Limits...
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.


Venn diagram of differential features and compound spectra in the wildtype-mutant experiment for the significance level of α = 0.01. Left: number of features detected by univariate and multivariate method. Right: number of compound spectra detected by the multivariate method, compared to the number of compound spectra where at least one feature was detected univariately.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585098&req=5

Figure 2: Venn diagram of differential features and compound spectra in the wildtype-mutant experiment for the significance level of α = 0.01. Left: number of features detected by univariate and multivariate method. Right: number of compound spectra detected by the multivariate method, compared to the number of compound spectra where at least one feature was detected univariately.

Mentions: As shown in Figure 2 (left), 5 features are reported exclusively by the univariate method, while the multivariate approach detected 23 features exclusively, both at a significance level of α = 0.01.


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)

Venn diagram of differential features and compound spectra in the wildtype-mutant experiment for the significance level of α = 0.01. Left: number of features detected by univariate and multivariate method. Right: number of compound spectra detected by the multivariate method, compared to the number of compound spectra where at least one feature was detected univariately.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Venn diagram of differential features and compound spectra in the wildtype-mutant experiment for the significance level of α = 0.01. Left: number of features detected by univariate and multivariate method. Right: number of compound spectra detected by the multivariate method, compared to the number of compound spectra where at least one feature was detected univariately.
Mentions: As shown in Figure 2 (left), 5 features are reported exclusively by the univariate method, while the multivariate approach detected 23 features exclusively, both at a significance level of α = 0.01.

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.