BACKGROUND
combining and integrating different 'omics' data such as transcript-, protein-, and metabolite levels and enzyme activities is essential for a full understanding of the nature of the cellular metabolism as a system  <cit> .
with respect to transcript levels, a large amount of microarray data is publicly available for arabidopsis thaliana, a model plant.
such large datasets facilitate the construction of gene co-expression databases  <cit>  and the survey of transcriptome organization  <cit> .
integrating transcript- and metabolite data by, for example, studying the correlation relationships among profiled data, facilitates the characterization of unknown gene functions, and furthers our understanding of plant cellular systems  <cit> .
the correlation between variables  is also important for multivariate statistical analyses such as principal component analysis  and hierarchical cluster analysis.
typical metabolite-profiling data show a few, but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions  <cit> .
the metabolomic correlation as well as gene co-expression are not always in agreement with known biochemical pathways.
metabolomic correlation approaches have highlighted some properties  in several species including plants  <cit> .
steuer et al.
<cit> , who provided a relationship between the structure of a metabolomic-correlation network and a metabolic reaction network using a jacobian matrix, found that the relationship is not simple.
they pointed out that small fluctuations such as glucose availability can result in a certain correlation pattern and persist through metabolic pathways.
using metabolic control analysis  and correlation analysis based on metabolomic data, camacho et al.
<cit>  suggested that metabolites are strongly correlated when they respond in the same directions to all perturbations  in enzyme levels.
for example, mass conservation and chemical equilibrium were suggested as one origin of a high correlation.
muller-linow et al.
<cit>  applied network similarity, a graph-theoretic parameter, to compare metabolomic correlation networks with biochemical reactions derived from the kegg database  <cit> .
they reported that these networks were in disagreement and that closeness in metabolomic correlation is not an indicator of closeness in biochemical networks.
studies on the effect of changes in environmental conditions and temporal- and spatial assessments of the topology of metabolomic correlation networks have been reported  <cit> .
further investigation of the properties of metabolomic correlation networks may discover whether highly connected metabolites, the so-called 'modules', in the correlation network reflect known biochemical pathways.
we investigated similarities and dissimilarities in metabolomic correlations in the aerial parts of  <dig> arabidopsis genotypes, col- <dig> wild-type , methionine-over accumulation  <dig>   <cit> , and transparent testa <dig>   <cit> .
elsewhere  <cit>  we reported that the mutation in cystathionine γ-synthase  and/or the over-accumulation of methionine  strongly affect the correlation networks in aerial parts of mto <dig>  in the present study, using gas chromatography-time-of-flight/mass spectrometry , we measured the relative metabolite levels in root samples of the  <dig> arabidopsis genotypes to assess tissue- and/or genotype-dependent changes in their metabolite levels.
we systematically compared the metabolomic correlations observed in  <dig> different datasets, the roots and the aerial parts.
multivariate statistical analyses showed the distinct metabolome of these plants and tissues.
we then constructed correlation networks by pair-wise correlation between the metabolites and performed graph clustering using the dpclus algorithm  <cit>  that efficiently extracts densely connected metabolites in a large-scale network.
we then evaluated the obtained clusters with kegg  <cit>  enrichment analysis.
our results demonstrate that changes in each network topology are tissue- and/or genotype-dependent and that they reflect, at least partially, known biochemical pathways in arabidopsis.
RESULTS
metabolic phenotypes of the roots of  <dig> arabidopsis genotypes
the experimental workflow is shown in figure  <dig>  roots of col- <dig> wild-type , mto <dig>  and tt <dig> mutants were sampled and analyzed.
we detected  <dig> metabolite peaks including mass spectral tags   <cit>  by the gc-tof/ms-based metabolite profiling we established for arabidopsis  <cit> .
of these,  <dig> were known metabolites including carbohydrates, amino-, fatty-, and organic acids and vitamins, and others were secondary metabolites .
for comparisons with data from aerial parts we selected  <dig> commonly-detected metabolites in both datasets using metmask http://metmask.sourceforge.net <cit> , a tool for metabolite identifier linking.
by pca we visually inspected the metabolite profiles of the roots and aerial parts based on commonly detected metabolites and all peaks .
to confirm the most important principal components  of these samples we prepared score plots for each dataset.
as in aerial parts, samples of mto <dig> roots were clearly separated while samples of tt <dig> roots were close to wt along the second component axis .
the loading plots highlighted and visualized metabolites with a significant role in genotype separation.
for example, methionine , urea, glycine, and pipecolate had a strong impact on the roots and aerial parts of mto <dig> and tt <dig> .
the complete list of metabolites that contributed to the separation of the mutant profile groups from wt  is shown in additional file  <dig>  in the pca score plots of the  <dig> metabolites, the first principal component  clearly discriminated between mto1- and the control samples .
to obtain further insights into the interpretation of our pca results, we re-calculated pca by removing the four most influencing metabolites  from the original data matrices.
additional file  <dig> shows the score and loading plots of roots  and aerial parts  using  <dig> metabolite datasets.
the pca plots revealed that asparagine and glutamine strongly contributed to pc <dig> separation in both the roots and aerial parts .
the number of significant correlations depends on the tissue/genotype and their differential correlations
to evaluate the correlations among the metabolite profiles observed in each tissue and genotype we calculated the spearman correlation coefficient .
the significant correlations  <  <dig> ; see ref  <cit> ) for each tissue and each genotype are shown in figure  <dig>  all resultant correlations are listed in additional file  <dig>
we first examined the number of significant correlations between tissues.
we found that the number of significant correlations in wt and mto <dig> roots was larger than in their aerial parts ; it was also much larger in the aerial parts of tt <dig> than its roots.
then, we compared differences between correlations in roots and aerial parts using fisher z-transformation .
similarly, we also compared differential correlations between genotypes.
the number of statistical differences between correlations in roots and aerial parts in wt, mto <dig>  and tt <dig> was  <dig>   <dig>  and  <dig>  although there were no significant changes in the correlations between genotypes except for  <dig> differential correlations between wt and tt <dig> in roots  .
our findings imply that in both tissue-types, the mto <dig> mutation strongly affects the metabolomic correlation network, and that there are many correlations intensified by a lack of the chalcone synthase  gene.
our observations also suggest that the tendency for changes in the number of significant correlations is similar within the studied tissues of the  <dig> genotypes.
construction of a metabolomic correlation network and selection of the correlation threshold
for visualization and to gain insights into the metabolomic correlations for each tissue and genotype we constructed correlation networks.
the selection of an appropriate threshold is important for the construction of such a correlation network, if it is too high the generated network will be sparse, if too low, only a large connected component will be produced.
to assess threshold-dependent changes in the network topology we computed  <dig> graph-theoretic measures , i.e., the graph density, clustering coefficient, average degree, average path length, number of connected components, and the number of edges .
we also compared the statistics of the real data and  <dig> randomized data .
we observed that the number of connected components showed a transition from small to large at a correlation threshold of  <dig>  .
therefore, we set the threshold at r ≥  <dig> .
although a threshold does not guarantee explicit biological significance, we found that examining various statistics among the constructed networks was useful for selection.
graph clustering identifies modules significantly enriched for metabolites contained in biochemical pathways
at r ≥  <dig> , dpclus  <cit>  identified  <dig>   <dig>  and  <dig> clusters in the metabolomic correlation network for the root samples of wt, mto <dig>  and tt <dig>  respectively; they ranged in size from  <dig> to  <dig> metabolites.
for the aerial parts of wt, mto <dig>  and tt <dig> we obtained  <dig>   <dig>  and  <dig> clusters, respectively; they ranged in size from  <dig> to  <dig> metabolites .
we posit that this difference in the number of clusters reflects differences in the network topology, which, in turn, is tissue- and genotype-dependent.
to assess the significance of the clusters we used the over-represented kegg pathways  in the obtained- and randomized clusters .
the results of graph clustering with kegg enrichment analysis for mto <dig> roots are presented in figure  <dig>  our findings on the other genotypes are listed in table 2; they are visualized in additional file  <dig>  the clusters obtained with the graph clustering method involved the enriched kegg pathway and included the 'alanine-, aspartate-, and glutamate metabolism'  in mto <dig> roots .
the statistical relevance of this approach was evaluated with the s-value  <cit>  that can be used for assessing the significance of the clustering results based on kegg pathways .
as shown in figure  <dig>  the averaged best p-value for significantly-enriched kegg pathways was better in real- than randomized data.
a is the number of metabolites in the kegg pathway belonging to the dpclus cluster, a+b indicates the total number of metabolites in the kegg pathway, a+c the number of metabolites in the dpclus cluster, and n the total number of metabolites in the profiling data.
rows reflect presence/absence within the kegg pathway.
columns contain each of the  <dig> dpclus clusters.
for each kegg pathway the metabolites belonging to the  <dig> comparison groups are shown.
predominant pathways in clusters reflect tissue- and/or genotype-dependent metabolomic regulation
except for the clusters found in the aerial parts of wt and mto <dig>  the detected clusters tended to include steroid biosynthetic pathways .
the strong correlation between sterols confirmed our earlier observations for aerial parts  <cit> .
metabolites belonging to fatty-acid biosynthesis tended to cluster together.
the clusters we commonly found in the aerial parts of the  <dig> genotypes were galactose metabolism.
the largest cluster for the aerial parts and roots of mto <dig> were 'alanine, aspartate, and glutamate metabolism.'
this suggests that the mutation resulting in met over-accumulation can affect the metabolic pathway of the aspartate family.
metabolite enrichment analysis using other mto mutants also extracted the same pathways  <cit> .
the largest cluster in the roots of tt <dig> was 'biosynthesis of alkaloids derived from the shikimate pathway.'
for both tissues, tt <dig>  which lacks the gene encoding chs  <cit> , showed less pronounced changes in the metabolite levels belonging to this biosynthetic pathway .
discussion
we focused on the changes in the topology of metabolomic correlation networks of  <dig> arabidopsis genotypes .
when comparing the wt, the increases and decreases in the number of significant correlations for mto <dig> and tt <dig> were similar in their aerial parts and roots.
our comparative studies showed strongly altered metabolomic correlations in the roots of mto <dig>  as did kusano et al.
<cit>  in aerial parts, we observed a marked increase in the number of significant correlations in the roots of tt <dig> compared to the roots of wt.
as our study imposed no environmental perturbations on the  <dig> genotypes, we suggest that the observed changes in the topology of metabolomic correlation networks are attributable to underlying tissue- and/or genotype-dependent biochemical regulations.
graph clustering using dpclus  <cit>  yielded densely connected metabolites on the metabolomic correlation networks.
kegg enrichment analysis to assess the statistical significance of the detected clusters demonstrated specific differences in the clusters in the enriched pathways.
we posit that the assigned kegg pathways for each cluster reflect differences in underlying genetic properties and in tissue- and/or genotype-dependent biochemical regulations.
the largest cluster in mto <dig> roots was assigned as the'alanine, aspartate, and glutamate metabolism'.
since mto <dig> mutants are characterized by mutations involving the aspartate pathway, the approach based on graph-clustering proved itself useful for characterizing genotypes.
although there were no significant enriched kegg pathways in cluster  <dig> in figure  <dig>  this cluster may represent the extensive coordination among biosynthetic pathways involved in glycolysis, the tricarboxylic acid -cycle, and stress-responsive metabolites in mto <dig> roots.
our approach may yield new insights into the organization of metabolites in the functional pathways of a given organism.
in tt <dig> roots, the largest cluster was 'biosynthesis of alkaloids derived from the shikimate pathway'.
this cluster contained metabolites associated with the biosynthetic pathways of the tca cycle and with glycolysis because this kegg pathway covered extensive pathways from glycolysis to alkaloids via shikimate for the biosynthesis of aromatic amino acids including phenylalanine, tyrosine, and tryptophan.
the mutation in the chs gene appears not to affect the biosynthesis of alkaloids in arabidopsis.
we posit that the model plant arabidopsis lacks the production of complex alkaloids, although homologues for many enzymes associated with alkaloid biosynthesis have been detected in its genome  <cit> .
our multivariate statistical analysis suggests that the mutation of chs may influence nitrogen assimilation .
furthermore, the third cluster  in tt <dig> roots was significantly enriched for metabolites contained in 'nitrogen metabolism' , suggesting that our approach may reflect underlying changes in the metabolism by lacking of chs in arabidopsis.
taken together, our observations demonstrate that variations in the topology of correlation networks are tissue and/or genotype-dependent and reflect at least partially known biochemical pathways in arabidopsis.
earlier studies indicated that the interpretation of metabolomic correlations requires careful evaluation  <cit> .
our work demonstrates that graph clustering can be used to gather metabolites belonging to the metabolic pathways that change in response to different regulations that in turn are dependent on the tissue and/or genotype.
our analyses differ from those of  <cit>  in that we examined the densely connected metabolites in the correlation network by statistical kegg enrichment analysis instead of using network similarity or proximity.
graph clustering approaches have been effectively applied to gene co-expression networks for extracting functional, densely connected genes  <cit> .
we showed that the approach is also effective for metabolomic correlations.
although we only evaluated  <dig> genotypes, the approach based on graph clustering seems to be useful and applicable to the monitoring of changes in the topology of correlation networks.
as in the gene co-expression analysis that involves thousands of microarrays, there were few negative correlations in this study.
for instance, the number of significant negative correlations  in the roots and aerial parts of wt was  <dig> and  <dig>  respectively.
therefore, we deliberately did not use negative correlations.
other similarity measures such as mutual information  <cit>  and partial correlation  <cit> , instead of spearman correlation coefficients, may be applied in this approach.
further studies are necessary to assess to what extent these measures affect the graph clustering results of metabolomic correlation networks.
there are few systematic comparisons of metabolomic correlations among time series data  <cit> .
temporal and spatial assessment of the topology in the metabolomic correlation network may contribute to the development of novel data-mining methods and to the discovery of biomarkers  <cit> .
there are also few published systematic metabolomic comparisons under stress conditions.
szymanski et al.
<cit>  demonstrated that the correlation network approach can help to identify stress-dependent regulatory mechanisms in escherichia coli.
our current report is a logical extension of our continued effort to understand the regulation of the primary  and the secondary  metabolism in arabidopsis.
our approach contributes to the generation of new testable hypotheses for further experiments and may expand our fundamental understanding of the metabolic behaviors affected by genetic and/or environmental perturbations.
such information will contribute to the characterization of unknown gene function and help in the high-throughput screening of metabolic phenotypes ranging from experimental models to crops  <cit>  and disease studies  <cit> .
CONCLUSIONS
this study demonstrated that  differences in mutations and/or tissues affect changes in the topology of metabolomic correlation networks;  graph clustering yields tissue- and/or genotype-dependent metabolomic clusters related to respective biochemical pathways; and  metabolomic correlations complement information on changes in mean metabolite levels and the approach based on metabolomic correlations yields insights into the organization and regulation of metabolic modules.

 combining and integrating different'omics' data such as transcript-, protein-, and metabolite levels and enzyme activities is essential for a full understanding of the nature of the cellular metabolism as a system. with respect to transcript levels, a large amount of microarray data is publicly available for arabidopsis thaliana, a model plant. such large datasets facilitate the construction of gene co-expression databases and the survey of transcriptome organization. integrating transcript- and metabolite data by, for example, studying the correlation relationships among profiled data, facilitates the characterization of unknown gene functions, and furthers our understanding of plant cellular systems. the correlation between variables is also important for multivariate statistical analyses such as principal component analysis and hierarchical cluster analysis. typical metabolite-profiling data show a few, but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. the metabolomic correlation as well as gene co-expression are not always in agreement with known biochemical pathways. metabolomic correlation approaches have highlighted some properties in several species including plants. steuer et al., who provided a relationship between the structure of a metabolomic-correlation network and a metabolic reaction network using a jacobian matrix, found that the relationship is not simple. they pointed out that small fluctuations such as glucose availability can result in a certain correlation pattern and persist through metabolic pathways. using metabolic control analysis and correlation analysis based on metabolomic data, camacho et al. suggested that metabolites are strongly correlated when they respond in the same directions to all perturbations in enzyme levels. for example, mass conservation and chemical equilibrium were suggested as one origin of a high correlation. muller-linow et al. applied network similarity, a graph-theoretic parameter, to compare metabolomic correlation networks with biochemical reactions derived from the kegg database. they reported that these networks were in disagreement and that closeness in metabolomic correlation is not an indicator of closeness in biochemical networks. studies on the effect of changes in environmental conditions and temporal- and spatial assessments of the topology of metabolomic correlation networks have been reported. further investigation of the properties of metabolomic correlation networks may discover whether highly connected metabolites, the so-called'modules', in the correlation network reflect known biochemical pathways. we investigated similarities and dissimilarities in metabolomic correlations in the aerial parts of arabidopsis genotypes, col- wild-type, methionine-over accumulation, and transparent testa. elsewhere we reported that the mutation in cystathionine γ-synthase and/or the over-accumulation of methionine strongly affect the correlation networks in aerial parts of mto in the present study, using gas chromatography-time-of-flight/mass spectrometry, we measured the relative metabolite levels in root samples of the arabidopsis genotypes to assess tissue- and/or genotype-dependent changes in their metabolite levels. we systematically compared the metabolomic correlations observed in different datasets, the roots and the aerial parts. multivariate statistical analyses showed the distinct metabolome of these plants and tissues. we then constructed correlation networks by pair-wise correlation between the metabolites and performed graph clustering using the dpclus algorithm that efficiently extracts densely connected metabolites in a large-scale network. we then evaluated the obtained clusters with kegg enrichment analysis. our results demonstrate that changes in each network topology are tissue- and/or genotype-dependent and that they reflect, at least partially, known biochemical pathways in arabidopsis.  metabolic phenotypes of the roots of arabidopsis genotypes the experimental workflow is shown in figure roots of col- wild-type, mto and tt mutants were sampled and analyzed. we detected metabolite peaks including mass spectral tags by the gc-tof/ms-based metabolite profiling we established for arabidopsis. of these, were known metabolites including carbohydrates, amino-, fatty-, and organic acids and vitamins, and others were secondary metabolites. for comparisons with data from aerial parts we selected commonly-detected metabolites in both datasets using metmask http://metmask.sourceforge.net, a tool for metabolite identifier linking. by pca we visually inspected the metabolite profiles of the roots and aerial parts based on commonly detected metabolites and all peaks. to confirm the most important principal components of these samples we prepared score plots for each dataset. as in aerial parts, samples of mto roots were clearly separated while samples of tt roots were close to wt along the second component axis. the loading plots highlighted and visualized metabolites with a significant role in genotype separation. for example, methionine, urea, glycine, and pipecolate had a strong impact on the roots and aerial parts of mto and tt. the complete list of metabolites that contributed to the separation of the mutant profile groups from wt is shown in additional file in the pca score plots of the metabolites, the first principal component clearly discriminated between mto1- and the control samples. to obtain further insights into the interpretation of our pca results, we re-calculated pca by removing the four most influencing metabolites from the original data matrices. additional file shows the score and loading plots of roots and aerial parts using metabolite datasets. the pca plots revealed that asparagine and glutamine strongly contributed to pc separation in both the roots and aerial parts. the number of significant correlations depends on the tissue/genotype and their differential correlations to evaluate the correlations among the metabolite profiles observed in each tissue and genotype we calculated the spearman correlation coefficient. the significant correlations <; see ref ) for each tissue and each genotype are shown in figure all resultant correlations are listed in additional file we first examined the number of significant correlations between tissues. we found that the number of significant correlations in wt and mto roots was larger than in their aerial parts; it was also much larger in the aerial parts of tt than its roots. then, we compared differences between correlations in roots and aerial parts using fisher z-transformation. similarly, we also compared differential correlations between genotypes. the number of statistical differences between correlations in roots and aerial parts in wt, mto and tt was and although there were no significant changes in the correlations between genotypes except for differential correlations between wt and tt in roots. our findings imply that in both tissue-types, the mto mutation strongly affects the metabolomic correlation network, and that there are many correlations intensified by a lack of the chalcone synthase gene. our observations also suggest that the tendency for changes in the number of significant correlations is similar within the studied tissues of the genotypes. construction of a metabolomic correlation network and selection of the correlation threshold for visualization and to gain insights into the metabolomic correlations for each tissue and genotype we constructed correlation networks. the selection of an appropriate threshold is important for the construction of such a correlation network, if it is too high the generated network will be sparse, if too low, only a large connected component will be produced. to assess threshold-dependent changes in the network topology we computed graph-theoretic measures, i.e., the graph density, clustering coefficient, average degree, average path length, number of connected components, and the number of edges. we also compared the statistics of the real data and randomized data. we observed that the number of connected components showed a transition from small to large at a correlation threshold of. therefore, we set the threshold at r ≥. although a threshold does not guarantee explicit biological significance, we found that examining various statistics among the constructed networks was useful for selection. graph clustering identifies modules significantly enriched for metabolites contained in biochemical pathways at r ≥, dpclus identified and clusters in the metabolomic correlation network for the root samples of wt, mto and tt respectively; they ranged in size from to metabolites. for the aerial parts of wt, mto and tt we obtained and clusters, respectively; they ranged in size from to metabolites. we posit that this difference in the number of clusters reflects differences in the network topology, which, in turn, is tissue- and genotype-dependent. to assess the significance of the clusters we used the over-represented kegg pathways in the obtained- and randomized clusters. the results of graph clustering with kegg enrichment analysis for mto roots are presented in figure our findings on the other genotypes are listed in table 2; they are visualized in additional file the clusters obtained with the graph clustering method involved the enriched kegg pathway and included the'alanine-, aspartate-, and glutamate metabolism' in mto roots. the statistical relevance of this approach was evaluated with the s-value that can be used for assessing the significance of the clustering results based on kegg pathways. as shown in figure the averaged best p-value for significantly-enriched kegg pathways was better in real- than randomized data. a is the number of metabolites in the kegg pathway belonging to the dpclus cluster, a+b indicates the total number of metabolites in the kegg pathway, a+c the number of metabolites in the dpclus cluster, and n the total number of metabolites in the profiling data. rows reflect presence/absence within the kegg pathway. columns contain each of the dpclus clusters. for each kegg pathway the metabolites belonging to the comparison groups are shown. predominant pathways in clusters reflect tissue- and/or genotype-dependent metabolomic regulation except for the clusters found in the aerial parts of wt and mto the detected clusters tended to include steroid biosynthetic pathways. the strong correlation between sterols confirmed our earlier observations for aerial parts. metabolites belonging to fatty-acid biosynthesis tended to cluster together. the clusters we commonly found in the aerial parts of the genotypes were galactose metabolism. the largest cluster for the aerial parts and roots of mto were'alanine, aspartate, and glutamate metabolism.' this suggests that the mutation resulting in met over-accumulation can affect the metabolic pathway of the aspartate family. metabolite enrichment analysis using other mto mutants also extracted the same pathways. the largest cluster in the roots of tt was'biosynthesis of alkaloids derived from the shikimate pathway.' for both tissues, tt which lacks the gene encoding chs, showed less pronounced changes in the metabolite levels belonging to this biosynthetic pathway. discussion we focused on the changes in the topology of metabolomic correlation networks of arabidopsis genotypes. when comparing the wt, the increases and decreases in the number of significant correlations for mto and tt were similar in their aerial parts and roots. our comparative studies showed strongly altered metabolomic correlations in the roots of mto as did kusano et al. in aerial parts, we observed a marked increase in the number of significant correlations in the roots of tt compared to the roots of wt. as our study imposed no environmental perturbations on the genotypes, we suggest that the observed changes in the topology of metabolomic correlation networks are attributable to underlying tissue- and/or genotype-dependent biochemical regulations. graph clustering using dpclus yielded densely connected metabolites on the metabolomic correlation networks. kegg enrichment analysis to assess the statistical significance of the detected clusters demonstrated specific differences in the clusters in the enriched pathways. we posit that the assigned kegg pathways for each cluster reflect differences in underlying genetic properties and in tissue- and/or genotype-dependent biochemical regulations. the largest cluster in mto roots was assigned as the'alanine, aspartate, and glutamate metabolism'. since mto mutants are characterized by mutations involving the aspartate pathway, the approach based on graph-clustering proved itself useful for characterizing genotypes. although there were no significant enriched kegg pathways in cluster in figure this cluster may represent the extensive coordination among biosynthetic pathways involved in glycolysis, the tricarboxylic acid -cycle, and stress-responsive metabolites in mto roots. our approach may yield new insights into the organization of metabolites in the functional pathways of a given organism. in tt roots, the largest cluster was'biosynthesis of alkaloids derived from the shikimate pathway'. this cluster contained metabolites associated with the biosynthetic pathways of the tca cycle and with glycolysis because this kegg pathway covered extensive pathways from glycolysis to alkaloids via shikimate for the biosynthesis of aromatic amino acids including phenylalanine, tyrosine, and tryptophan. the mutation in the chs gene appears not to affect the biosynthesis of alkaloids in arabidopsis. we posit that the model plant arabidopsis lacks the production of complex alkaloids, although homologues for many enzymes associated with alkaloid biosynthesis have been detected in its genome. our multivariate statistical analysis suggests that the mutation of chs may influence nitrogen assimilation. furthermore, the third cluster in tt roots was significantly enriched for metabolites contained in'nitrogen metabolism', suggesting that our approach may reflect underlying changes in the metabolism by lacking of chs in arabidopsis. taken together, our observations demonstrate that variations in the topology of correlation networks are tissue and/or genotype-dependent and reflect at least partially known biochemical pathways in arabidopsis. earlier studies indicated that the interpretation of metabolomic correlations requires careful evaluation. our work demonstrates that graph clustering can be used to gather metabolites belonging to the metabolic pathways that change in response to different regulations that in turn are dependent on the tissue and/or genotype. our analyses differ from those of in that we examined the densely connected metabolites in the correlation network by statistical kegg enrichment analysis instead of using network similarity or proximity. graph clustering approaches have been effectively applied to gene co-expression networks for extracting functional, densely connected genes. we showed that the approach is also effective for metabolomic correlations. although we only evaluated genotypes, the approach based on graph clustering seems to be useful and applicable to the monitoring of changes in the topology of correlation networks. as in the gene co-expression analysis that involves thousands of microarrays, there were few negative correlations in this study. for instance, the number of significant negative correlations in the roots and aerial parts of wt was and respectively. therefore, we deliberately did not use negative correlations. other similarity measures such as mutual information and partial correlation, instead of spearman correlation coefficients, may be applied in this approach. further studies are necessary to assess to what extent these measures affect the graph clustering results of metabolomic correlation networks. there are few systematic comparisons of metabolomic correlations among time series data. temporal and spatial assessment of the topology in the metabolomic correlation network may contribute to the development of novel data-mining methods and to the discovery of biomarkers. there are also few published systematic metabolomic comparisons under stress conditions. szymanski et al. demonstrated that the correlation network approach can help to identify stress-dependent regulatory mechanisms in escherichia coli. our current report is a logical extension of our continued effort to understand the regulation of the primary and the secondary metabolism in arabidopsis. our approach contributes to the generation of new testable hypotheses for further experiments and may expand our fundamental understanding of the metabolic behaviors affected by genetic and/or environmental perturbations. such information will contribute to the characterization of unknown gene function and help in the high-throughput screening of metabolic phenotypes ranging from experimental models to crops and disease studies.  this study demonstrated that differences in mutations and/or tissues affect changes in the topology of metabolomic correlation networks; graph clustering yields tissue- and/or genotype-dependent metabolomic clusters related to respective biochemical pathways; and metabolomic correlations complement information on changes in mean metabolite levels and the approach based on metabolomic correlations yields insights into the organization and regulation of metabolic modules. 
metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules. in our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry and used published information on the aerial parts of arabidopsis genotypes, col- wild-type, methionine over-accumulation, and transparent testa to compare systematically the metabolomic correlations in samples of roots and aerial parts. although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. we found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways. deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. we then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis.
 deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways.  in our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry and used published information on the aerial parts of arabidopsis genotypes, col- wild-type, methionine over-accumulation, and transparent testa to compare systematically the metabolomic correlations in samples of roots and aerial parts. we then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. we found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways.  we demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules. 