Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

BackgroundIntrinsic capacity is the combination of individual physical and mental abilities, reflecting the aging degree of the older adults. However, the mechanisms and metabolic characteristics of the decline in intrinsic capacity are still unclear.AimsTo identify metabolic signatures and associated pathways of decline in intrinsic capacity based on the metabolite features.MethodsWe recruited 70 participants aged 77.19 ± 8.31 years. The five domains of intrinsic capacity were assessed by Short Physical Performance Battery (for mobility), Montreal cognition assessment (for cognition), 30-Item Geriatric Depression Scale (for psychology), self-reported hearing/visual impairment (for sensory) and Nutritional risk screening (for vitality), respectively. The serum samples of participants were analyzed by liquid chromatography-mass spectrometry-based metabolomics, followed by metabolite set enrichment analysis and metabolic pathway analysis.ResultsThere were 50 participants with a decline in intrinsic capacity in at least one of the domains. A total of 349 metabolites were identified from their serum samples. Overall, 24 differential metabolites, 5 metabolite sets and 13 pathways were associated with the decline in intrinsic capacity.DiscussionOur results indicated that decline in intrinsic capacity had unique metabolomic profiles.ConclusionThe specific change of acyl carnitines was observed to be a feature of decline in intrinsic capacity. Dysregulation of the pentose phosphate pathway and of arginine and ornithine metabolism was strongly associated with the decline in intrinsic capacity.

An empirical comparison of the efficacy of covariance-based and variance-based SEM

Some recent developments in PLS modeling

Multiblock analysis of environmental measurements: A case study of using Proton Induced X-ray Emission and meteorology dataset obtained from Islamabad Pakistan

Background Tasty&Healthy (T&H) diet is an exclusion whole food diet shown to induce clinical and biological remission in Crohn’s disease (CD). We aimed to explore fecal and serum metabolomic response to T&H vs. EEN and habitual diet arms in the TASTI-MM (T&H vs. EEN) and TASTI-E (T&H vs. habitual) RCTs. Methods We analyzed 670 serum and fecal metabolites at baseline, week 4 (fecal only) and week 8. Remission was defined by MINI index< 8 in TASTI-MM and by 50% decrease in calprotectin in TASTI-E. Univariate testing with multiple comparisons correction (FDR<0.25) were applied to each week, measured both in absolute level and in relative to baseline level. Principal component analysis (PCA) was applied for each metabolomics dataset to identify total signature shift or trends in metabolic patterns. Finally, metabolite set enrichment analysis (MESA) was applied to identify biological pathways associated with the diets using MetaboAnalyst v6.0, reporting pathway-level enrichment strength (Q statistic > 10) and significance (FDR < 0.05). Results Overall 121 children were enrolled in both RCTs with a total of 227 serum samples and 312 fecal samples collected over the RCTs’ duration with no observed metabolic differences at baseline. 140 serum and 63 fecal metabolites (in both weeks 4 and 8) consistently differed between T&H and EEN in both absolute and relative comparisons. 17 serum metabolites consistently differed between T&H and habitual diet in both absolute and relative comparisons while none differed in stool. PCA analysis showed overall metabolic shift in serum (Figure A) and fecal (Figure B) metabolomics signature in TASTI-MM, the most noticable shift was observed in stool at week 4, while a trend of metabolic shift in serum metabolomics was observed in TASTI-E. Both shifts were in concordance with differential metabolites detected in univariate analysis. MESA was used to infer biological interpretation which revealed in fecal metabolomics consistent and robust significant enrichment (Q-Statistic > 10, FDR < 0.05 at both weeks 4 and 8) of butyrate, histidine and tryptophan metabolism pathways in the T&H group in comparison to EEN. Conclusion The T&H diet promoted a distinct systemic metabolic signature which differed from those treated with EEN or continuing their habitual diet. The consistent and robust enrichment of butyrate, histidine and tryptophan metabolism pathways points to restoration of host–microbial amino acid and short-chain fatty acid networks that are implicated in intestinal homeostasis and immune regulation. Conflict of interest: Mr. Azulay, Asaf: No conflict of interest Plotkin, Luba: No conflict of interest Aharoni-Frutkoff, Yonat: No conflict of interest Livovsky, Jessica: No conflict of interest Focht, Gili: Other: Received in the past 3 years consultation fee from Lilly Lev Zion, Raffi: No conflict of interest Ledder, Oren: No conflict of interest Assa, Amit: No conflict of interest Yogev, Dotan: No conflict of interest Orlansky Meyer, Esther: No conflict of interest Broide, Efrat: No conflict of interest Kierkuś, Jarosław: Grant: Nestle Other: Nutricia, Abbvie, Nestle Kang, Ben: Other: For the past 3 years, Ben Kang has served as a speaker or consultant for Celltrion, Janssen, Eisai, Abbvie, Takeda, Yuhan, Yungjin, JW Pharmaceutical, and Samsung Bioepis and has received research funding from Celltrion. Weiss, Batia: No conflict of interest Aloi, Marina: Personal Fees: AbbVie Pfizer Dicofarm Slae, Mordechai: No conflict of interest Yerushalmy-Feler, Anat: No conflict of interest Wine, Eytan: Personal Fees: Janssen, AbbVie, Nestle Health Sciences, Mead Johnson Nutrition, Pfizer, BioJamp Turner, Dan: Consultation fee: Janssen, Pfizer, Ferring, Abbvie, Takeda, Prometheus Biosciences, Lilly, SorrisoPharma, Boehringer Ingelheim, Galapagos, BMS, AlfaSigma, Merck, Gentech Research support: Janssen, Abbvie, Takeda, Pfizer Royalties: Shaare Zedek Medical Center, Hospital for Sick Children

BackgroundThe role of circulating metabolome in cognitive impairment is limited and inconclusive. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors.MethodIn the community‐based Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance (NMR) spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired group according to their performance in two objective cognitive tests. A two‐step strategy of cross‐sectional discovery followed by prospective validation was applied to identify cognitive impairment‐related metabolites. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately‐to‐severely impaired) during the 2‐year follow‐up to 204 cognitively normal controls by age and sex with a 1:3 ratio and conducted conditional logistic regression. Metabolite set enrichment analysis was performed to identify cognitive impairment‐related metabolic pathways. We constructed prediction models for incident cognitive impairment using Lasso regression.ResultWe identified 28 metabolites cross‐sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were each prospectively associated with 28%, 28%, 29%, and 33% lower risk of incident cognitive impairment. Based on incident cognitive impairment‐related metabolites, three enriched metabolic pathways (valine, leucine, and isoleucine biosynthesis; valine, leucine, and isoleucine degradation; and aminoacyl‐tRNA biosynthesis) were found. Incorporating 13 metabolite biomarkers selected by Lasso regression into the traditional risk factors‐based prediction model substantially improved the prediction performance on incident cognitive impairment (AUCs: 0.839 vs. 0.703, P <0.001).ConclusionThe study identified specific plasma metabolites and potential enriched metabolic pathways related to cognitive impairment. Utilizing the selected metabolites substantially improved the prediction performance for cognitive impairment.

Obstructive sleep apnea (OSA) is caused by repeated obstruction of the upper airway during sleep. The purpose of this study was to test the relative contributions of specific demographic and cephalometric measurements to OSA severity. Demographic, cephalometric, and overnight polysomnographic records of 291 male OSA patients and 49 male nonapneic snorers were evaluated. A partial least squares (PLS) analysis was used for statistical evaluation. The results revealed that the predictive powers of obesity and neck size variables for OSA severity were higher than the cephalometric variables used in this study. Compared with other cephalometric characteristics, an extended and forward natural head posture, lower hyoid bone position, increased soft palate and tongue dimensions, and decreased nasopharyngeal and velopharyngeal airway dimensions had relatively higher associations with OSA severity. The respiratory disturbance index (RDI) was the OSA outcome variable that was best explained by the demographic and cephalometric predictor variables. We conclude that the PLS analysis can successfully summarize the correlations between a large number of variables, and that obesity, neck size, and certain cephalometric measurements may be used together to evaluate OSA severity.

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports two optimal QSPR models for estimating water solubility ( log S W ) and n-octanol/water partition coefficient ( log K OW ) of chloric and alkyl benzene derivatives. Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log S W and log K OW of chloric and alkyl benzene derivatives. The correlation coefficients of the optimal models for log S W and log K OW were 0.973 and 0.990, respectively. The results of internal cross-validation test and external validation test showed that both of the optimal models had high fitting precision and good predicting ability. The log S W and log K OW values predicted by the optimal models are very close to those observed. The PLS analysis indicated that chloric and alkyl benzene derivatives with larger electronic spatial extent and lower molecular total energy tend to be more hydrophobic and lipophilic, and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals leads to larger dissolvability.

In principal component regression (PCR) and partial least‐squares regression (PLSR), the use of unlabeled data, in addition to labeled data, helps stabilize the latent subspaces in the calibration step, typically leading to a lower prediction error. For using unlabeled data in PLSR, a non‐sequential approach based on optimal filtering (OF) has been proposed in the literature. In this work, a sequential version of the OF‐based PLSR and a PCA‐based PLSR (PLSR applied to PCA‐preprocessed data) are proposed. It is shown analytically that the sequential version of the OF‐based PLSR is equivalent to that of PCA‐based PLSR, which leads to a new interpretation of OF. Simulated and experimental data sets are used to point out the usefulness and pitfalls of using unlabeled data. Unlabeled data can replace labeled data to some extent, thereby leading to an economic benefit. However, in the presence of drift, the use of unlabeled data can result in an increase in prediction error compared to that obtained with a model based on labeled data alone. Copyright © 2011 John Wiley & Sons, Ltd.

A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape

This study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (Kd) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch Kd values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of Kd from the MIR spectra and reference Kd data, were compared with predictions from Koc-based indirect estimation using SOC content. The reference Kd data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas Koc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R2 = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of Kd from Koc. This poor prediction suggested that total SOC content alone could explain only half of the variation in Kd. In contrast, the regression plot of PLS predicted versus measured Kd resulted in an improved correlation, with R2 = 0.72 ( P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of Kd, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils.

The purpose of this study is to determine and analyze the capacity of financial aspect and human capital of financial performance of SMEs in Pekanbaru City. This research use descriptive analysis method and verification with technical analysis of Partial Least Square (PLS) with primary data from questionnaire to 20 UMKM as respondent. Data are classified into three characteristics, ie micro, small and medium enterprises grouped based on business turnover per year. Then measured factor loading (convergent validity), discriminant validity, reliability, coefficient of determination, and path coefficient by using SmartPLS. The results showed that the financial and human capital have a significant influence on financial performance, and human capital has a significant influence on the financial aspects

Partial least-squares (PLS) analysis was applied for the first time to ternary and binary mixtures of pesticides producing overlapping peaks in gas chromatograms. Several preprocessing algorithms for pretreatment of data were tested for optimization of the PLS models, and the most advantageous were used in each case. Three different mixtures of pesticides were resolved with satisfactory results: parathion-methyl, chlorpyrifos-methyl, and vinclozolin; parathion-ethyl, chlorpyrifos, and triadimefon; and endosulfan sulfate and carbophenothion. The proposed models were applied to the determination of 3 mixtures of pesticides at levels of 0.02-1.00 ng/mL in wetland water samples after a preconcentration step with C18 cartridges, with recoveries ranging from 82.7 to 117.4%. The reported method is sufficiently sensitive to measure pesticide residues at the maximum allowable concentrations given in the European Union Drinking Water Directive.

Analysis of lard extracted from lipstick formulation containing castor oil has been performed using FTIR spectroscopic method combined with multivariate calibration. Three different extraction methods were compared, namely saponification method followed by liquid/liquid extraction with hexane/dichlorometane/ethanol/water, saponification method followed by liquid/liquid extraction with dichloromethane/ethanol/water, and Bligh & Dyer method using chloroform/methanol/water as extracting solvent. Qualitative and quantitative analysis of lard were performed using principle component (PCA) and partial least square (PLS) analysis, respectively. The results showed that, in all samples prepared by the three extraction methods, PCA was capable of identifying lard at wavelength region of 1200-800 cm-1 with the best result was obtained by Bligh & Dyer method. Furthermore, PLS analysis at the same wavelength region used for qualification showed that Bligh and Dyer was the most suitable extraction method with the highest determination coefficient (R2) and the lowest root mean square error of calibration (RMSEC) as well as root mean square error of prediction (RMSEP) values.