Simultaneous Component Analysis Research Articles

Variable-selection ANOVA Simultaneous Component Analysis (VASCA)

ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the 'holistic' testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart. The code for VASCA is available in the MEDA Toolbox at https://github.com/josecamachop/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https://github.com/josecamachop/VASCA/tree/v1.0.0 (DOI 10.5281/zenodo.7410623). Supplementary data are available at Bioinformatics online.

Simultaneous component analysis as an interesting preliminary data analysis method in GC-MS – An example of headspace screening of Polish grasses

Abstract70 species of grasses family (Poaceae), coming from genera: Agrostis, Alopecurus, Anthoxanthum, Apera, Arrhenatherum, Avena, Brachypodium, Briza, Bromus, Calamagrostis, Corynephorus, Cynosurus, Dactylis, Danthonia, Deschampsia, Digitaria, Echinochloa, Elymus, Eragrostis, Festuca, Glyceria, Helictotrichon, Hierochloe, Holcus, Hordeum, Koeleria, Leymus, Lolium, Milium, Molinia, Nardus, Panicum, Phalaris, Phleum, Phragmites, Poa, Saccharum and Setaria, collected mostly from natural stands in Poland during 2020 season, were subjected to GC-MS fingerprinting of headspace volatile fraction above dried material. Obtained mass spectrometry data were analyzed by means of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Five species: Glyceria maxima (Hartm.) Holmb., Lolium multiflorum Lam., Hordeum jubatum L., Bromus tectorum L. and Bromus secalinus L. were identified as outliers, which is consistent with our earlier analysis by thin layer chromatography. These species deserve further look and their outliance is orthogonal to coumarin content, which was independently observed for odorant species of grasses.

ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods.

The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA+) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA+). The ALASCA package for R makes the ASCA+ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

Effect of high-fat diet and exercise in the morning or evening on lipid subfractions among men with overweight/obesity: a randomised trial

Abstract Background Although main lipid fractions in the blood, such as HDL and LDL, are associated with risk of cardiovascular disease (CVD), the risk associated with different subfractions of these particles varies. For example, small dense LDL particles are associated with significantly increased CVD risk while larger LDL particles are not. Purpose To investigate the effect of a high-fat diet on serum lipid subfractions in men with overweight/obesity and to determine whether morning or evening exercise affected the lipid subfraction profile. Methods In a three-armed randomised trial, 24 men (aged 36±4 years, body mass index 31.2±2.3 kg/m2) consumed a high-fat diet (HFD; 65% energy from fat) for 11 days. One group of participants (n=8, control) did not exercise, while one group (n=8) exercised in the morning (06:30 hours) and one group (n=8) in the evening (18:30 hours) on days 6–10. We measured the effect of HFD, and that of morning or evening exercise, on fasting circulating lipoprotein subclass profiles determined using nuclear magnetic resonance spectroscopy. We performed a principal component analysis and partial least squares discriminant analyses to investigate patterns of changes in lipid subfractions after 5 days of HFD and a repeated measures ANOVA simultaneous component analysis (RM-ASCA) to determine multivariate changes in lipoprotein profiles between the three groups. To determine univariate effects of HFD on lipoprotein subclasses, we used Benjamini-Hochberg-adjusted paired samples t-tests. We used linear mixed models to determine between-group differences in lipoprotein subfractions after exercise in the morning or evening, compared to no exercise. Results Lipid subfraction profiles changed substantially after 5 days of HFD, with significant changes in 31 of 100 subfraction variables (adjusted p-values <0.05). Total serum cholesterol in LDL increased from 113±26 mg/dL to 122±26 mg/dL after 5 days of HFD (adjusted p=0.039) due to increased cholesterol in larger LDL particles (LDL-2 and LDL-3, Figure 1). Exercise induced a systematic change in lipid subfraction profiles, with small overall differences between morning versus evening exercise. Morning and evening exercise reduced serum concentrations of 20% and 24% of fasting lipid subfraction variables, respectively, compared to the control group (Figure 2). Evening exercise reduced fasting cholesterol concentrations in three LDL subfractions by 20%, 26% and 86%, respectively, while morning exercise only reduced concentration in the largest LDL particles by 12% (adjusted p-values <0.05 compared to the control group for all). Discussion and conclusion We found a marked change in lipid subfraction profiles after only five days of HFD among men with overweight/obesity. Exercise, either in the morning or in the evening, induced a substantial change in lipid subfraction profile compared with no exercise during an HFD. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Novo Nordisk Foundation

Influence of measurement procedure on the use of a handheld NIR spectrophotometer

The development of portable NIR instruments facilitates widespread use among non-specialists. However, untrained operators may follow non-optimal measurement procedures. This work investigates how different factors in the measurement procedure influence the spectra of pig feed samples produced by SCiO, a handheld NIR. Measurement conditions were studied by means of Design of Experiments and evaluated with analysis of variance – simultaneous component analysis (ANOVA–SCA or ASCA). We quantified and visualized how measurement distance, angle, background lighting, the use of plastic lids and different devices interactively affect the resulting spectra. The samples could be distinguished with 100% accuracy with Partial Least Squares-Discriminant Analysis (PLS-DA) a scanning distance of 0.5 cm. Replication of the experiment with special attention to reproducing the conditions still lead to some differences, which highlights both the challenges in controlling conditions and the importance of considering them. Based on the results, generalizable guidelines for acceptance of spectra were proposed for this case study. Of main importance are performing measurements at distances of 0.5 cm or at least in an environment without background lighting. Overall, the provided guidelines for measurement conditions and a methodology to investigate this for other devices are a key enabler to spreading handheld spectrometry to a non-expert audience.

Breath response following a nutritional challenge monitored by secondary electrospray ionization high-resolution mass spectrometry

On-line breath analysis using secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a sensitive method for biomarker discovery. The strengths of this technology have already been demonstrated in the clinical environment. For the first time, this study demonstrates the application of SESI-HRMS in the field of nutritional science using a standardized nutritional intervention, consisting of a high-energy shake (950 kcal, 8% protein, 35% sugar and 57% fat). Eleven subjects underwent the intervention on three separate days and their exhaled breath was monitored up to six hours postprandially. In addition, sampling was performed during equivalent fasting conditions for selected subjects. To estimate the impact of inter- and intra-individual variability, analysis of variance simultaneous component analysis was conducted, revealing that the inter-individual variability accounted for 30% of the data variation. To distinguish the effect of the intervention from fasting conditions, partial least squares discriminant analysis was performed. Candidate compound annotation was performed with pathway analysis and collision-induced dissociation (CID) experiments. Pathway analysis highlighted, among others, features associated with the metabolism of linoleate, butanoate and amino sugars. Tentative compounds annotated through CID measurements include fatty acids, amino acids, and amino acid derivatives, some of them likely derived from nutrients by the gut microbiome (e.g. propanoate, indoles), as well as organic acids from the Krebs cycle. Time-series clustering showed an overlap of observed kinetic trends with those reported previously in blood plasma.

Effects of sulfamethoxazole exposure on mussels (Mytilus galloprovincialis) metabolome using retrospective non-target high-resolution mass spectrometry and chemometric tools

In this work, the Regions of Interest-Multivariate Curve Resolution (ROIMCR) method is proposed for the analysis of non-target metabolomics data. Samples from marine mussels (Mytilus galloprovincialis) exposed to a sublethal concentration (10 μg/L) of sulfamethoxazole (SMX) during 4 days in different seasonal conditions (summer and winter) were analyzed by High-Performance Liquid Chromatography – High-Resolution Mass Spectrometry (HPLC-HRMS) to study the effect of their exposure to SMX and the different seasonal conditions. The Regions of Interest (ROI) procedure has been applied for data filtering, compression, preprocessing and storage steps. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is then applied to the previously MS ROI preprocessed data sets to resolve the elution profiles and spectral fingerprints of the chemical constituents of the analyzed samples. The peak areas of the elution profiles of the chemical constituents resolved by the combined ROIMCR procedure were analyzed by Principal Component Analysis (PCA) and samples were clustered according to their experimental seasonal and SMX exposure. The effects of the two investigated factors and of their interaction on the concentrations of the metabolites were statistically assessed by ANOVA simultaneous component analysis (ASCA). Both types of analyses, PCA clustering and ASCA, confirmed that the seasonal conditions (summer versus winter) produced larger effects than those produced by the exposure to SMX and by the interaction of these two factors. The concentration changes of 16 identified endogenous metabolites were validated individually using a Wilcoxon statistical test, which confirmed the presence of significant disturbances in the levels of some of these metabolites (free fatty acids, amino acids and nucleic acids), and indicated the possible alteration of six different biological pathways, affected by the investigated seasonal and SMX exposure factors.

Effects of Water Stress, Defoliation and Crop Thinning on Vitis vinifera L. cv. Solaris Must and Wine Part II: 1H NMR Metabolomics.

Proton nuclear magnetic resonance (1H NMR) metabolomics was employed to investigate the impact of water deficit, defoliation, and crop thinning on the chemical composition of must and wines from the cool-climate white grape variety Solaris. The obtained results show that viticultural practices (defoliation and crop thinning) affected the amino acid and sugar content of Solaris must and thereby the quality of the final wine—mainly in terms of compounds normally related to fruity aroma (i.e., isopentanol), non-sugar sweetness (i.e., proline and glycerol), and alcohol content. The content of tyrosol, a natural phenolic antioxidant with a high bioavailability, was increased in the final wine by a combination of defoliation and crop thinning. The results of the metabolomics analysis performed on the must and wine samples from the water stress experiment showed that short-term water deficit significantly affected the concentration of several flavor-related compounds, including glutamate, butyrate and propanol, of the organic acids lactate and fumarate, and of the phenolic compounds caffeic acid and p-coumaric acid. ANOVA simultaneous component analysis showed that the effect of water deficit accounted for 11% (p < 0.001) and 8% (p < 0.001) of the variability in the metabolite concentrations in must and wines, respectively, while viticultural practices accounted for 38% (p < 0.001) and 30% (p < 0.001) of the metabolite variability in must and wines, respectively.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

Comprehensive multivariate evaluation of the effects on cell phenotypes in multicolor flow cytometry data using ANOVA simultaneous component analysis

AbstractThis work proposes an approach to assess the effects observed in multicolor flow cytometry (MFC) experiments, for all markers and experimental factors simultaneously. It achieves this end by extending ANOVA simultaneous component analysis (ASCA), a multivariate version of ANOVA, to flow cytometry data. It is based on an initial multiset PCA model to describe the main variation patterns of cell marker expression, followed by an ASCA model on the histograms built from these PCA scores. This approach allows for determining the variations in cell phenotype distribution that are related to the experimental design. On a data set from a study of the immune response to prolonged physical exercise, the proposed method computed the effect size and statistical significance of all the experimental factors and their interactions. Most notably, it provided easily interpretable submodels for the overall effect of the walking exercise and for the interaction between exercise and the responsiveness to a bacterial stimulus. The application of a time‐guided sequential clustering algorithm to the ASCA scores revealed a stratification of the studied individuals based on their neutrophil activation dynamics. These effects were not clearly detectable using PCA alone. In comparison with pairwise classification models by DAMACY (a discriminant analysis method for MFC data), ASCA results were less detailed in describing differences between specific samples, but had the advantage of modeling several factors and levels simultaneously. Such characteristics make the proposed implementation of ASCA an effective and complementary addition to the chemometric methodologies for the analysis of MFC data.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach.

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Permutation tests for ASCA in multivariate longitudinal intervention studies

AbstractPermutation tests are the standard technique for significance testing in Analysis of Variance Simultaneous Component Analysis. However, there is a vast number of alternative approaches for permutation testing, and the number of choices grows in relation to the complexity of the study design. In this paper, we focus on longitudinal intervention studies with multivariate outcomes, a relevant experimental design in clinical studies where the outcome is an omics profile (such as in genomics, metabolomics, and the like). We propose a new technique to derive power curves tailored to the size and (un)balanced nature of the data set in the study. This technique is useful to identify misleading permutation tests, with lack of power or overly optimistic outcomes. We found that choosing the best permutation approach is far from intuitive and that there is a significant risk of deriving incorrect conclusions in real‐life analyses. Our approach avoids this risk and can be extended to other complex designs of interest. The code is available for free use.

Fast GC E-Nose and Chemometrics for the Rapid Assessment of Basil Aroma

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations.

Effect of Dairy, Season, and Sampling Position on Physical Properties of Trentingrana Cheese: Application of an LMM-ASCA Model.

Trentingrana hard cheese is a geographic specification of the PDO Grana Padano. It is produced according to an internal regulation by many cooperative dairy factories in the Trentino region (northern Italy), using a semi-artisanal process (the only allowed ingredients are milk, salt, and rennet). Within the PSR project TRENTINGRANA, colorimetric and textural measurements have been collected from 317 cheese wheels, which were sampled bi-monthly from all the consortium dairies (n = 15) within the timeframe of two years, to estimate the effect on physical properties related to the season of the year and the dairy factory implant. To estimate the effect of the dairy and the time of the year, considering the internal variability of each cheese wheel, a linear mixed-effect model combined with a simultaneous component analysis (LMM-ASCA) is proposed. Results show that all the factors have a significant effect on the colorimetric and textural properties of the cheese. There are five clusters of dairies producing cheese with similar properties, three different couples of months of the year when the cheese produced is significantly different from all the others, and the effect of the geometry of the cheese wheel is reported as well.

In-depth understanding of the photoreduction of graphene oxide to reduced-graphene oxide on TiO2 surface: Statistical analysis of X-ray photoelectron and Raman spectroscopy data

In UV-assisted photoreduction of graphene oxide (GO) using TiO2, TiO2 supplies photogenerated electrons to reduce GO, which exhibits enhanced electrical conductivity, and induce a chemical interaction between TiO2 and reduced-GO (rGO) for improved charge separation. Here, we investigated the photosynthesis of the TiO2–rGO composite with varying the UV irradiation time, TiO2 source (TiO2-D or –HT, commonly employed in dye-sensitized solar cells), and light power. The final products were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, and field-emission scanning electron microscopy. Most importantly, the chemical interaction between the rGO matrix and TiO2 was confirmed by X-ray photoelectron spectroscopy (XPS), whereas the carbon defects were studied by Raman spectroscopy. As expected, the chemical interaction between the rGO matrix and TiO2 was confirmed by the formation of Ti–O–C and Ti–C bonds. Moreover, during UV irradiation, the concentration of carbon defects increased evenly, implying that the photodegradation occurred simultaneously with the photoreduction of GO. The XPS and Raman data were further subjected to statistical analyses, including principal component analysis, multivariate linear regression and analysis of variance (ANOVA), and ANOVA–simultaneous component analysis. The results indicated that the irradiation time and TiO2 source had the highest impacts on the final products.

Mapping associations across multiple aspects of Alzheimer disease and the role of CSF biomarkers in individuals without dementia

AbstractBackgroundAlzheimer disease (AD) is a complex disorder involving biological, clinical, and lifestyle aspects. Assessing all these aspects simultaneously allows to better disentangle the position of CSF marker abnormalities across AD stages. Here, we employ a novel statistical method specifically developed to reveal meaningful associations across multiple aspects that can occur simultaneously within individuals.MethodWe selected the baseline data from the European Prevention of Alzheimer Disease cohort of individuals without dementia (www.ep‐ad.org; n=1243, mean±SD age = 65±7 years old, 57% female, 18% CDR ≥0.5) covering ten aspects: demographics including APOE‐genotype, cerebrospinal fluid (CSF) biomarkers, MRI visual rating scales, MRI regional volumes, MRI white matter lesions, cognition, function, psychological factors, general physical health, and comorbidities. We used regularized simultaneous component analysis (regSCA), a statistical technique that extracts relevant components which explain variation within and across these aspects, and robustly selects the appropriate variables [1, 2].ResultThe optimal model revealed eight components explaining 51% of the total variance. Figure 1 presents an overview of all components, showing the strengths and directions of the associations between variables. Five components included CSF biomarkers, which were (1) Abeta42‐dominant, (2) Tau‐dominant, (3) function‐dominant, (4) age‐dominant and, (5) white matter lesion‐dominant. In (3) and (4), Abeta42 and Tau related to cognition and function. Only (3) was independent of age, and may represent purely AD. In (5), Abeta42 related to white matter lesions, and may represent the degree cerebral amyloid angiopathy (CAA). Abeta42 in (1) and Tau in (2) related to APOE‐genotype, suggesting these may be independent effects. The three components unrelated to CSF biomarkers were: (6) sex‐dominant, (7) psychological factors‐dominant, and (8) life‐style‐only.ConclusionAnalysis of robust associations across multiple aspects of AD simultaneously implicated CSF biomarkers in five components. These components may represent risk‐profiles and AD disease processes in cognitively normal and mildly impaired individuals. Our next step is to investigate the stability of the components and their co‐expression within individuals. This overview of complex relationships between AD‐related aspects, helps to understand the disease mechanisms in of AD. This novel approach can be a solution for analyses of large and complex datasets in AD.

Longitudinal liver proteome profiling in dairy cows during the transition from gestation to lactation: Investigating metabolic adaptations and their interactions with fatty acids supplementation via repeated measurements ANOVA-simultaneous component analysis

Repeated measurements analysis of variance – simultaneous component analysis (ASCA) has been developed to handle complex longitudinal omics datasets and combine novel information with existing data. Herein, we aimed at applying ASCA to 64 liver proteomes collected at 4-time points (day −21, +1, +28, and + 63 relative to parturition) from 16 Holstein cows treated from 9 wk. antepartum to 9 wk. postpartum (PP) with coconut oil (CTRL) or a mixture of essential fatty acids (EFA) and conjugated linoleic acid (CLA) (EFA + CLA). The ASCA modeled 116, 43, and 97 differentially abundant proteins (DAP) during the transition to lactation, between CTRL and EFA + CLA, and their interaction, respectively. Time-dependent DAP were annotated to pathways related to the metabolism of carbohydrates, FA, and amino acid in the PP period. The DAP between FA and the interaction effect were annotated to the metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, retinol metabolism, and steroid hormone biosynthesis. Collectively, ASCA provided novel information on molecular markers of metabolic adaptations and their interactions with EFA + CLA supplementation. Bioinformatics analysis suggested that supplemental EFA + CLA amplified hepatic FA oxidation; cytochrome P450 was enriched to maintain metabolic homeostasis by oxidation/detoxification of endogenous compounds and xenobiotics. SignificanceThis report is among the first ones applying repeated measurement analysis of variance–simultaneous component analysis (ASCA) to deal with longitudinal proteomics results. ASCA separately identified differentially abundant proteins (DAP) in ‘transition time’, ‘between fatty acid treatments’, and ‘their interaction’. We first identified the molecular signature of hepatic metabolic adaptations during postpartum negative energy balance; the enriched pathways were well-known pathways related to mobilizing fatty acids (FA) and amino acids to support continuous energy production through fatty acid oxidation, TCA cycle, and gluconeogenesis. Some of the DAP were not previously reported in transition dairy cows. Secondly, we provide novel information on the mechanisms by which supplemented essential FA and conjugated linoleic acids interact with hepatic metabolism. In this regard, FA amplified hepatic detoxifying and oxidation capacity through ligand activation of nuclear receptors. Finally, we briefly compared the strengths and weaknesses of the ASCA model with PLS-DA and outlined why these methods are complementary.

Routine Monitoring of Instrument Stability in a Milk Testing Laboratory With ASCA: A Pilot Study.

Mid-infrared spectroscopy has been developed as a reliable and rapid tool for routine analysis of fat, protein, lactose and other components in liquid milk. However, variations within and between FTIR instruments, even within the same milk testing laboratory, present a challenge to the accuracy of measurement of particularly minor components in the milk, such as individual fatty acids or proteins. In this study we have used Analysis of variance–Simultaneous Component Analysis (ASCA), to monitor the spectral variation between and within each of four different FOSS FTIR spectrometers over each week in an independent milk testing laboratory over 4 years, between August 2017 and March 2021 (223 weeks). On everyday of each week, spectra of the same pilot milk sample were recorded approximately every hour on each of the four instruments. Overall, variations between instruments had the largest effect on spectral variation over each week, making a significant contribution every week. Within each instrument, day-to-day variations over the week were also significant for all but two of the weeks measured, however it contributed less to the variance overall. At certain times other factors not explained by weekday variation or inter-instrument variation dominated the variance in the spectra. Examination of the scores and loadings of the weekly ASCA analysis allowed identification of changes in the spectral regions affected by drifts in each instrument over time. This was found to particularly affect some of the fatty acid predictions.

Multivariate Analysis in Microbiome Description: Correlation of Human Gut Protein Degraders, Metabolites, and Predicted Metabolic Functions.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging

As advances in technology allow the acquisition of complementary information, it is increasingly common for scientific studies to collect multiple datasets. Large-scale neuroimaging studies often include multiple modalities (e.g., task functional MRI, resting-state fMRI, diffusion MRI, and/or structural MRI) with the aim to understand the relationships between datasets. In this study, we seek to understand whether regions of the brain activated in a working memory task relate to resting-state correlations. In neuroimaging, a popular approach uses principal component analysis for dimension reduction prior to canonical correlation analysis with joint independent component analysis, but this may discard biological features with low variance and/or spuriously associate structure unique to a dataset with joint structure. We introduce SImultaneous Non-Gaussian component analysis (SING) in which dimension reduction and feature extraction are achieved simultaneously, and shared information is captured via subject scores. We apply our method to a working memory task and resting-state correlations from the Human Connectome Project. We find joint structure as evident from joint scores whose loadings highlight resting-state correlations involving regions associated with working memory. Moreover, some of the subject scores are related to fluid intelligence.