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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.


Different decisions from univariate and multivariate test to detect differential features or compound spectra. Each gray rectangles marks the confidence interval of one test dimension, so the intersection of two rectangles marks the combined confidence region. The blue ellipse is the confidence region for a multivariate test. There are six different possibilities (six different colored spaces) for the position of the origin corresponding to the  hypotheses marked by a red “+.”
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Figure 1: Different decisions from univariate and multivariate test to detect differential features or compound spectra. Each gray rectangles marks the confidence interval of one test dimension, so the intersection of two rectangles marks the combined confidence region. The blue ellipse is the confidence region for a multivariate test. There are six different possibilities (six different colored spaces) for the position of the origin corresponding to the hypotheses marked by a red “+.”

Mentions: If independent univariate tests for two features in a compound spectrum are combined, the confidence interval becomes a rectangular confidence region as shown in Figure 1, or in general for groups with p features a p-dimensional hypercube. Even if multiple testing correction is done, the confidence region holds a hypercube.


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)

Different decisions from univariate and multivariate test to detect differential features or compound spectra. Each gray rectangles marks the confidence interval of one test dimension, so the intersection of two rectangles marks the combined confidence region. The blue ellipse is the confidence region for a multivariate test. There are six different possibilities (six different colored spaces) for the position of the origin corresponding to the  hypotheses marked by a red “+.”
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Different decisions from univariate and multivariate test to detect differential features or compound spectra. Each gray rectangles marks the confidence interval of one test dimension, so the intersection of two rectangles marks the combined confidence region. The blue ellipse is the confidence region for a multivariate test. There are six different possibilities (six different colored spaces) for the position of the origin corresponding to the hypotheses marked by a red “+.”
Mentions: If independent univariate tests for two features in a compound spectrum are combined, the confidence interval becomes a rectangular confidence region as shown in Figure 1, or in general for groups with p features a p-dimensional hypercube. Even if multiple testing correction is done, the confidence region holds a hypercube.

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.