A randomization test for PLS component selection

Wiklund, Susanne; Nilsson, David; Eriksson, Lennart; Sjöstróm, Michael; Wold, Svante; Faber, Klaas

doi:10.1002/cem.1086

Cited by 127 publications

(126 citation statements)

References 56 publications

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“…To guard against model overfitting, permutation tests with 100 iterations were performed (Fig. 3D) to compare the goodness of fit of the original model with that of randomly permuted models (28). The criteria for validity included the following: all the permuted R 2 and Q 2 values to the left were lower than the original points to the right and the regression line of the Q 2 points intersects the vertical axis (on the left) at or below zero (29).…”

Section: Metabonomic Profiling Of Rplc-ms and Hilic-ms-bothmentioning

confidence: 99%

Bladder Cancer Determination Via Two Urinary Metabolites: A Biomarker Pattern Approach

Huang

Lin

Gao

et al. 2011

Molecular & Cellular Proteomics

122

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The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for lowgrade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer

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Section: Metabonomic Profiling Of Rplc-ms and Hilic-ms-bothmentioning

confidence: 99%

Bladder Cancer Determination Via Two Urinary Metabolites: A Biomarker Pattern Approach

Huang

Lin

Gao

et al. 2011

Molecular & Cellular Proteomics

122

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show abstract

“…The selected number of components using k-fold CV correctly find this range, the actual value of the number of components is immaterial as long as the prediction error is close to its minimum (Wiklund et al, 2007;Ibrahim and Wibowo, 2012). We used 10-fold CV to obtain the appropriate model for predicting water level at Galas River of Kuala Krai using two types of pre-processing data.…”

Section: Model Selectionmentioning

confidence: 95%

“…In this study, we will restrict ourselves to the common variants of CV called K-fold CV, where the calibration objects are divided in k segments and for this experiment we use k = 10 (Breiman, 1984;Wiklund et al, 2007;Ibrahim and Wibowo, 2012). The selected number of components using k-fold CV correctly find this range, the actual value of the number of components is immaterial as long as the prediction error is close to its minimum (Wiklund et al, 2007;Ibrahim and Wibowo, 2012).…”

Section: Model Selectionmentioning

confidence: 99%

Partial Least Squares Regression Based Variables Selection for Water Level Predictions

Ibrahim¹

2013

American Journal of Applied Sciences

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Floods are common phenomenon in the state of Kuala Krai, specifically in Kelantan-Malaysia. Every year, floods affecting biodiversity on this region and also causing property loss of this residential area. The residents in Kelantan always suffered from floods since the water overflows to the areas adjoining to the rivers, lakes or dams. Months, average monthly rainfall, temperature, relative humidity and surface wind were used as predictors while the water level of Galas River was used as response. The selection of suitable predictor variables becomes an important issue for developing prediction model since the analysis data uses many variables from meteorological and hydrogical departments. In this study, we conduct K-fold Cross-Validation (CV) to select the important variables for the water level predictions. A suitable prediction model is needed to forecast the water level in Galas River by adopting the Ordinary Linear Regression (OLR) and Partial Least Squares Regression (PLSR). However, we need to perform pre-processing data of the datasets since the original data contain missing data. We perform two types of pre-processing data which are using mean of the corresponding months (type I pre-processing data) and OLR (type II pre-processing data) of missing data. Based on the experiment, PLSR is more suitable model rather than OLR for predicting the water level in Galas River and the use of the type I preprocessing data gives higher accuracy than the type II pre-processing data.

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“…The PLS models were validated using response of permutation test through 100 permutations. The permutation test assesses the statistical significance of the estimated predictive power previously calculated by cross validation test 40 .…”

Section: Statistical Validation Of the Modelmentioning

confidence: 99%

Detection of Butter Adulteration with Lard by Employing <sup>1</sup>H-NMR Spectroscopy and Multivariate Data Analysis

et al. 2015

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A randomization test for PLS component selection

Cited by 127 publications

References 56 publications

Bladder Cancer Determination Via Two Urinary Metabolites: A Biomarker Pattern Approach

Bladder Cancer Determination Via Two Urinary Metabolites: A Biomarker Pattern Approach

Partial Least Squares Regression Based Variables Selection for Water Level Predictions

Detection of Butter Adulteration with Lard by Employing <sup>1</sup>H-NMR Spectroscopy and Multivariate Data Analysis

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