Discriminant analysis by double stage principal component analysis

Hoogerbrugge, R; Willig, Simon J.; Kistemaker, Piet G.

doi:10.1021/ac00261a016

Cited by 130 publications

(64 citation statements)

References 20 publications

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“…Projections of individual sample spectra on these functions reveal the main characteristics of the spectra compared to the origin and thus give information on the molecular composition of the samples. Details on the procedure can be found elsewhere (Hoogerbrugge et al 1983, Windig et al 1983, Tas 1991. Klap 1997.…”

Section: Methodsmentioning

confidence: 99%

Retention of lignin in seagrasses:angiosperms that returned to the sea

Klap¹,

Hemminga²,

Boon³

2000

Mar. Ecol. Prog. Ser.

101

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Using Curie-point Pyrolysis Gas Chromatography Mass Spectrometry [Py-GCMS) and Direct Temperature-resolved Mass Spectrometry (DT-bfS), lignin was detected In highly purified preparations (Milled Wood Lignin = hlWL) of various tissues of the seagrasses Zostera rnarina and Posidonia oceanica. The results ind~cate that P. oceanica contains more lign~n than Z. manna and that roots and rhizomes generally contain more Lignin than leaves. It is concluded that the ability to produce lignin is not lost by the angiosperm ancestors of extant seagrasses upon their colonization of the marine environment. Relative lignin abundances in the different tissues appear to be posltlvely correlated with life span. It is suggested that lignification contributes to the longevity of a tissue by protecting lt against microbial attack, but that deposition of lignin in seagrasses is restricted to tissues that show limited growth.

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Section: Methodsmentioning

confidence: 99%

Retention of lignin in seagrasses:angiosperms that returned to the sea

Klap¹,

Hemminga²,

Boon³

2000

Mar. Ecol. Prog. Ser.

101

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“…PCA has been combined with linear discriminant analysis as the classifier applied to the PCA scores. This approach, originally proposed by Hoogerbrugge et al (19), has been used for feature selec-FIG. 1.…”

Section: Data Set Designmentioning

confidence: 99%

“…Our results provide guidelines for researchers who will engage in biomarker discovery or other differential profiling "omics" studies with respect to sample size and selecting the most appropriate feature selection method for a given data set. We evaluated the following approaches: univariate t test and Mann-Whitney-Wilcoxon test (mww test) with multiple testing correction (14), nearest shrunken centroid (NSC) (15,16), support vector machine-recursive features elimination (SVM-RFE) (17), PLSDA (18), and PCDA (19). PCDA and PLSDA were combined with the rank-product as a feature selection criterion (20).…”

mentioning

confidence: 99%

A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics

Christin

Hoefsloot

Smilde

et al. 2013

Molecular & Cellular Proteomics

134

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In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery-t test, the Mann-Whitney-Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machinerecursive features elimination (SVM-RFE), principal component discriminant analysis (PCDA), and partial least squares discriminant analysis (PLSDA)-using human urine and porcine cerebrospinal fluid samples that were spiked with a range of peptides at different concentration levels. The ideal feature selection method should select the complete list of discriminating features that are related to the spiked peptides without selecting unrelated features. Whereas many studies have to rely on classification error to judge the reliability of the selected biomarker candidates, we assessed the accuracy of selection directly from the list of spiked peptides. The feature selection methods were applied to data sets with different sample sizes and extents of sample class separation determined by the concentration level of spiked compounds. For each feature selection method and data set, the performance for selecting a set of features related to spiked compounds was assessed using the harmonic mean of the recall and the precision (f-score) and the geometric mean of the recall and the true negative rate (g-score). We conclude that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes (n ‫؍‬ 6), but their performance improves markedly with increasing sample size up to a point (n > 12) at which they outperform the other methods. PCDA and PLSDA select small feature sets with high precision but miss many true positive features related to the spiked peptides. NSC strikes a reasonable compromise between recall and precision for all data sets independent of spiking level and number of samples. Linear SVM-RFE performs poorly for selecting features related to the spiked compounds, even though the classification error is relatively low. Molecular & Cellular

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“…The most frequent applied methods for classification problems in metabolomics are PLS-DA [63] and PCDA [64]. One of the main problems is the chance of overfit and finding methods to avoid this.…”

Section: Discussionmentioning

confidence: 99%