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A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments.

Robinson MD, De Souza DP, Keen WW, Saunders EC, McConville MJ, Speed TP, Likić VA - BMC Bioinformatics (2007)

Bottom Line: When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment).This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment.The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3050, Australia. mrobinson@wehi.edu.au

ABSTRACT

Background: Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies.

Results: A new approach for matching signal peaks based on dynamic programming is presented. The proposed approach relies on both peak retention times and mass spectra. The alignment of more than two peak lists involves three steps: (1) all possible pairs of peak lists are aligned, and similarity of each pair of peak lists is estimated; (2) the guide tree is built based on the similarity between the peak lists; (3) peak lists are progressively aligned starting with the two most similar peak lists, following the guide tree until all peak lists are exhausted. When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment). When more than two sets of replicate experiments are present, the between-state alignment also employs the guide tree. We demonstrate the usefulness of this approach on GC-MS metabolic profiling experiments acquired on wild-type and mutant Leishmania mexicana parasites.

Conclusion: We propose a progressive method to match signal peaks across multiple GC-MS experiments based on dynamic programming. A sensitive peak similarity function is proposed to balance peak retention time and peak mass spectra similarities. This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment. The accuracy of the proposed method was close to the accuracy of manually-curated peak matching, which required tens of man-hours for the analyzed data sets. The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.

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The accuracy of peak alignment as a function of gap penalty. The accuracy of dynamic programming peak alignment as a function of gap penalty G, shown on the x-axis. Eight replicate experiments of L mexicana polar extracts were processed, generating eight peak lists. The peak lists were aligned with the dynamic programming for a range of the gap penalty parameter between 0.10 and 0.55 (the retention time tolerance was fixed D = 2.5 s), and the resulting alignment tables were compared to the correct alignment table built manually. The top panel shows the total number of errors in the alignment. The middle panel shows the number of errors of type A (solid line) and type B (dashed line). The bottom panel shows the total number of metabolites in the resulting alignment. The correct number of metabolites is 173, shown in the dashed line.
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Figure 6: The accuracy of peak alignment as a function of gap penalty. The accuracy of dynamic programming peak alignment as a function of gap penalty G, shown on the x-axis. Eight replicate experiments of L mexicana polar extracts were processed, generating eight peak lists. The peak lists were aligned with the dynamic programming for a range of the gap penalty parameter between 0.10 and 0.55 (the retention time tolerance was fixed D = 2.5 s), and the resulting alignment tables were compared to the correct alignment table built manually. The top panel shows the total number of errors in the alignment. The middle panel shows the number of errors of type A (solid line) and type B (dashed line). The bottom panel shows the total number of metabolites in the resulting alignment. The correct number of metabolites is 173, shown in the dashed line.

Mentions: Figure 6 shows the number of errors in a peak alignment table as a function of gap penalty in the range G = 0.10 to 0.55, for the fixed retention time tolerance of D = 2.5 s. For small values of G, metabolite splitting errors (type B) predominated, while for large G values peak mixing errors (type A) predominated. The decrease in the total number of metabolites for low values of G (Figure 6, bottom panel) is counterintuitive, since low values of G would favor the insertion of gaps, which in turn would create more metabolite rows in the alignment table. This effect was however more than countered by the removal of spurious metabolites rows in post-processing (see Methods). In addition, some cases of metabolite splitting resulted in a complete removal of the metabolite from the alignment table, due to the effect shown in Figure 5(b).


A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments.

Robinson MD, De Souza DP, Keen WW, Saunders EC, McConville MJ, Speed TP, Likić VA - BMC Bioinformatics (2007)

The accuracy of peak alignment as a function of gap penalty. The accuracy of dynamic programming peak alignment as a function of gap penalty G, shown on the x-axis. Eight replicate experiments of L mexicana polar extracts were processed, generating eight peak lists. The peak lists were aligned with the dynamic programming for a range of the gap penalty parameter between 0.10 and 0.55 (the retention time tolerance was fixed D = 2.5 s), and the resulting alignment tables were compared to the correct alignment table built manually. The top panel shows the total number of errors in the alignment. The middle panel shows the number of errors of type A (solid line) and type B (dashed line). The bottom panel shows the total number of metabolites in the resulting alignment. The correct number of metabolites is 173, shown in the dashed line.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194738&req=5

Figure 6: The accuracy of peak alignment as a function of gap penalty. The accuracy of dynamic programming peak alignment as a function of gap penalty G, shown on the x-axis. Eight replicate experiments of L mexicana polar extracts were processed, generating eight peak lists. The peak lists were aligned with the dynamic programming for a range of the gap penalty parameter between 0.10 and 0.55 (the retention time tolerance was fixed D = 2.5 s), and the resulting alignment tables were compared to the correct alignment table built manually. The top panel shows the total number of errors in the alignment. The middle panel shows the number of errors of type A (solid line) and type B (dashed line). The bottom panel shows the total number of metabolites in the resulting alignment. The correct number of metabolites is 173, shown in the dashed line.
Mentions: Figure 6 shows the number of errors in a peak alignment table as a function of gap penalty in the range G = 0.10 to 0.55, for the fixed retention time tolerance of D = 2.5 s. For small values of G, metabolite splitting errors (type B) predominated, while for large G values peak mixing errors (type A) predominated. The decrease in the total number of metabolites for low values of G (Figure 6, bottom panel) is counterintuitive, since low values of G would favor the insertion of gaps, which in turn would create more metabolite rows in the alignment table. This effect was however more than countered by the removal of spurious metabolites rows in post-processing (see Methods). In addition, some cases of metabolite splitting resulted in a complete removal of the metabolite from the alignment table, due to the effect shown in Figure 5(b).

Bottom Line: When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment).This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment.The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3050, Australia. mrobinson@wehi.edu.au

ABSTRACT

Background: Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies.

Results: A new approach for matching signal peaks based on dynamic programming is presented. The proposed approach relies on both peak retention times and mass spectra. The alignment of more than two peak lists involves three steps: (1) all possible pairs of peak lists are aligned, and similarity of each pair of peak lists is estimated; (2) the guide tree is built based on the similarity between the peak lists; (3) peak lists are progressively aligned starting with the two most similar peak lists, following the guide tree until all peak lists are exhausted. When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment). When more than two sets of replicate experiments are present, the between-state alignment also employs the guide tree. We demonstrate the usefulness of this approach on GC-MS metabolic profiling experiments acquired on wild-type and mutant Leishmania mexicana parasites.

Conclusion: We propose a progressive method to match signal peaks across multiple GC-MS experiments based on dynamic programming. A sensitive peak similarity function is proposed to balance peak retention time and peak mass spectra similarities. This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment. The accuracy of the proposed method was close to the accuracy of manually-curated peak matching, which required tens of man-hours for the analyzed data sets. The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.

Show MeSH
Related in: MedlinePlus