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Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

Trutschel D, Schmidt S, Grosse I, Neumann S - Front Bioeng Biotechnol (2015)

Bottom Line: After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis.For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed.The spectra-wise analysis showed better detection results for all simulated effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Halle , Germany ; Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle , Germany.

ABSTRACT
Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student's t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects.

No MeSH data available.


Results of univariate and multivariate methods in feature detection are compared on the feature level (upper). At the compound spectra level (lower) the results of different grouping analysis approaches are shown. For each simulation step, several added effects of 0.2, 0.3, …, 1.4, 1.5 on the “mutant” class, the mean and SE of the evaluated AUCs (results from 100 repetitions) are plotted.
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Figure 4: Results of univariate and multivariate methods in feature detection are compared on the feature level (upper). At the compound spectra level (lower) the results of different grouping analysis approaches are shown. For each simulation step, several added effects of 0.2, 0.3, …, 1.4, 1.5 on the “mutant” class, the mean and SE of the evaluated AUCs (results from 100 repetitions) are plotted.

Mentions: The next question was the behavior of the methods for different effects. The AUC was used as a summary metric of the performance. Figure 4 shows that the multivariate T2 as well as the diagonal T2 method has a better AUC for the feature detection compared to the univariate approach for all effects of 0.2, 0.3, …, 1.4, 1.5. To improve the generalization, the sampling of the “mutant” data was repeated 100 times for each effect. Especially for smaller effects, the benefit of the multivariate approach is visible and also that the simplified diagonal T2 approximates to the original T2 for larger effects.


Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.

Trutschel D, Schmidt S, Grosse I, Neumann S - Front Bioeng Biotechnol (2015)

Results of univariate and multivariate methods in feature detection are compared on the feature level (upper). At the compound spectra level (lower) the results of different grouping analysis approaches are shown. For each simulation step, several added effects of 0.2, 0.3, …, 1.4, 1.5 on the “mutant” class, the mean and SE of the evaluated AUCs (results from 100 repetitions) are plotted.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585098&req=5

Figure 4: Results of univariate and multivariate methods in feature detection are compared on the feature level (upper). At the compound spectra level (lower) the results of different grouping analysis approaches are shown. For each simulation step, several added effects of 0.2, 0.3, …, 1.4, 1.5 on the “mutant” class, the mean and SE of the evaluated AUCs (results from 100 repetitions) are plotted.
Mentions: The next question was the behavior of the methods for different effects. The AUC was used as a summary metric of the performance. Figure 4 shows that the multivariate T2 as well as the diagonal T2 method has a better AUC for the feature detection compared to the univariate approach for all effects of 0.2, 0.3, …, 1.4, 1.5. To improve the generalization, the sampling of the “mutant” data was repeated 100 times for each effect. Especially for smaller effects, the benefit of the multivariate approach is visible and also that the simplified diagonal T2 approximates to the original T2 for larger effects.

Bottom Line: After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis.For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed.The spectra-wise analysis showed better detection results for all simulated effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Halle , Germany ; Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle , Germany.

ABSTRACT
Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student's t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects.

No MeSH data available.