Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance
[['Since their inception over 13 years ago [1] , DNA microarrays have become a staple experimental tool used primarily for exploring the effects of biological interventions on gene expression .', 'Microarrays have enabled a range of experimental queries , including a survey of gene expression across dozens of mammalian tissues [2] , a comparison of human cancers in over 2000 tumor samples [3] , and the identification of differentially expressed ( DE ) genes between pairs of conditions .', 'Identifying DE genes is especially common , as it is often the first means of characterizing differences between two poorly understood conditions .', 'As of 2009 , there are publicly available microarray data for human conditions ( at the Gene Expression Omnibus [4] ) .', 'These data make possible a huge number of pairwise comparisons for DE gene analysis .', 'Given this sizable opportunity for biological discovery , we focus our attention on the task of DE gene identification .', 'Microarrays are notorious for generating noisy or irreproducible data [5]–[8] .', 'This is partially due to the inherent technical noise of the experiment , which can be modeled and often removed from the resulting data .', 'However , biological noise also plays a significant role , and effects of this noise source are not as easily corrected [9] .', 'A common solution to biological noise involves replicating the experiment many times in order to “average out” noise effects .', 'In the context of DE gene prediction , we define a replicate as a biologically independent comparison of RNA levels between the experimental conditions of interest .', 'Unfortunately , assay cost and a limited supply of biological material often limit the efficacy of a replication-based strategy .', 'To circumvent these difficulties , we need analytical methods which increase DE gene prediction accuracy at no additional cost .', 'One inexpensive source of microarray replicates comes from prior experiments .', 'In the last decade , researchers have generated data from hundreds of thousands of microarrays , and many of these are publicly available at repositories like the Gene Expression Omnibus ( GEO ) .', 'It is unlikely that any of these arrays ( hereafter referred to as “knowledge” ) represent exact replicates of data from a novel study ( referred to as “data” ) , but a subset of these experiments may describe similar underlying biology and could be considered “partial replicates” .', 'Because it is not clear a priori which of the prior experiments ( if any ) would qualify as partial replicates , pre-existing microarray knowledge cannot be used directly to identify DE genes in a novel dataset .', 'It is therefore worth considering indirect methods for using this knowledge .', 'Two previously existing methods use microarray knowledge to compute more accurate variance estimates for each gene [10] , [11] .', 'Both methods replace sample variance estimates for each gene by gene-specific variances calculated across a compendium of microarrays from GEO .', 'This approach was shown to be most useful with small data sample sizes , and no further benefits were seen when the microarray knowledge exceeded arrays .', 'A different approach might involve identifying transcriptional modules: groups of genes that exhibit coordinated or correlated expression changes across a range of conditions .', 'A complete and accurate understanding of module structure would reveal expression dependencies between genes , such that on average , genes in the same module would be coexpressed more often than genes chosen at random .', "Thus , knowledge of one gene's expression would confer information about the expression of the other genes in the module .", 'Several studies [12]–[18] have used microarray knowledge to identify transcriptional modules .', 'Of these , five have been tested on yeast datasets of 1000 arrays or fewer [12]–[14] , [16] , [17] and one has been applied to human cancer datasets [15] .', 'Only one [18] was applied to a diverse human microarray knowledge set , in this case containing arrays .', 'Given that tens of thousands of arrays are publicly available for some individual microarray platforms , a larger-scale identification of transcriptional modules is certainly possible .', 'Knowledge of transcriptional modules and their constituent genes is not directly applicable to DE gene identification , and most existing methods ignore these relationships .', 'Of the few that provide a means to incorporate expression modules [19]–[21] , none provide a mechanism for extracting these modules from large-scale microarray knowledge sets .', 'Consequently , there is a need for a method that can identify relevant transcriptional modules from huge compendia of microarray knowledge and use this information to better predict DE genes .', 'In this work , we present the SVD Augmented Gene expression Analysis Tool ( SAGAT ) , a mathematical approach that identifies expression modules from microarray knowledge and combines these with novel data to identify DE genes .', 'To accomplish these tasks , SAGAT employs Singular Value Decomposition ( SVD ) in concert with pseudoinverse projection .', 'SVD has been used previously to decompose microarray knowledge into mathematically independent transcriptional modules ( eigengenes ) and the corresponding independent cellular states where these modules are active ( eigenarrays ) [22] .', 'Most non-SVD module-finding methods identify discrete modules where module membership for each gene is a binary feature .', 'In contrast , SVD assigns a continuously-valued weight for each gene , which allows varying strengths of coexpression to be present in the same module and genes to be part of multiple modules .', "SVD models the expression of each gene as a linear combination of the eigengenes' expressions , and a number of studies have used this technique to define modules on smaller scales .", 'Raychaudhuri et al . [23] and Alter et al . [22] each initially applied SVD ( the former in the form of PCA ) to yeast time course data to identify fundamental modes of expression response that vary over time .', 'The latter study also demonstrated the ability of SVD to remove noise or experimental artifacts present in the data .', 'Shortly thereafter , Troyanskaya et al . [24] used SVD to identify eigengenes in gene expression data for the purposes of missing value estimation .', 'Alter and colleagues subsequently employed generalized [25] and higher order [26] versions of SVD for the integration and decomposition of heterogeneous microarray datasets .', 'Horvath and Dong [27] used SVD of microarray data in combination with coexpression analysis to generate eigengene coexpression networks .', 'Finally , in a large scale study , SVD was shown to reduce noise when used in the integration of disparate microarray datasets [28] .', 'The technique of pseudoinverse projection has also previously been applied to genome-scale data .', 'Alter and Golub demonstrated the utility of SVD coupled with pseudoinverse projection by reconstructing one genomic dataset in terms of the eigenarrays of another [29] .', 'This enabled the observation of a set of cellular states in one dataset that were also manifested in the other .', 'Subsequent work used pseudoinverse projection in concert with an alternative matrix decomposition technique ( non-negative matrix factorization ) to classify gene expression states of one organism in terms of another [30] .', 'In the current work , using SAGAT , we combine SVD-derived modules , pseudoinverse projection , and a rigorous statistical model to adjust gene expression error estimates in a dataset of interest .', 'This yields a knowledge-informed differential expression score for each gene .', 'We demonstrate SAGAT in several ways .', 'First , we investigate whether transcriptional modules are readily detectable in a large compendium of microarray knowledge .', 'Second , we test SAGAT on a range of simulated datasets to assay its performance with respect to a known gold standard .', "Third , we evaluate SAGAT's ability to increase DE gene predictive power in three highly replicated real world datasets .", 'Finally , we apply SAGAT to a new human dataset investigating transcriptional profiles in the setting of insulin resistance ( IR ) , a risk factor for type 2 diabetes .', 'Though a known relationship exists between obesity and insulin resistance [31] , [32] , it is not always consistent [33] , [34]; in addition , many studies characterizing IR do not deconvolve the effects of obesity [35] .', 'This novel microarray dataset builds upon previous work [35]–[37] to investigate obesity-independent transcriptional effects of insulin resistance .', 'We illustrate the improved sensitivity of SAGAT over existing methods by identifying IR candidate DE genes , and we validate a subset of these using quantitative PCR assays .', 'Results of this analysis contribute to a more comprehensive molecular understanding of human insulin resistance .'], ['To demonstrate that transcriptional modules are detectable in a multi-condition microarray knowledge compendium , we characterized the degree of modularity in a collection of 4440 arrays from the HGU95Av2 platform .', 'We consider an expression module a group of genes exhibiting coordinated expression across some subset of the entire compendium .', 'Genes in such a group will have relatively large positive or negative pairwise covariances; thus , degree of modularity refers to the number of genes in the compendium that belong to one or more groups of significantly covarying genes .', 'Figure 1A displays a binarized representation of the sample covariance matrix for the entire HGU95Av2 compendium , whereby each covariance value whose magnitude is is colored black ( white otherwise ) .', 'This matrix was then subjected to hierarchical biclustering ( Figure 1B ) , which resulted in many blocks of nonzero binary covariance , ranging in size from a few genes to nearly 1000 .', 'Furthermore , this covariance pattern does not appear to be due to chance , as the biclustering results from 100 randomized knowledge matrices ( see Materials and Methods ) showed no covariance blocks exceeding a 15 gene cutoff .', 'To parameterize a simulation study ( details below ) , we used a 1000-gene compendium to characterizee the mean number of genes per module , the mean percentage of DE genes found in modules , and the mean percentage of non-DE genes found in modules .', 'This was achieved by subsetting the HGU95Av2 compendium and coupling it with a human prostate cancer dataset [38] .', 'The mean number of genes per module was 15 , the mean percentage of DE genes found in modules was 60% ( 673/1122 ) , and the mean percentage of non-DE genes found in modules was 47% ( 3752/7983 ) .', 'These values were employed in the simulation study that follows .', 'SVD identifies eigengenes whose expression is mutually orthogonal across all arrays in the compendium .', 'To demonstrate that mathematical orthogonality correlates with biological orthogonality ( as manifested by biologically independent eigengenes ) , we performed a Gene Ontology ( GO ) term enrichment analysis of a subset of the eigengenes from the HGU95Av2 compendium ( using the gene weights of each eigengene as scores ) .', 'Table 1 displays the top three significant Biological Process terms with fewer than 500 annotated genes for eigengenes 1–5 , 10 , 20 , 50 , and 200 .', 'The terms within each eigengene are largely consistent , and each eigengene describes a relatively distinct biological process .', 'We note that there is not an absolute correspondence between the modules displayed in Figure 1B and the eigengenes identified by SVD , as the methods used to identify these structures are algorithmically different .', 'However , we detected substantial overlap in the enriched Biological Process terms associated with the largest covariance modules and highest ranking eigengenes ( e . g . the largest module and first eigengene were both strongly enriched for translation and biosynthesis terms ) .', 'We first tested the validity of the SAGAT model using simulated data .', 'We simulated knowledge compendia with structures ranging from that shown in Figure 2A , where 60 of the 100 DE genes are in 15-gene modules and none of the 900 non-DE genes are , to that shown in Figure 2C , where the same number of DE genes are in modules and all 900 non-DE genes are also .', 'Figure 2B depicts a modularity structure that is approximately equivalent to that of the prostate cancer dataset , where 60% of prostate cancer DE genes are found in modules and 47% of non-DE prostate cancer genes are found in modules .', 'After running SVD on each simulated compendium to calculate the appropriate W matrix , we tested SAGAT on all combinations of data and knowledge .', 'As SAGAT relies on a single parameter specifying the number of eigengenes ( M ) , we first estimated the optimal value for this parameter by trying all possible values on several configurations of data and knowledge ( results not shown ) .', 'The best performance was achieved with ; we used this value for all subsequent simulation runs .', 'Figure 3 displays results from running SAGAT on two compendia with modularity structures identical to Figures 2A ( 3A , 3B ) and 2B ( 3C , 3D ) coupled with datasets having either one or 15 replicates .', 'The mean AUC improvement over the fold change metric ( Mean ) , ranging from . 0042 to . 0708 , is shown .', 'Within the range of structures bounded by these two compendia and for both sample sizes , SAGAT consistently improves the AUC of DE gene prediction .', 'The two trends observed are: ( 1 ) increasing performance improvement with decreasing numbers of array replicates , and ( 2 ) increasing performance improvement with decreasing numbers of non-DE gene modules .', 'Performance begins to degrade below that of fold change if the simulated compendia adopt modularity structures between those of Figures 2B and C ( results not shown ) , but we have evidence suggesting that the modularity of real world datasets resemble configurations falling between Figures 2A and B ( see Discussion ) .', 'To demonstrate that use of SAGAT could yield improved statistical power without concurrently increasing the false positive rate of prediction , we repeated the above experiments using true positive rate ( TPR ) evaluated at a fixed false positive rate ( FPR ) of . 05 ( in place of AUC ) .', 'These results are shown in Figure S3 , and the performance improvements with respect to fold change closely resemble those displayed in Figure 3 .', 'To evaluate SAGAT performance on real data , we tested it on subsets of three highly replicated human microarray datasets ( see Materials and Methods for details ) .', 'As a gold standard , we used either the fold change or limma t [39] metrics to identify significant DE genes from each dataset in its entirety; this resulted in 1122 ( 12 . 3% ) , 588 ( 4 . 4% ) , and 6002 ( 29 . 9% ) DE genes for the prostate cancer , letrozole treatment ( GEO ID: GSE5462 ) , and colorectal cancer ( GSE8671 ) datasets , respectively .', 'After downloading the three corresponding knowledge compendia ( minus the highly replicated datasets ) and running SVD on each , we determined the optimal number of eigengenes by training on the prostate cancer dataset .', 'Figure 4 shows results of SAGAT run on two non-overlapping subsets of this dataset and the HGU95Av2 compendium while varying the number of eigengenes ( parameter M ) .', 'The AUCs of the fold change metric are displayed as red horizontal lines .', 'SAGAT outperforms fold change for many values of M , and for both subsets there is a distinct maximum in the AUC curve for a particular value of the parameter .', 'For these two subsets and several others tested ( not shown ) , the optimal value for M is approximately half the number of arrays in the compendium .', 'We used this value for subsequent analyses on all datasets and compendia , which translates to 2220 , 7238 , and 6108 eigengenes for the HGU95Av2 , HGU133A , and HGU133plus2 . 0 platforms , respectively .', "To show that SAGAT's performance as a function of M was not due to chance , we randomized the expression values of the compendium and re-ran the same test in Figure 4B .", 'These results are shown in gray .', 'In this case , SAGAT never outperforms fold change , suggesting that the performance improvement from the original compendium is not spurious .', 'Next we applied SAGAT to multiple subsets of each of the three datasets .', 'Figure 5 displays the performance of SAGAT coupled with the appropriate W matrices .', 'For comparison , we feature AUC differences with respect to fold change of both SAGAT and the limma t-statistic .', 'Figures 5A and B display performance on the prostate cancer dataset using a fold change and limma t-derived gold standard , yielding mean AUC improvements of . 023 and . 018 , respectively .', 'Given that the relative performance trends are similar , Figures 5C and D show performance on the letrozole treatment and colorectal cancer datasets using only the fold change-derived gold standard , yielding AUC improvements of . 009 and . 019 , respectively .', 'In all three datasets , irrespective of sample size , SAGAT nearly always improves the AUC over fold change; in cases where this does not occur , AUC is left essentially unchanged .', 'In contrast , the t-statistic consistently lowers the AUC of DE gene prediction and is not applicable when the number of replicates is 1 .', 'Though the limma t performance improves when using a limma t gold standard , it is still unable to outperform the other two metrics .', 'AUC improvement for SAGAT generally decreases with increasing sample size , and the improvement is largest for the prostate cancer and colorectal cancer datasets .', 'To express the performance of SAGAT in a more tangible form , we estimated the effective number of arrays added by using the method .', 'Table 2 shows results for each of the three highly replicated datasets at four initial sample sizes .', 'On average , with one exception in 12 tests , use of SAGAT always increased the effective number of arrays .', 'In some cases , this improvement was quite significant: a two-array prostate cancer subset coupled with SAGAT effectively performed as well as a 4 . 72 array dataset .', 'As before , the number of arrays added generally decreases with increasing sample size .', 'As with the simulated data , we also repeated the highly replicated dataset experiments using TPR calculated at an FPR of . 05 as an evaluation metric .', 'These results are displayed in Figure S4 , and the performance improvements very closely resemble those shown in Figure', '5 . We evaluated the GEO method ( both standard and “voting” methods ) on the prostate cancer dataset and HGU95Av2 compendium and compared its performance to SAGAT .', 'Figure S1 shows the results , which demonstrate that SAGAT ( and fold change ) outperform the GEO method in much the same way as when compared to the limma t-statistic above .', 'We also measured the sensitivity of SAGAT performance to compendium size .', 'As Figure S2 shows , SAGAT continues to improve performance as the compendium increases to its full size .', 'The performance begins to level off near 4400 arrays , but further improvement would still be expected with an even larger compendium .', 'Given encouraging performance of SAGAT on simulated and real human datasets , we applied it to an unpublished experimental dataset investigating expression differences between human insulin resistant and insulin sensitive adipose tissue .', 'The obesity-independent relationship between insulin resistance and adipose gene expression has previously been characterized on a small scale [40] , but no large-scale studies have attempted to decouple the effects of obesity from insulin resistance [35] .', 'In this experimental design , patients were otherwise healthy and matched for levels of obesity; thus , we expected to identify more subtle expression changes associated with insulin sensitivity status .', 'As detailed in Materials and Methods , the same 12 pairs of RNA samples were applied to three different microarray platforms: Affymetrix , Agilent , and Illumina .', 'We initially attempted to identify DE genes using the limma t metric on data from each platform individually .', 'After correcting the results for multiple tests , we did not detect any significant genes at a . 05 FDR cutoff .', 'Next , we integrated results from all three platforms to try to capture subtle but consistent signals .', 'We applied the method of Rank Products ( RP ) [41] to lists of genes ranked by either fold change or SAGAT .', 'Table 3 shows results from this procedure .', 'As we wanted to evaluate only the most confident predictions , we corrected for multiple testing by controlling the PFER ( per family error rate ) .', 'This is a strict multiple hypothesis test correction method that is generally more conservative than the FDR ( false discovery rate ) or FWER ( family wise error rate ) [42] .', 'A total of 19 genes were found to be significantly DE at a PFER of . 05 .', 'When ranking genes by fold change before applying RP , 12 genes were found to be significantly DE—five upregulated and seven downregulated .', 'When using SAGAT to rank the genes instead , 18 genes were significantly DE—seven upregulated and 11 downregulated .', 'SAGAT with RP detected all but one of the genes found using fold change with RP , and seven genes were identified only through use of SAGAT .', 'We refer to the 11 genes detected by both fold change and SAGAT rankings as Group I; Group II genes are those that were detected exclusively using SAGAT .', 'We searched the literature for evidence implicating the genes of Table 3 in insulin resistance , diabetes , or fatty acid metabolism ( an important function of adipose tissue ) .', 'Genes for which evidence was found are marked with an asterisk .', 'Four of the Group I genes [FOSB ( Entrez Gene ID: 2354 ) , FADS1 ( 3992 ) , SELE ( 6401 ) , PPBP ( 5473 ) ] had some literature describing their involvement; five of the Group II genes [ATP1A2 ( Entrez Gene ID: 477 ) , FASN ( 2194 ) , FOS ( 2353 ) , CXCR4 ( 7852 ) , ELOVL6 ( 79071 ) ] were also implicated .', 'To experimentally validate these candidates , we performed quantitative RT-PCR ( qPCR ) using 23 of the original 24 RNA samples subjected to an amplification reaction .', 'We tested 11 of the 19 genes from Table 3: five from Group I and six from Group II .', 'We also tested four genes that were not significant by Rank Products; these genes serve as negative controls .', 'For each gene , we calculated the mean fold change over the -actin ( Entrez Gene ID: 60 ) housekeeping gene for the insulin resistant and insulin sensitive samples .', 'Results are displayed in Figure', '6 . Of the Group I and II genes tested , all had qPCR expression differences that matched the direction of those identified using Rank Products .', "We then tested the significance of each gene's expression difference using a Wilcoxon rank-sum test .", 'Three of the genes had p-values smaller than a . 05 threshold: CSN1S1 ( Entrez Gene ID: 1446 ) , FOSB , and CXCR4 ( marked by asterisks in Figure 6 ) .', 'The first two genes are from Group I; the third is from Group II .', 'Of the four negative controls tested , none were found significantly different in expression between the two groups .'], ['In this work , we present SAGAT , a principled method for integrating pre-existing microarray knowledge with a dataset of interest to identify DE genes .', 'From prior knowledge , SAGAT extracts “eigengenes” , or mathematically independent transcriptional modules , which collectively describe observed expression dependencies between genes .', 'These dependencies are combined with the expression changes of each gene in the data to form the SAGAT score , which enables expression information to be shared between genes that are coexpressed in the knowledge .', 'To validate SAGAT , we first demonstrated that a compendium of microarray knowledge showed significant modularity .', 'This result , which was not sensitive to varying compendium sizes ( not shown ) , was not surprising , as it has been shown before on knowledge sets of a smaller scale .', 'Nevertheless , it was not clear whether such modules would be detectable on a much larger and more heterogeneous collection of microarrays .', 'Next , we demonstrated favorable SAGAT performance in identifying DE genes on a series of simulated datasets .', 'We note that our model for simulating data represents an oversimplification of realistic coexpression relationships between genes ( see Materials and Methods ) , but with it we can create distinct numbers of modules in DE and non-DE genes to test the limits of SAGAT performance .', 'As detailed in the Results , SAGAT most improves performance with respect to the fold change metric when transcriptional modules are only composed of DE genes .', 'As the number of non-DE gene modules increases , the performance improvement decreases , but at a realistic ratio of DE gene modules to non-DE gene modules ( Figure 2B , which closely matches the configuration of the prostate cancer dataset ) , SAGAT still outperforms fold change for all numbers of replicates tested .', 'We evaluated SAGAT on three highly replicated microarray datasets .', 'We chose datasets with many replicates so we could approximate a gold standard DE gene list for each one .', 'Ideally , results from an independent and more accurate experiment like quantitative RT-PCR would provide the DE gene truth for a given dataset , but quantifying expression differences of every gene on a microarray would be prohibitively expensive .', 'Instead , we assume that for each of the three datasets , the number of replicates is large enough that DE genes calculated using fold change on all arrays is approximately correct .', 'Then the task becomes using small ( often noisy ) subsets of each dataset to predict the true DE genes .', 'We applied the fold change , limma t , and SAGAT metrics to multiple non-overlapping subsets of varying numbers of replicates .', 'SAGAT always outperforms the t-statistic , often by a large margin .', 'With sample sizes of only 1 replicate , the limma t is not applicable as it requires a fold change variance estimate .', 'Compared to fold change , SAGAT nearly always better identifies DE genes; in the worst case it leaves performance unchanged .', 'These results suggest that SAGAT would be consistently beneficial for predicting DE genes from a dataset of interest .', 'Importantly , the results displayed in Figure S4 demonstrate that use of SAGAT leads to improved statistical power at a small fixed false positive rate , which is a necessity for the effective analysis of high-throughput biological experiments .', "We expressed SAGAT's performance improvement over fold change in terms of the effective number of arrays added .", 'This shows that , except in a small number of cases , use of SAGAT always increases the effective sample size of an experiment .', 'In some cases this increase is substantial: for one two-array subset of the prostate cancer dataset , the effective sample size became 4 . 72 arrays , or more than double the initial sample size of the experiment .', 'As expected , the number of arrays added decreases as the initial number of arrays increases , due in part to the lower capacity for prediction improvement when starting with a larger sample size .', 'We also demonstrated that SAGAT outperforms the related GEO method when evaluated on the prostate cancer dataset .', 'As even the fold change method consistently outperforms the GEO method , it appears that more accurate estimation of gene variances is not the most effective way to improve performance for this dataset .', 'In contrast , use of gene module information from an SVD of microarray knowledge gives consistent improvement over fold change .', 'We determined the sensitivity of SAGAT performance to the number of arrays in the knowledge compendium .', 'It was shown in [11] that the GEO method does not give further performance improvement when knowledge exceeds arrays .', 'To compare , we evaluated the effect of compendium size on SAGAT performance using the prostate cancer dataset .', 'Unlike the GEO method , SAGAT continues to improve performance as the compendium increases in size .', "The improvement starts leveling off near the compendium's full size ( 4400 arrays ) , but an even larger knowledge compendium should still give better performance .", 'Thus , SAGAT is able to extract useful information from much larger microarray compendia than the GEO method .', "Given SAGAT's potential to improve DE gene identification , we applied the method to a novel insulin resistance dataset obtained from three different microarray platforms .", 'An initial attempt to identify DE genes on each platform separately yielded no candidates , suggesting that the transcriptional response in question was noisy and/or subtle .', 'A Gene Ontology term enrichment analysis on data from each platform consistently identified terms related to immune response ( results not shown ) , implying that a reproducible biological signal was present in the data .', 'To improve the signal to noise ratio at the gene level , we used the method of Rank Products ( RP ) across all three platforms to identify subtly but consistently changing DE genes .', 'An application of RP to genes ranked by fold change yielded 12 DE gene candidates with a per family error rate of . 05 or smaller .', 'A similar analysis on genes ranked by SAGAT yielded 18 genes , 11 of which overlapped with the fold change list .', 'This suggests that the incorporation of transcriptional module information resulted in an increased sensitivity to detecting DE genes .', 'We intentionally used a very strict significance threshold to select a small number of DE genes that were most consistently changed ( and which hopefully represent true biological differences ) , but relaxation of this threshold would lead to additional candidates .', 'We next performed a literature search on each significant gene for information implicating it in insulin resistance , diabetes , or fatty acid metabolism .', 'This uncovered evidence for multiple genes from three biological processes: inflammation [SELE , IL6 ( Entrez Gene ID: 3569 ) , PPBP , CXCR4] , cell differentiation [FOSB , FOS] , and fatty acid synthesis [FADS1 , FASN , ELOVL6] [43]–[45] .', 'A role for inflammation in IR has previously been suggested by a similar study [35] , but of the four pro-inflammatory genes listed above only IL6 was also detected in that work .', 'In this study , SELE , IL6 , and CXCR4 were upregulated in insulin resistant patients , reinforcing the positive role of inflammation in IR .', 'Cell differentiation has also been implicated in insulin resistance in the sense that insulin resistant adipose tissue displayed lower expression of differentiation markers than their insulin sensitive counterparts [37] .', 'In this work FOSB and FOS were upregulated in IR , which is compatible with the above since both gene products have been shown to trigger de-differentiation [46] , [47] .', 'Fatty acid synthesis has long been known to be relevant to insulin resistance [48] .', 'The details of this relationship are not always consistent: FADS1 is known to be downregulated in IR [49] , while ELOVL6 has shown the opposite effect [50] and FASN has shown conflicting results [51] .', 'To our knowledge , no single study has analyzed the effects of all three of these fatty acid synthesis genes with respect to insulin resistance in adipose tissue .', 'Our results show a coherent decrease in the gene expression of all three genes , suggesting that obesity-independent insulin resistance is associated with altered fatty acid synthesis and storage in adipose tissue .', 'We speculate that such an occurrence may lead to inappropriate fatty acid accumulation elsewhere ( i . e . circulating in serum ) , which has been known to lead to IR [51] .', 'One explanation for the inconsistent results in previous studies is the potentially confounding effects of obesity ( a condition where fatty acid synthesis increases ) and insulin resistance .', 'The current study explicitly attempts to remove the former effect .', 'Taken together , the above results emphasize the importance of increased inflammation , differentiation , and decreased fatty acid synthesis to adipose tissue-based insulin resistanc
['Introduction', 'Results', 'Discussion', 'Materials and Methods']
['Although they have become a widely used experimental technique for identifying differentially expressed ( DE ) genes , DNA microarrays are notorious for generating noisy data .', 'A common strategy for mitigating the effects of noise is to perform many experimental replicates .', 'This approach is often costly and sometimes impossible given limited resources; thus , analytical methods are needed which increase accuracy at no additional cost .', 'One inexpensive source of microarray replicates comes from prior work: to date , data from hundreds of thousands of microarray experiments are in the public domain .', 'Although these data assay a wide range of conditions , they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods .', 'We present the SVD Augmented Gene expression Analysis Tool ( SAGAT ) , a mathematically principled , data-driven approach for identifying DE genes .', "SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements .", 'We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2 . 72 arrays .', 'We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance , resulting in a 50% increase in the number of significant genes detected .', 'We evaluated 11 ( 58% ) of these genes experimentally using qPCR , confirming the directions of expression change for all 11 and statistical significance for three .', 'Use of SAGAT revealed coherent biological changes in three pathways: inflammation , differentiation , and fatty acid synthesis , furthering our molecular understanding of a type 2 diabetes risk factor .', 'We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses , and we provide it as a freely available software package that is immediately applicable to any human microarray study .']
['Though the use of microarrays to identify differentially expressed ( DE ) genes has become commonplace , it is still not a trivial task .', 'Microarray data are notorious for being noisy , and current DE gene methods do not fully utilize pre-existing biological knowledge to help control this noise .', 'One such source of knowledge is the vast number of publicly available microarray datasets .', 'To leverage this information , we have developed the SVD Augmented Gene expression Analysis Tool ( SAGAT ) for identifying DE genes .', 'SAGAT extracts transcriptional modules from publicly available microarray data and integrates this information with a dataset of interest .', "We explore SAGAT's ability to improve DE gene identification on simulated data , and we validate the method on three highly replicated biological datasets .", "Finally , we demonstrate SAGAT's effectiveness on a novel human dataset investigating the transcriptional response to insulin resistance .", 'Use of SAGAT leads to an increased number of insulin resistant candidate genes , and we validate a subset of these with qPCR .', 'We provide SAGAT as an open source R package that is applicable to any human microarray study .']
['computational biology/systems biology', 'genetics and genomics/gene expression', 'computational biology/genomics', 'genetics and genomics/bioinformatics', 'diabetes and endocrinology/type 2 diabetes']