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An extension of PPLS-DA for classification and comparison to ordinary PLS-DA.

Telaar A, Liland KH, Repsilber D, Nürnberg G - PLoS ONE (2013)

Bottom Line: For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA.A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error.Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genetics and Biometry, Department of Bioinformatics and Biomathematics, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.

ABSTRACT
Classification studies are widely applied, e.g. in biomedical research to classify objects/patients into predefined groups. The goal is to find a classification function/rule which assigns each object/patient to a unique group with the greatest possible accuracy (classification error). Especially in gene expression experiments often a lot of variables (genes) are measured for only few objects/patients. A suitable approach is the well-known method PLS-DA, which searches for a transformation to a lower dimensional space. Resulting new components are linear combinations of the original variables. An advancement of PLS-DA leads to PPLS-DA, introducing a so called 'power parameter', which is maximized towards the correlation between the components and the group-membership. We introduce an extension of PPLS-DA for optimizing this power parameter towards the final aim, namely towards a minimal classification error. We compare this new extension with the original PPLS-DA and also with the ordinary PLS-DA using simulated and experimental datasets. For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA. A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error. On the contrary, for the data set with strong between-feature collinearity and a low proportion of differentially expressed genes and a large total number of genes, the prediction error of PPLS-DA and the extensions is clearly lower than for PLS-DA. Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

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Related in: MedlinePlus

Rough overview of the proposed extension of PPLS-DA.
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pone-0055267-g001: Rough overview of the proposed extension of PPLS-DA.

Mentions: We calculate the prediction error (PE), the proportion of wrongly classified samples of a test set, as a measure for good classification. In Figure 1 a rough overview of our proposed extension is given. In this paper, all cross-validation procedures consist of random samples of the corresponding data sets to the proportions of 0.7 (training set) and 0.3 (test set). For example, a cross-validation with 10 repeats, repeats the sampling 10 times. Utilizing the statistical software R, we use the function cppls (of the R-package pls) for PPLS-DA and the function lda (of the R-package MASS). Furthermore, we use the default setting for the priors in the lda function, using the proportions of the groups which are equal in our cases.


An extension of PPLS-DA for classification and comparison to ordinary PLS-DA.

Telaar A, Liland KH, Repsilber D, Nürnberg G - PLoS ONE (2013)

Rough overview of the proposed extension of PPLS-DA.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569448&req=5

pone-0055267-g001: Rough overview of the proposed extension of PPLS-DA.
Mentions: We calculate the prediction error (PE), the proportion of wrongly classified samples of a test set, as a measure for good classification. In Figure 1 a rough overview of our proposed extension is given. In this paper, all cross-validation procedures consist of random samples of the corresponding data sets to the proportions of 0.7 (training set) and 0.3 (test set). For example, a cross-validation with 10 repeats, repeats the sampling 10 times. Utilizing the statistical software R, we use the function cppls (of the R-package pls) for PPLS-DA and the function lda (of the R-package MASS). Furthermore, we use the default setting for the priors in the lda function, using the proportions of the groups which are equal in our cases.

Bottom Line: For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA.A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error.Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

View Article: PubMed Central - PubMed

Affiliation: Institute for Genetics and Biometry, Department of Bioinformatics and Biomathematics, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.

ABSTRACT
Classification studies are widely applied, e.g. in biomedical research to classify objects/patients into predefined groups. The goal is to find a classification function/rule which assigns each object/patient to a unique group with the greatest possible accuracy (classification error). Especially in gene expression experiments often a lot of variables (genes) are measured for only few objects/patients. A suitable approach is the well-known method PLS-DA, which searches for a transformation to a lower dimensional space. Resulting new components are linear combinations of the original variables. An advancement of PLS-DA leads to PPLS-DA, introducing a so called 'power parameter', which is maximized towards the correlation between the components and the group-membership. We introduce an extension of PPLS-DA for optimizing this power parameter towards the final aim, namely towards a minimal classification error. We compare this new extension with the original PPLS-DA and also with the ordinary PLS-DA using simulated and experimental datasets. For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA. A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error. On the contrary, for the data set with strong between-feature collinearity and a low proportion of differentially expressed genes and a large total number of genes, the prediction error of PPLS-DA and the extensions is clearly lower than for PLS-DA. Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

Show MeSH
Related in: MedlinePlus