So you think you can PLS-DA?

Ruiz-Perez, Daniel; Guan, Haibin; Madhivanan, Purnima; Mathee, Kalai; Narasimhan, Giri

doi:10.1109/iccabs.2018.8542038

Cited by 16 publications

(12 citation statements)

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“…sPLS-DA assumes that only a subset of original predictors is driving the outcome variable. This assumption stands when the number of predictors is far more than the number of samples: a common attribute in microbiome data ( Chung and Keles, 2010 ; Ruiz-Perez et al., 2020 ). sPLS-DA is a multivariate non-parametric method that does not require any distributional assumption about the data.…”

Section: Methodsmentioning

confidence: 99%

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome

et al. 2022

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Summary Gut dysbiosis in Huntington's disease (HD) has recently been reported using microbiome profiling in R6/1 HD mice and replicated in clinical HD. In HD mice, environmental enrichment (EE) and exercise (EX) were shown to have therapeutic impacts on the brain and associated symptoms. We hypothesize that these housing interventions modulate the gut microbiome, configuring one of the mechanisms that mediate their therapeutic effects observed in HD. We exposed R6/1 mice to a protocol of either EE or EX, relative to standard-housed control conditions, before the onset of gut dysbiosis and motor deficits. We characterized gut structure and function, as well as gut microbiome profiling using 16S rRNA sequencing. Multivariate analysis identified specific orders, namely Bacteroidales, Lachnospirales and Oscillospirales, as the main bacterial signatures that discriminate between housing conditions. Our findings suggest a promising role for the gut microbiome in mediating the effects of EE and EX exposures, and possibly other environmental interventions, in HD mice.

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Section: Methodsmentioning

confidence: 99%

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome

et al. 2022

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“…2(a)-(b) (ellipse confidence level=95%) shows even a supervised method cannot differentiate the groups, in general or by subscale. This is significant, as supervised approaches like sPLS-DA have a priori sample category knowledge and can sometimes differentiate completely random data (82). sPLS-DA did differentiate the two sets with scarce taxa present, showing some separation between Control, ADHD samples high on one subscale, and ADHD samples high on both (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Our study uses Sparse Partial Least Squares Discriminant Analysis (sPLS-DA, (68)), a sparse version of the Partial Least Squares (PLS, (81)) method, as a supervised method for determining differentiation degree with respect to taxa relative abundance (82).…”

Section: Methodsmentioning

confidence: 99%

Attention Deficit Hyperactivity Disorder and the gut microbiome: An ecological perspective

Cickovski

Mathee

Aguirre

et al. 2022

Preprint

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Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is a complex ecosystem of interdependent taxa with an exponentially complex web of interactions involving these taxa, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and composition, and have proposed several biomarkers. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset from 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We conduct our study in two parts. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential, biomarker, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p < 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We conclude by summarizing our results, and provide conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

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“…The scripts showing the metabolites considered for each PCA analysis can be found in the online repository. The rationale behind using PCA after PLS-DA was to avoid the increased group separation due to artifacts inherent to PLS-DA (found especially in datasets with low sample-to-feature ratios [ 27 ]), and instead visually reflect the variability that distinguishes the groups [ 28 ]. PCA analysis, although unsupervised, is improved by removing the variables that contribute the less to the separation between groups, which are found by PLS-DA.…”

Section: Methodsmentioning

confidence: 99%

Mass recovery following caloric restriction reverses lipolysis and proteolysis, but not gluconeogenesis, in insulin resistant OLETF rats

et al. 2021

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Caloric restriction (CR) is one of the most important behavioral interventions to reduce excessive abdominal adiposity, which is a risk factor for the development of insulin resistance. Previous metabolomics studies have characterized substrate metabolism during healthy conditions; however, the effects of CR and subsequent mass recovery on shifts in substrate metabolism during insulin resistance (IR) have not been widely investigated. To assess the effects of acute CR and the subsequent mass recovery on shifts in substrate metabolism, a cohort of 15-week old Long Evans Tokushima Otsuka (LETO) and Otsuka Long Evans Tokushima Fatty (OLETF) rats were calorie restricted (CR: 50% × 10 days) with or without partial body mass recovery (PR; 73% x 7 days), along with their respective ad libitum controls. End-of-study plasma samples were analyzed for primary carbon metabolites by gas chromatography (GC) time-of-flight (TOF) mass spectrometry (MS) data acquisition. Data analysis included PCA, Pearson correlation vs previously reported variables (adipose and body masses, and insulin resistance index, IRI), and metabolomics maps (MetaMapp) generated for the most significant group comparisons. All treatments elicited a significant group differentiation in at least one principal component. CR improved TCA cycle in OLETF, and increased lipolysis and proteolysis. These changes were reversed after PR except for gluconeogenesis. Plasma lipid concentrations were inversely correlated to IRI in LETO, but not OLETF. These shifts in substrate metabolism suggest that the CR-induced decreases in adipose may not be sufficient to more permanently alter substrate metabolism to improve IR status during metabolic syndrome.

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So you think you can PLS-DA?

Cited by 16 publications

References 0 publications

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome

Gene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome

Attention Deficit Hyperactivity Disorder and the gut microbiome: An ecological perspective

Mass recovery following caloric restriction reverses lipolysis and proteolysis, but not gluconeogenesis, in insulin resistant OLETF rats

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