  for a gei, i = 1,N, generate ni data pairs from a multivariate normal distribution with parameters μxy,i and Σxy,i (we used the function mvrnorm in MASS [ni columns (number of elements ni).1. For each simulated group  in MASS ). Each sN groups into one pool of 2 rows (genes) by 2. Combine simulated data of 3. Obtain the Pearson correlation coefficient from the pool of data (we used the function cor in R ).4. Repeat steps 1–3 above 1000 times; results are presented as averages over 1000 repetitions.μxy,i =  and i = 1,10. This simulation provided nearly zero correlation coefficients (−0.004 ≤ rxy ≤ 0.003), thus reassuring us that our simulation procedure worked as expected.As a control, we first performed an experiment with parameters N groups of simulated data were due only to mean differences, i.e. μxy,i ≠ μxy,j for i ≠ j, but variance-covariances remained constant, i.e. Σxy,i = Σxy. We first simulated the case of zero correlation within each group and the effect of differing means (by a parameter α) in only two of the N groups. The simulation results for the set of parameters μxy,1 = , μxy,2 = , −10 ≤ α ≤ 10, and μxy,i =  for i ≥ 3; ni = 10, λi = λ, 0.01 ≤ λ ≤ 0.1, ∀ i = 1,N, and 10 ≤ N ≤ 100 are shown in Figure rxy shown in Figure α < 2, negative for α ≥ 2, and nearly zero for α = 2. Even though data pairs in each group were drawn from populations with zero correlations, rxy ranged from −0.56 to 0.48. Also shown in Figure rxy and α as well as between rxy and λ. Not surprisingly, coefficients rxy increase as λ increases. The smooth curves observed in Figure rxy with α and λ) are explained by the asymptotic formulation of Equation 1 as written for the set of population parameters used in this simulation case study (Equation 4):First, we analyzed the case in which heterogeneities across α = 2 and 0.01 ≤ λ ≤ 0.1; τxy > 0 for α < 2 because the term −4λ(1−2λ)(α−2) > 0 and dominates the term −λ2α2 < 0; τxy < 0 for α > 2 because −4λ(1−2λ)(α−2) < 0 and −λ2α2 < 0.Through Equation 4 one can see that xy differ for all N groups but the correlation ρxy ≠ 0 assumes the same value within each group. The simulation results for the set of parameters μxy,i = ), ρxy ≤ 0.9, ni = 10, λi = λ, 0.01 ≤ λ ≤ 0.1, for i = 1,N, and 10 ≤ N ≤ 100 are shown in Figure rxy shown in Figure rxy < −0.80, even when the true correlation within groups ρxy was positive. The asymptotic formulation of Equation 1 as written for the set of parameters used in this simulation case study is given in Equation 5:Secondly, we simulated the case in which the means of a gene pair τxy assumes values of nearly −1 for 10 ≤ N ≤ 100. A visualization of the problem considered in our second simulation case study is shown ), ρxy = 0.9, and ni = 50, for i = 1,10. As shown in Figure x and gene y within each of the 10 groups, the trend between expression of the gene pair xy across the pool of all groups is negative.Through Equation 5 one can see that n Figure b throughN groups, keeping μxy,i = μ constant. Simulation of the case where σxy,i = 0 but i ≠ j resulted in nearly zero correlation coefficients from the pool of N groups (−0.04 ≤ rxy ≤ 0.05). This result agrees with Equation 1, which predicts zero correlation if covariances and mean differences of a gene pair across all groups are zero, even when variances differ across groups.Through simulation, we analyzed the effect of variations in x changes across N groups, but the variance of gene y and the correlation between genes x and y remain constant across N groups. Results of this experiment for the set of parameters μxy,i = , ρ ≤ 0.9, ni = 10, λi = λ, and 0.01 ≤ λ ≤ 0.1 for i = 1,N and 10 ≤ N ≤ 100 are shown in Figure rxy ≤ 0.8  is greater than the mean squared error between ρ. Therefore, pools of N simulated groups marked by only heteroskedasticity provide less efficient estimates of the correlation across groups than combining the N groups’ correlation coefficients into an average.There is also a linear relationship between the average of correlation coefficients within each group ρ Figure , in whicτxy and ni, the number of simulated elements in each group, for 10 ≤ ni ≤ 100 and N = 20  from AtGenExpress . Cel filet al.[et al.[rxy) were obtained from the large expression matrix (pooled data) with the R function cor. [We imported data from cel files into the R environment  and procet al., we did et al. were alsl.[et al.. As descl.[et al., all datl.[et al.,31. We oion cor. .ni, for groups i = 1,19, are given in Table ni correspond to the number of mean expression values of a gene within group i).We used estimates of the asymptotic expression given in Equation 1 to examine the makeup of Pearson correlation coefficients obtained from pooling the means of biological replicates of different experimental conditions into one large expression matrix. In order to accomplish this task, we classified data in columns of the large expression matrix into 19 groups. Each group had gene expression values from either root or shoot in each of nine types of abiotic stress treatments  can explain the signs and magnitudes of rxy, the Pearson correlation coefficient obtained from the large expression matrix.4. Small residual errors indicate good agreement between We first obtained Pearson correlation coefficients for pairwise combinations of all 22,810 genes present in the large expression matrix, which resulted in more than 260 million coefficients. The Pearson correlation coefficients ranged from −0.992 to 0.998 with 0.008 as the mean value, and coefficients showed a symmetric distribution around zero; roughly 10% of these coefficients were greater than 0.7 or less than −0.7  ranged from −0.979 to 0.990 with 0.007 as the mean value, and coefficients showed a symmetric distribution around zero; roughly 10% of these coefficients were greater than 0.7 or less than −0.7, as shown in the histogram of Figure rxy /(2n) [ρ(1−ρ2)/(2n) is maximized as ρ assumes a value around ±0.58.Because all genes present in the large expression matrix provide more than 260 million pairwise correlation coefficients, we used a subset of 500 randomly selected genes and all their 124,750 pairwise correlation coefficients to illustrate potential problems with gene pairwise correlation coefficients estimated from a pool of microarray data. Pearson correlation coefficients from the pooled expression data (ρ2)/(2n) ,33. The rxy andni ≤ 18, whereas Hassler and Thadewald’s example data set had around 90 elements in each of two groups [The analysis of all 124,750 pairwise correlations of 500 randomly selected genes revealed good agreement between rxy, the Pearson correlation coefficients obtained from the large expression matrix. The plot of rxy versusrxy ranges from −1 to +1 for negative and positive values ofrxy versus dxy  of a gene pair across the 19 groups are the sole determinant of the sign of rxy, i.e. dxy > 0 ⇒ rxy > 0 and dxy < 0 ⇒ rxy < 0. The magnitude of rxy is due mostly to mean differences because the correlation between rxy andrxy andNext, we show the influence of each term of Equation 7 on signs and magnitudes of y Figure a shows ty Figure b indicatrxy| > 0.7 obtained from pools of microarray data has been used as the cut-off value representing a strong association between gene pairs [rxy| ≥ 0.7, i.e.rxy < −0.7 and rxy > 0.7 categories. The median of% with 50% of the data showing values between 0.04% and 5.32%. Conversely, the median of% with 50% of the data showing values between 93.51% and 98.98%.Because |ne pairs -23, we crxy andA combination of correlation coefficients between expression profiles within each group, given byBy applying the asymptotic theory developed by Hassler and Thadewald  to the Pxy,i ≠ Σxy,j for i ≠ j. In this case, Equation 1 can be written asHere, we examine the case in which Pearson correlation coefficients are obtained from a pool of microarray data in which only gene pairwise variances-covariances differ across groups of data, i.e. Σet al.[For instance, heteroskedasticity could occur in a situation in which data from completely replicated microarray experiments are pooled to be examined as one data set. As was reported in the work of Goldstein et al., data vai = 1,19) and obtained their residuals. Following the methodology for differential expression of genes proposed by Smith [i, here represented by a matrix Yi, with a systematic treatment effect (a linear model represented by Ziβi) plus error, i.e.In order to attain only heteroskedasticity across the 19 groups of our example data set, we removed the effect of varied experimental conditions on expressions of genes within each group. For this purpose, we fitted linear models to genes , where Σi is the variance-covariance matrix of all genes in each group i = 1,19. We obtained residuals asfor wherege Limma ,31. Thisge Limma ,31.We combined all gene expression residuals from the 19 groups into one pool of residuals . Expression levels of the two treatment conditions “genotoxic stress applied to root 1 hour post-treatment” and “heat control applied to shoot 24 hours post-treatment” could not be used in the analysis of residuals because they had only one biological replicate  for the same subset of 500 genes used in the analysis of the large expression matrix. Pearson correlation coefficients i = 1,19 groups comprising the large matrix of residuals. For the large expression matrix,B(rxy),We evaluated the bias (as defined in Equation 15) of each of the 124,750 gene pairs’ correlation coefficients that were obtained according to: (a)B(rxy) than did those ofB(rxy) is twice as much the maximum value ofThe analysis involving the data in the large expression matrix (whose heterogeneities were due to means and variances-covariances differences across 19 groups) resulted in consistently larger statistical summaries of y) Table . For theB(rxy) versus rxy  increases as |rxy| approaches ±1, is very distinct from that shown in the plot of B(rxy) versusB(rxy) decreases asB(rxy) for |rxy| > 0.7 is 0.18, whereas the mean value ofMoreover, more information can be grasped through the visualization of biases versus coefficients, as shown in Figures y Figure a, where y Figure b, where , ∀The plot of) Figure  reveals In this study, we performed a comprehensive analysis of Pearson correlation coefficients obtained from combining data of 19 heterogeneous groups of experimental microarray data into one large pool. By applying the theory developed by Hassler and Thadewald [We quantified the bias of the correlation coefficient of a gene pair through the mean squared error between its estimate across a pool of groups and its estimates within groups. A similar method has been used by Hunter and Schmidt to assess the variance of their meta-analysis estimator of the Pearson correlation coefficient across independent studies . We evalet al.[et al.’s study [The data used in this study came from 10 microarray experiments (AtGenExpress Project ) carriedet al.. Experimet al.. Mean di’s study . We expeThe analysis of the components of the correlation coefficients obtained from the large expression matrix corroborated the results predicted by both theory and simulation that variances-covariances within the 19 groups had negligible impact on correlation coefficients, but different means across the 19 groups had a decisive effect on the sign as well as on the magnitude of coefficients. Coefficients that were greater than 0.7 or less than −0.7 showed the largest range of bias Table . TherefoN ≤ 100 groups could generate Simpson’s paradox. In our case study simulation shown in Figure N (10 ≤ N ≤ 100) groups provided negative correlation coefficients because the combination of all pairwise mean differences had a negative sign and greater magnitude than the positive covariance of the data within groups. Hassler and Thadewald [N = 2, and showed that the occurrence of mean differences with opposite signs in both correlated variables is a condition for contradictory results between a correlation coefficient that is estimated across or within each of two groups.We also showed through Monte Carlo simulation that the structure of different means across a pool of 10 ≤ hadewald  studied xy,i, i = 1,19. The large matrix of residuals gave us the opportunity to evaluate gene pair correlations estimated from a pool of data marked by only heteroskedasticity. Our results showed that correlation coefficients estimated across the 19 groups of residuals were closely related to the variance-covariances within groups. We also found a strong linear relationship between the Pearson correlation coefficients obtained from the large matrix of residuals and the coefficients resulting from averaging correlation estimates within groups. However, the heteroskedasticity of the data in the large matrix of residuals resulted in less efficient estimations of the correlation between a gene pair than did the classical meta-analysis approach of combining correlation coefficients into an average. These results were corroborated by Monte Carlo simulations of only heteroskedasticity across N > 2 groups of data.We combined residuals from fitting linear models of every gene into a large matrix of residuals . Here we departed from the assumption of independence  and The results shown in this study indicate that the combination of statistical results is best suited for estimating correlations of a gene pair across several microarray studies. Nevertheless, further studies are necessary to assess various methods of combining within-groups gene-to-gene correlation coefficients.This study demonstrates three aspects of the importance of statistical methods in the synthesis of information across microarray experiments: (A) Large values of gene-to-gene Pearson correlation coefficients estimated from a pool of 19 groups of microarray data were an ecological fallacy; the effect of heterogeneous means across a pool of data overpowers the covariance structure of the data.(B) The effect of heterogeneous variance-covariances across a pool of data causes less efficient estimates of Pearson correlation coefficients across groups of microarray data than does the approach of combining correlation coefficients of individual groups.(C) The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies.The authors declare that they have no competing interests.MMAM elaborated the project idea, compiled the example data set, performed the statistical analysis, and wrote major parts of the paper. NR downloaded the cel files from TAIR. YF and JH contributed to the project idea and editing of the manuscript. ESW wrote parts of the paper and provided the biological input. All authors read and approved the final manuscript.This file contains additional figures detailing results.Click here for fileThis file gives detailed information about treatment conditions and number of biological replicates in each of the 10 microarray experiments of our example data set.Click here for fileThis file gives the R function that estimates,, dxy, dx and dy.Click here for file


Esophageal squamous cell cancer (ESCC) is one of the most common fatal human cancers. The identification of biomarkers for early detection could be a promising strategy to decrease mortality. Previous studies utilized microarray techniques to identify more than one hundred genes; however, it is desirable to identify a small set of biomarkers for clinical use. This study proposes a sequential forward feature selection algorithm to design decision tree models for discriminating ESCC from normal tissues. Two potential biomarkers of RUVBL1 and CNIH were identified and validated based on two public available microarray datasets. To test the discrimination ability of the two biomarkers, 17 pairs of expression profiles of ESCC and normal tissues from Taiwanese male patients were measured by using microarray techniques. The classification accuracies of the two biomarkers in all three datasets were higher than 90%. Interpretable decision tree models were constructed to analyze expression patterns of the two biomarkers. RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas. Esophageal cancer is the sixth most common fatal human cancer in the world . The hisEarly detection of ESCC could be a promising strategy to decrease mortality. Microarray techniques are extensively utilized to measure expression levels of a large number of genes simultaneously and provide better understanding of the molecular mechanism of ESCC carcinogenesis. The microarray expression data could be analyzed to identify and give insights into clinical biomarkers of ESCC for detection. Several efforts have been made to study gene expression profiles and differential expressed genes for discovering biomarkers using microarray techniques –7. UsualThe incorporation of classification and feature selection algorithms has been widely used to identify promising features for various classification problems such as ubiquitylation sites , immunogFor the application of biomarkers for detecting ESCC, the simple decision tree methods capable of generating human interpretable rules were chosen instead of the black-box methods such as support vector machines (SVM). In this study, a sequential forward feature selection algorithm is proposed to identify genes best for decision tree classifications that is capable of selecting a small set of biomarkers with human interpretable rules. Two public available microarray datasets obtained from Gene Expression Omnibus (GEO) database  are utilhttp://www.bioconductor.org/. There are 18,400 transcripts and variants in Affymetrix Human U133 set, including approximately 14,500 well-characterized human genes in greater than 22,000 probe sets and 500,000 distinct oligonucleotide features. GSE23400 consisting of 53 ESCC and 53 normal tissues from patients was utilized to identify genomic biomarkers for ESCC. The GSE20347 consisting of 17 ESCC and 17 normal tissues was applied to validate the identified genomic biomarkers.In order to identify and validate genomic biomarkers for ESCC, two microarray datasets of GSE23400  and GSE2We selected 17 incident male ESCC patients who regularly consumed tobacco and alcoholic beverage to validate the candidate biomarkers obtained from the above two datasets. All of them underwent total esophagectomy in Kaohsiung Medical University Hospital. One pair of resected tumor and adjacent normal tissue for each patient was immediately put into a portable container with dry ice and then transferred and maintained in a nitrogen tank until analysis. After review by a qualified pathologist (Dr. CC Wu), the tumor parts were found to have cancer cells in >80% of the tissues, whereas the normal parts were microscopically tumor-free. This study was in compliance with the Helsinki Declaration and approved by the internal review board of KMUH. All patients provided their written informed consent.Total RNA from each pair was isolated by a single-step guanidinium isothiocyanate method using the Trizol Reagent total RNA Purification Kit  according to the manufacturer's instructions. The yield and quality of RNA were assessed by spectrophotometry and the Agilent 2100 Bioanalyzer . All paired samples had an A260/A280 between 1.8 and 2.2 and A260/A230 ratio above 1.0 and were eligible for the subsequent array experiment.μg total RNA was placed in a microcentrifuge tube and incubated at 70°C for 10 minutes in a thermocycler . After denaturing secondary structure of RNA, the thermocycler was rapidly set at 4°C waiting for the next step. During the RNA denatured process, the reaction components were prepared according to the manufacturer's guideline. The final concentrations of reaction component were 5 mM MgCl2; 1X reverse transcription buffer (10 mM tris-HCl (pH 9.0 at 25°C); 50 mM KCl; 0.1% triton X-100); 1 mM each dNTP; 1 u/μL recombinant RNasin ribonuclease inhibitor; 15 u/μg AMV reverse transcriptase (high conc.); and 0.5 μg oligo (dT)15. The reaction mixture was loaded into a microcentrifuge tube mixed with total RNA and incubated at 42°C for 15 minutes. Subsequently, the reaction mixture was heated to 95°C for 5 minutes for inactivating AMV reverse transcriptase and stored the first-strand cDNA at −20°C until analysis.After RNA isolation, cDNA was prepared from each sample by Reverse Transcription System . For the appropriate efficiency of reverse transcription, 1 Human oligonucleotide DNA microarrays (Human Whole Genome OneArray) from Phalanx Biotech Group  were used. The Human Whole Genome OneArray  contains 32,050 60-mer oligonucleotide probes, including 28,703 probes corresponding to the annotated genes in Unigene v175 and RefSeq database, 2,265 experimentally defined probes, and 1,082 control probes.The detailed experimental method is described elsewhere . Each cDSpots in each array with foreground median intensity of wavelength 532 nm greater than or equal to that of background median intensity plus 3-fold standard deviation of wavelength 532 nm were considered as the “Present” flag and included for the further analysis. In order to evaluate the quality of each array in the entire array experiment, three evaluation steps were performed: basic, reproducible, and diagram. In the basic step, three parameters, including percentage of “Present” spots among all spots, the average intensity of “Present” spots, and coefficient of variation of intensity for control spots in the entire arrays, were all considered. If any two parameters in one array were located outside the 1.5-folds interquartile range (25th–75th) of the same parameters for all arrays, that array was excluded. The remaining arrays were then evaluated in reproducible steps which the repeated arrays of the same sample would pass, when their Pearson's correlation coefficient was larger than 0.95 and “2-fold percentage” was less than 15%. The “2-fold percentage” was the percentage of probes among all probes in which the ratio of the same probe between two arrays exceeded 2-fold. In the final diagram step, the density plot of repeated arrays was used to examine the intensity profile of each array. An array would pass if the profile was similar to the rest of arrays in the same phenotype groups. When the arrays passed all three steps, the raw intensity of spots was log-2 transformed for subsequent analysis. To adjust the systematic variation of experiments and dye effects, global Lowess normalizations were performed within repeated arrays of the same sample and between the samples. Spot was included for further analysis when it was “Present” in at least one of the qualified arrays.Decision tree algorithms are useful methods to generate interpretable rules based on gene expressions for ESCC classification that are widely used in various classification and regression problems such as immunogenic peptides , promoteThere are more than forty thousand probes in a microarray experiments. The selection of informative probes for discriminating between ESCC and normal tissues is a crucial step for biomarker identification. Although the decision tree algorithm J48 has a built-in function for feature selection, the incorporation of various feature selection algorithms could generate decision trees with higher classification accuracy , 23.n, n − 1 sample is utilized to train a decision tree classifier and the remaining one sample is utilized to validate the decision tree classifier for each run of LOOCV. The accuracy of LOOCV is calculated by averaging the n validation accuracies. Given an empty pool of selected probes S, the SFFS algorithm iteratively selects informative probes into S as shown as follows. First, the LOOCV accuracy is evaluated for each probe. Second, the best probes with highest LOOCV accuracy are appended into S. Third, for each remaining probe p, its LOOCV accuracy is evaluated by using p and probes in S. Fourth, the second and third steps are repeated until the termination criteria are satisfied. The resulting probe set S consists of the final biomarkers.In this study, a sequential forward feature selection algorithm (SFFS) is proposed to identify useful biomarkers for discriminating between ESCC and normal tissues. The selection process is based on the accuracy of leave-one-out cross-validation (LOOCV) using the decision tree algorithm J48. Given a dataset of sample size The SFFS algorithm utilizes the greedy selection strategy under the property monotonic assumption. In contrast to univariate feature selection methods, the SFFS algorithm considering the interaction effects of sequential selected probes on the accuracy is expected to perform better. The SFFS algorithm is only applied to training dataset to identify potential biomarkers.To evaluate classifiers for their prediction performance, the leave-one-out cross-validation method is applied as it is widely used as an objective evaluation method for error rate estimation , 9, 24. t-test with multiple test correction of Benjamini and Hochberg is applied to calculate its corresponding t-statistic and adjusted P value using GEO2R [To identify potential biomarkers for ESCC, a microarray dataset GSE23400 is fetched from GEO database for the following analysis. GSE23400 consists of 53 pairs of ESCC and adjacent normal tissue from 53 patients in China. For each probe, ng GEO2R . The corng GEO2R .P values smaller than 0.001 and overexpressed in tumor tissue were selected for subsequent analysis. A total of 3,910 probes were obtained by the above criteria.Because overexpressed genes are more useful for clinical diagnosis than downexpressed genes, only significantly differential expressed probes with adjusted To identify potential biomarkers for discriminating ESCC from adjacent normal tissues, a sequential forward feature selection (SFFS) algorithm is proposed to determine the best probe set giving the highest leave-one-out cross-validation (LOOCV) accuracy using a decision tree algorithm J48. By applying the proposed SFFS algorithm, two probes giving the highest LOOCV accuracy were selected as potential biomarkers whose gene names are RUVBL1 and CNIH.The selection process of SFFS is shown in RUVBL1 alone can be utilized to discriminate ESCC from normal tissues. In contrast, CNIH alone is not suitable for this purpose with an LOOCV accuracy of 75.47%. However, the combined use of RUVBL1 and CNIH provides the best LOOCV accuracy. The decision tree models trained on the whole dataset GSE23400 for RUVBL1, CNIH, and both of RUVBL1 and CNIH are shown in To externally validate the two potential biomarkers of RUVBL1 and CNIH, another dataset GSE20347 fetched from GEO database consisting of 17 pairs of ESCC and normal tissues from patients in China. LOOCV is applied to GSE20347 dataset to validate the discrimination ability of RUVBL1 and CNIH for ESCC. The use of RUVBL1 yields a high accuracy of 97.06% in GSE20347 that is consistent with that in GSE23400. The same accuracy obtained from 10-CV demonstrates the usefulness of the biomarkers. However, CNIH alone failed to discriminate ESCC from adjacent normal tissues with an LOOCV accuracy of 44.18%. By using both genes of RUVBL1 and CNIH, the accuracy of the decision tree model remains unchanged.Figures After the identification and validation of the two biomarkers from the two public available datasets, a total of 34 gene expression profiles from 17 pairs of matched tumor and adjacent normal tissues were measured and collected to test the discriminating ability of the two biomarkers for ESCC. The 34 profiles are generated by using Human Whole Genome OneArray  that is different from the two datasets generated by using Affymetrix U133A chips.The LOOCV accuracy using RUVBL1 was firstly evaluated. The sensitivity, specificity, and accuracy are 94.12%, 76.47%, and 85.29%, respectively. The performances using both genes of RUVBL1 and CNIH are 88.24%, 94.12%, and 91.18% for sensitivity, specificity, and accuracy, respectively. Results show that performances can be improved by incorporating CNIH. The LOOCV and 10-CV accuracies are exactly the same. The improvement is consistent with that in GSE23400. A comparison of classification accuracies on three datasets is shown in The decision tree models trained on the 34 profiles using RUVBL1 and both genes of RUVBL1 and CNIH are shown in Figures This study proposed a feature selection-based method to discover a small subset of genes to discriminate ESCC from normal tissues. The method was based on a sequential forward feature selection algorithm to design decision tree models for classifying expression profiles of ESCC and normal tissues. Two genes of RUVBL1 and CNIH were discovered with a high LOOCV accuracy of 99.06% in a published dataset GSE23400 (available at GEO database) consisting of 53 pairs of ESCC and normal tissues. The gene set has been validated in another dataset GSE20347 consisting of 17 pairs of ESCC and normal tissues whose platform is the same as GSE23400. A high LOOCV accuracy of 97.06% for GSE20347 shows the discrimination ability of the two genes.To further test the two genes, microarray techniques were applied to measure gene expression profiles of Taiwanese patients. The dataset consists of 17 pairs of ESCC and normal tissues. An LOOCV accuracy of 91.18% obtained by using RUVBL1 and CNIH shows their potential as biomarkers for ESCC. Each gene alone performs worse in datasets of GSE23400 and our dataset. It suggests that the two genes should be used simultaneously to obtain the best performance. The 10-CV accuracies demonstrate that the performance remains the same when less training samples are available.The relationship between the two newly identified biomarkers of RUVBL1 and CNIH genes and ESCC has not been reported. The decision tree models show that RUVBL1 is overexpressed in all three datasets of patients in China and Taiwan. However, CNIH is over- and downexpressed in datasets of patients in China (GSE23400) and Taiwan, respectively. For GSE20347 of patients in China, CNIH is neither over- nor downexpressed.RUVBL1 plays important roles in chromatin remodeling, transcriptional and developmental regulation, DNA repair, and apoptosis. RUVBL1 is ubiquitously and highly expressed in thymus and testis –30 and iMus musculus [Homo sapiens [Homo sapiens [CNIH is involved in the selective transport and maturation of TGF-alpha family proteins. There is no research reporting the relationship between CNIH gene and cancers. Interestingly, the expression of CNIH mRNA is affected by tetrachlorodibenzodioxin , 42–44. musculus , 44 and  sapiens . Cigaret sapiens . Also, k sapiens  and is a sapiens . The dif sapiens . Moreove sapiens . The divIn conclusion, using feature selection and decision tree models from two public available microarray datasets, we found that two genes (RUVBL1 and CNIH), particularly RUVBL1, could be useful biomarkers in the clinic for discriminating cancer and normal tissues in Taiwanese ESCC patients. The collection of a larger dataset for independent test could further validate the robustness of the two biomarkers. A future work to study the mechanism of these two genes in the carcinogenesis of ESCC is necessary.


Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations. Breast cancer is the most common type of cancer in women and the second-leading cause of cancer death among women in the United States. A molecular biomarker that can predict the likelihood of cancer progression to invasive or metastatic disease can guide how aggressively patients are initially treated The introduction of genome-scale gene expression profiling has led to the identification of specific transcriptional biomarkers known as gene expression signatures. The discovery of gene expression signatures from any single well-powered study is relatively straightforward. Some signatures have utility as transcriptional biomarkers for classifying patients with significantly different survival outcomes in breast cancer With hundreds of breast cancer gene expression datasets deposited in public databases, we now have the ability to utilize these data to their full potential and discover recurrent and reliable gene expression signatures for breast cancer prognosis prediction. However, the identification of a prognostic expression signature through meta-analysis of publicly available cancer gene expression profiles represents an underexploited opportunity. There are several reports of meta-analysis frameworks that use multiple breast cancer datasets to build and validate prognostic classifiers EXpression AnaLysis Tool) To avoid the weaknesses of single study-derived signatures and to generate a new strategy to better utilize the available gene expression data from independent studies, we have developed a meta-analysis strategy called EXALT  To organize the complex transcriptional data, we have established a hierarchical data structure. The top level consists of the transcriptional studies, and each transcriptional study was partitioned into three levels: data sets, groups, and samples. A study can include one or many data sets depending on its experimental design A gene expression signature (“simple signature”) as defined by EXALT is a set of significant genes with their corresponding statistical scores and gene expression direction codes (up or down). Some “simple signatures” are biologically related to breast cancer prognosis, but they were derived from individual transcription profiling studies and are all too often underpowered, truncated, or of low quality. There are inherent limitations for any individual profiling study including small sample size relative to the large number of potential predictors, limitations of technological platforms, sample variation, and bioinformatics or statistical method bias. An underlying assumption we made in formulating this approach is that any individual transcriptional profiling study does not decode an entire expression signature. Rather, these “simple signatures” represent only fragments of a complete and common transcriptional profile (meta-signature).We hypothesized that a meta-signature with improved predictive power could be discovered by data similarity-driven meta-analysis of transcriptional profiles from multiple related studies. EXALT analysis provided the basis for grouping or clustering “simple signatures” sharing significant data similarity. The iterative EXALT process gathered homologous signatures from “simple signatures” and consolidated them into meta-signatures  to query all “simple signatures” in HuCaSigDB repeatedly and to bring other homologous signatures together by their common elements . This iterative process “grouped” or “clustered” signatures based on their similarities  were assembled into a synthetic signature designated as BRmet. The genes within BRmet were ranked based on recurrent frequency and direction of differential expression (meta-direction) among all 11 signatures. A 100% recurrent frequency was applied to select the top 50 genes for the meta-signature (BRmet50) . BRmet50Annotation for BRmet50 genes is provided in Since the BRmet50 was deduced from a signature cluster comparing more and less aggressive cancers, we retrospectively examined the ability of BRmet50 to predict prognosis in 21 datasets, including 11 independent validation datasets not used in the signature clustering process .To examine the stability of the iterative EXALT method and to avoid over-fitting of the nine training datasets, we used a ‘leave-one-out’ cross-validation strategy to deduce nine BRmet50 control signatures for the corresponding nine training datasets. In each leave-one-out trial, the included signatures remained clustered. Furthermore, all BRmet50 control signatures from the ‘leave-one-out’ procedure shared the core set of the 50 genes. We then tested these control meta-signatures in corresponding training datasets  and founThe 11 independent validation datasets were used to evaluate BRmet50 prognosis performance. Log-rank tests were conducted to assess the differences in survival analysis. The p-values from log-rank tests comparing BRmet50, BRsig70, BRsig76, and the six other published cancer signatures  are summarized . Each sip<0.05). Among patients for whom BRmet50 predicted a good prognosis, the 10-year rate of relapse-free survival was 79% versus only 47% among those with a poor prognosis  . The res-value: 0.002) was better than those of BRsig70 and BRsig76  when examining the BR1042 dataset. Our results indicate that the predictive power of BRmet50 is robust and applicable across a wide range of independent datasets.The performance of BRmet50  using randomly selected genes from the human genome. All random signatures were tested in the same panel of 21 test datasets. After 1,000 random permutations of the gene signatures, the p-value distribution  from each test dataset was generated, and p-values from BRmet50 and the six other published cancer signatures were also plotted on the X-axis of the distribution plots  and S3.p<0.05) associated with breast cancer outcomes in various datasets, the associations are stronger for the seven breast cancer signatures in more than half of the test datasets. These control results provide valid statistical support for their prognosis relevance. Furthermore, we noticed that most p-values from BRmet50 were on the far right side of the random p-value distributions (p<0.05).Although some random signatures are significantly . Furthermore, the BRmet50 could segregate tumors with intermediate differentiation or ER-positive into good and poor prognostic subcategories  but not for those that were ER-negative. Neither BRsig70 nor BRsig76 was capable of stratifying tumors with either good or poor differentiation in any subset of BR1141 except tamoxifen treatment subset  represented 1,183 tumors and had data on a common set of clinicopathologic characteristics including tumor size, grade, lymph node status, and Nottingham Prognostic Index (NPI) Univariate Cox proportional-hazards analysis demonstrated that BRsig70, BRsig76, or any individual common prognostic factor  could not successfully predict cancer prognoses in all five datasets. However, BRmet50 was uniquely able to significantly differentiate tumor samples into two prognostic groups in all five validation sets. The prognostic value of BRmet50 was greater than each of the established risk factors . For exap<0.05) were observed in all five test datasets between BRmet50 and patient relapse-free or disease-free time after adjustment for standard clinical covariates. Thus, BRmet50 contributed new and important prognostic information beyond that provided by established clinical predictors. For the most part, BRsig70 and BRsig76 showed no significant associations in these analyses.Multivariate Cox proportional-hazards analysis was used to determine if BRmet50, BRsig70, or BRsig76 added independent prognostic information to other standard clinicopathological features. In this multivariate Cox proportional-hazards analysis , signifiBecause BRmet50 successfully predicted breast cancer prognosis and because some molecular oncogenic events are conserved among multiple cancer types p<0.01), suggesting that transcriptional profiles for poor-prognosis may be more conserved in breast, lung, and prostate cancer. In the lung cancer dataset, the good prognosis groups predicted by BRmet50 had the highest relapse-free survival (>40% and p<0.01) among the 3 signatures. We also determined whether the association between the three signatures and the clinical outcomes in patients with prostate, lung, and colon cancer was independent of established clinical and pathological criteria  criteria . The resData generated by high-throughput transcriptional studies of cancer has rapidly accumulated and there is increasing interest in translating this information into clinical value. Although single-study analysis can be informative, it is often affected by inherent limitations. These limitations can be overcome by combining related independent studies into a meta-analysis. Our study demonstrated that heterogeneous signatures from individual cancer studies can be systematically organized into a meta-signature (BRmet50) based on their intrinsic data similarities by a novel meta-analysis strategy (iterative EXALT). This meta-analysis approach can increase statistical power, minimize false discovery, reduce batch effects, and improve the generalizability of the findings. The value of the BRmet50 signature was evaluated in terms of predicting prognoses in breast and other cancers.data combination and data integration methods. The data combination method is a comprehensive reanalysis of the primary data by merging data from multiple studies data integration method compares gene lists from any expression platform filtered according to p-values or rank combination There are two strategies for meta-analysis of transcriptional datasets: Iterative EXALT helped us understand the relationship between the intrinsic signature data similarities and signature-associated phenotypes. When the clustered signature phenotypes in The power of the iterative EXALT is illustrated in the identification of a meta-signature (BRmet50). In our meta-validation of BRmet50, we found that distinct gene expression signatures have a common significant predictive value in more than half of the breast cancer studies  and S3. p<0.05. However, for the majority of the other validation datasets, outcome association of BRmet50 and most published cancer signatures showed stronger associations than the median of random signatures. On average, the association of BRmet50 with disease outcome was stronger than that of the top 5% random signatures (A previous report gnatures  and S3.p<0.05) in a small number of test datasets, but it is still very difficult for a random signature to repeatedly yield significant results by chance in a majority of 21 test datasets. Out of 1,000 random signatures, there were only 13 (1.3% of random signatures) that generated significant predictions (p<0.05) from more than 10 out of 21-test datasets (>50%). However, for the same 21 validation datasets, 100% of the tests of BRmet50 and more than 50% of the tests of the six other known cancer signatures were significant. Abiding to this criterion, the probability that a random signature achieves the similar level of performance as the BRmet50 by chance is low (p<0.013). Clearly, our study emphasizes the importance of large-scale validation tests.One important observation from this large scale validation of results is that a random signature may produce significant outcome associations (A 21-gene signature (Oncotype DX) is a diagnostic test that quantifies the likelihood of relapse of tamoxifen-treated, lymph node-negative breast cancer using a recurrence score method p<0.001) but not for those with ER-negative status or those with high-grade , suggesting that different cancers may share common transcriptional elements that influence their clinical behaviors. Additional prospective studies will be valuable in determining the clinical value of BRmet50 in breast cancer patient subsets and other cancers.The methods used for signature extraction, developing the signature databases, and EXALT analysis were previously reported Patient information, both clinical data and gene expression data for signature identification and validation, were obtained from independently published human cancer studies and the Gene Expression Omnibus (GEO) provided by the National Center for Biotechnology Information (NCBI) To ensure quality in the test survival data sets derived from published breast cancer studies, we applied the “rule of fifty” www.r-project.org).Our statistical approach, as illustrated in For general prognosis performance evaluation of various signatures in full datasets and subsets, p-values from log-rank tests and from univariate and multivariate Cox proportional hazard models were evaluated. Various disease outcomes  were used as clinical end points . The estFigure S1Flow chart of statistical methods for validation. Four types of signatures were used in this study: (1) BRmet50; (2) BRmet50 control signatures from “Leave-one-out” process; (3) BRsig70, BRsig76 and other six known signatures for breast cancer prognosis; and (4) 1,000 random signatures of identical in size to BRmet50. Gene expression signatures were used for unsupervised hierarchical clustering. Sample group assignments were determined in each data set based on the sample clustering dendrogram. Gene expression-based sample groups together with patient survival data and clinicopathological variables in cancer were used to determine the signature prognostic performance in survival analyses. The survival analyses include log-rank tests and Cox proportional hazards regression models (univariate and multivariate models). All signatures were validated in 21 breast cancer (BR) data sets Click here for additional data file.Figure S2Comparison of cancer signatures and random signatures (part 1). 12 datasets were tested individually with 1,000 random signatures and seven known cancer signatures. Each panel is labeled with its respective test dataset ID and depicts the distribution of p-values from1,000 random signatures identical in size to BRmet50 (50 genes). The x-axis denotes the reciprocal logarithm of p-value  from survival analyses. Colored arrowheads represent the seven known cancer signatures and point to the p-value locations in the random p-value distributions.(TIFF)Click here for additional data file.Figure S3Comparison of cancer signatures and random signatures (part 2). Nine datasets were tested individually with 1,000 random signatures and seven known cancer signatures. Each panel is labeled with its respective test dataset ID and depicts the distribution of p-values from1,000 random signatures identical in size to BRmet50 (50 genes). The x-axis denotes the reciprocal logarithm of p-value  from survival analyses. Colored arrowheads represent the seven known cancer signatures and point to the p-value locations in the random p-value distributions.(TIFF)Click here for additional data file.Table S1Breast cancer dataset source and derived signature phenotypes.(DOCX)Click here for additional data file.Table S2List of breast cancer meta-signatures.(DOCX)Click here for additional data file.Table S3Annotation of genes in BRmet50.(DOCX)Click here for additional data file.Table S4Hazard ratio risks and log-rank tests in BR1141.(DOCX)Click here for additional data file.Table S5Comparison of signatures with common clinicopathologic factors by univariate hazard ratio model.(DOCX)Click here for additional data file.Table S6Multivariate analysis of relapse risk among patients with breast cancer.(DOCX)Click here for additional data file.Methods S1Supplemental methods.(DOC)Click here for additional data file.