Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data

Palermo, Giuseppe; Piraino, Paolo; Zucht, Hans‐Dieter

doi:10.2147/aabc.s3619

Cited by 88 publications

(86 citation statements)

References 13 publications

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“…Feature selection was used to identify the top ϳ10% of all metabolic predictors for T1D from each biochemical domain. The full variable set was filtered to retain analytes that dispalyed 1) significant correlation with model scores (Spearman's pFDR Յ 0.05) (58) and 2) model loadings on LV1 in the top 90th quantile in magnitude (41) and Mann-Whitney U-test P values Ͻ 0.05.…”

Section: Discussionmentioning

confidence: 99%

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

Fahrmann

Grapov

Yang

et al. 2015

American Journal of Physiology-Endocrinology and Metabolism

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Hara M. Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia. Am J Physiol Endocrinol Metab 308: E978-E989, 2015. First published April 8, 2015; doi:10.1152/ajpendo.00019.2015.-Nonobese diabetic (NOD) mice are a commonly used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra-highperformance liquid chromatography-accurate mass quadruple time-offlight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spectrometry-based methodologies, was used to capture metabolic alterations in the metabolome and lipidome of plasma from NOD mice progressing or not progressing to T1D. Using this multi-platform approach, we identified Ͼ1,000 circulating lipids and metabolites in male and female progressor and nonprogressor animals (n ϭ 71). Statistical and multivariate analyses were used to identify age-and sex-independent metabolic markers, which best differentiated metabolic profiles of progressors and nonprogressors. Key T1D-associated perturbations were related with 1) increases in oxidation products glucono-␦-lactone and galactonic acid and reductions in cysteine, methionine and threonic acid, suggesting increased oxidative stress; 2) reductions in circulating polyunsaturated fatty acids and lipid signaling mediators, most notably arachidonic acid (AA) and AAderived eicosanoids, implying impaired states of systemic inflammation; 3) elevations in circulating triacylglyercides reflective of hypertriglyceridemia; and 4) reductions in major structural lipids, most notably lysophosphatidylcholines and phosphatidylcholines. Taken together, our results highlight the systemic perturbations that accompany a loss of glycemic control and development of overt T1D. diabetic mice; metabolomics; inflammation; oxidative stress TYPE 1 DIABETES (T1D) is an autoimmune disease characterized by the selective destruction of pancreatic ␤-cells. Currently, it is considered that the autoimmune insult is the primary driver of ␤-cell destruction that leads to development of T1D. However, it is becoming increasingly evident that early metabolic perturbations are inherently involved in the development and progression of T1D (39,40,53). These metabolic alterations may potentiate or attenuate ␤-cell loss and dysfunction. Oresic et al. (40) reported that alterations in branched-chain amino acids (BCAA) and lipid metabolism preceded the appearance of autoantibodies in children who later progressed to T1D. Pflueger et al. (44) found that, independent of age-related differences, autoantibody-positive children had higher levels of odd-chain triglycerides and polyunsaturated fatty acid (PUFA)-containing phospholipids than autoantibody-negative children. Furthermore, it was found that children who developed autoantibodies by age 2 yr had a signific...

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Section: Discussionmentioning

confidence: 99%

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

Fahrmann

Grapov

Yang

et al. 2015

American Journal of Physiology-Endocrinology and Metabolism

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“…Feature selection was implemented to identify the top ~10% (42 out of 462) of all metabolic predictors for cancer. The full variable set was filtered to retain metabolites which displayed significant correlation with model scores (Spearman’s pFDR ≤ 0.05) (16) and model loadings on OLV1 in the top 90th quantile in magnitude (17). …”

Section: Methodsmentioning

confidence: 99%

Metabolomic Markers of Altered Nucleotide Metabolism in Early Stage Adenocarcinoma

Wikoff

Grapov

Fahrmann

et al. 2015

Cancer Prevention Research

109

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Adenocarcinoma, a type of non-small-cell lung cancer (NSCLC), is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and non-malignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and non-malignant lung tissue pairs from current or former smokers with early stage (Stage IA–IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared to non-malignant tissue. Cancer-associated biochemical alterations were characterized by: 1) decreased glucose levels, consistent with the Warburg effect, 2) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, 3) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, 4) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis and 5) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.

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“…LMASS was predicted with an R 2 value of 0.62 using two PLS factors. The PLS algorithm produces variable importance (VIP) scores that are used to select the relevant predictors in the model according to the magnitude of their values (Chong andJun 2005, Palermo, Piraino, andZucht 2009). Figure 3 shows the variable importance in the projection (VIP) for the TE and LMASS.…”

Section: Integrating Tree Equivalents and Leaf Mass With Graze And Brmentioning

confidence: 99%

Modelling Carrying Capacity for the Thanda Private Game Reserve, South Africa Using Landsat 8 Multispectral Data

Oumar

Botha

Adam

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Rangelands which consist of grasslands, shrublands and savannahs are used by wildlife for habitat and are the main source of forage for livestock. The assessment and monitoring of rangeland condition is one of the most important factors for rangeland scientists in order to calculate the carrying capacity of livestock with consideration for coexisting wildlife. This study assessed the potential of Landsat 8 multispectral bands and broadband vegetation indices to model woody vegetation parameters such as tree equivalents (TE) and total leaf mass (LMASS) for the Thanda Private Game Reserve using partial least squares regression (PLSR). The PLSR model predicted TE with an R 2 value of 0.76 and a root mean square error (RMSE) of 1411 TE/ha using an independent test dataset. LMASS was predicted with an R 2 value of 0.67 and a RMSE of 853 kg/ha on an independent test dataset. The predictive models were then inverted to map TE and LMASS over the study area. The modelled TE and LMASS layers were integrated with conventional grazing and browse capacity models to map carrying capacity for the Game Reserve. The study indicates the potential of Landsat 8 multispectral data in carrying capacity modelling. The result is significant for rangeland monitoring in Southern Africa using remote sensing technologies.

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Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data

Cited by 88 publications

References 13 publications

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

Metabolomic Markers of Altered Nucleotide Metabolism in Early Stage Adenocarcinoma

Modelling Carrying Capacity for the Thanda Private Game Reserve, South Africa Using Landsat 8 Multispectral Data

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