Limits...
Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

Show MeSH

Related in: MedlinePlus

P-value scatter plots comparing pathway enrichments at the 10% cut-off level for the CO2 dataset for (a) HMM3 and Pearson, (b) combined HMM3+Pearson and Pearson. The gray line corresponds to FDR<0.05. Points in the upper-left part of the graph indicate a higher enrichment for the method on the y-axis; points in the bottom-right corner indicate a higher enrichment for the method on the x-axis. The farther the points are from the diagonal, the higher is the enrichment gain of the best method. In the left plot, we observe the presence of pathways with higher enrichment for either HMM or Pearson. This indicates that each method recovers coresponses on distinct pathways. In the right plots, we see that the combination of Pearson and HMM has an overall higher enrichment of pathways than Pearson alone. PS: photosynthesis; Starch/suc: starch and sucrose synthesis pathway.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3117345&req=5

Figure 7: P-value scatter plots comparing pathway enrichments at the 10% cut-off level for the CO2 dataset for (a) HMM3 and Pearson, (b) combined HMM3+Pearson and Pearson. The gray line corresponds to FDR<0.05. Points in the upper-left part of the graph indicate a higher enrichment for the method on the y-axis; points in the bottom-right corner indicate a higher enrichment for the method on the x-axis. The farther the points are from the diagonal, the higher is the enrichment gain of the best method. In the left plot, we observe the presence of pathways with higher enrichment for either HMM or Pearson. This indicates that each method recovers coresponses on distinct pathways. In the right plots, we see that the combination of Pearson and HMM has an overall higher enrichment of pathways than Pearson alone. PS: photosynthesis; Starch/suc: starch and sucrose synthesis pathway.

Mentions: Pathway level analysis provides a direct way to search for interpretable coresponse patterns between metabolites and transcripts. To further visualize these results, we performed side-by-side comparison of the Fisher exact test P-values at the 10% cut-off level using Pearson and the HMM method of choice for each pathway (Fig. 7 and Supplementary Figs. S4–S7). Similar to the results seen for the lift charts, it is clear that HMM and Pearson are complementary, i.e. they show enrichment for distinct pathways. Moreover, this complementarity is successfully summarized using our OPLS-DA-based approach, which shows an overall higher enrichment than Pearson alone.Fig. 7.


Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

P-value scatter plots comparing pathway enrichments at the 10% cut-off level for the CO2 dataset for (a) HMM3 and Pearson, (b) combined HMM3+Pearson and Pearson. The gray line corresponds to FDR<0.05. Points in the upper-left part of the graph indicate a higher enrichment for the method on the y-axis; points in the bottom-right corner indicate a higher enrichment for the method on the x-axis. The farther the points are from the diagonal, the higher is the enrichment gain of the best method. In the left plot, we observe the presence of pathways with higher enrichment for either HMM or Pearson. This indicates that each method recovers coresponses on distinct pathways. In the right plots, we see that the combination of Pearson and HMM has an overall higher enrichment of pathways than Pearson alone. PS: photosynthesis; Starch/suc: starch and sucrose synthesis pathway.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117345&req=5

Figure 7: P-value scatter plots comparing pathway enrichments at the 10% cut-off level for the CO2 dataset for (a) HMM3 and Pearson, (b) combined HMM3+Pearson and Pearson. The gray line corresponds to FDR<0.05. Points in the upper-left part of the graph indicate a higher enrichment for the method on the y-axis; points in the bottom-right corner indicate a higher enrichment for the method on the x-axis. The farther the points are from the diagonal, the higher is the enrichment gain of the best method. In the left plot, we observe the presence of pathways with higher enrichment for either HMM or Pearson. This indicates that each method recovers coresponses on distinct pathways. In the right plots, we see that the combination of Pearson and HMM has an overall higher enrichment of pathways than Pearson alone. PS: photosynthesis; Starch/suc: starch and sucrose synthesis pathway.
Mentions: Pathway level analysis provides a direct way to search for interpretable coresponse patterns between metabolites and transcripts. To further visualize these results, we performed side-by-side comparison of the Fisher exact test P-values at the 10% cut-off level using Pearson and the HMM method of choice for each pathway (Fig. 7 and Supplementary Figs. S4–S7). Similar to the results seen for the lift charts, it is clear that HMM and Pearson are complementary, i.e. they show enrichment for distinct pathways. Moreover, this complementarity is successfully summarized using our OPLS-DA-based approach, which shows an overall higher enrichment than Pearson alone.Fig. 7.

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

Show MeSH
Related in: MedlinePlus