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36 results for “Multivariate data analysis”
Fig. 3 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 3. The use of retention time, accurate mass, and co-elution pattern for compound validation. A. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) of [6]-gingerol measured by UPLC/MS in positive and negative ionization modes. B. Different gingerols within the same class show a retention time pattern according to their chain length. Intra-class variability is shown by XIC in positive ionization mode for the 6, 8 and 10-gingerol. C. Scatter plot representation of different gingerols annotated from the tested samples. The m/z of the loss of water from the protonated adducts is given on the x-axis and the observed RT (min) is given in the y-axis. The plot illustrates how the correlation between elution (RT) and chain length in the annotated gingerols can be used for the prediction of other compounds within the same class.
Fig. 4 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 4. Multivariate data analysis (MVDA) of fresh Ginger samples collected from different localities. A. Principal component analysis (PCA) showed clear separation of geographically different fresh ginger samples. B. VIP scores showing the top 25 metabolites discriminating ginger samples C. Dendrogram of the investigated fresh ginger samples based on the metabolites obtained after MS data analysis.
Fig. 2 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 2. Schematic diagram showing the steps for confident compound identification. The feature with the retention time of 12.94 min and 295.191 m/z, representing 6-gingerol was selected. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) were measured by UPLC/MS in the positive ionization mode.
Fig. 1 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 1. Experimental design for metabolic profiling of fresh ginger rhizomes collected from different geographical sources and the effect of drying.
Data from: A multivariate analysis of genetic variation in the advertisement call of the gray treefrog, Hyla versicolor
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Data from: Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama
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Data from: Discrimination of volatiles in herbal formula Baizhu Shaoyao San before and after processing using needle trap device with multivariate data analysis
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Data from: Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence
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Data from: Multivariate ratio analysis reveals Trigonoderus pedicellaris Thomson (Hymenoptera, Chalcidoidea, Pteromalidae) as a valid species
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Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
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Data from: Comparing G: multivariate analysis of genetic variation in multiple populations
The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.
Fig. 8 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 8. The influence of drying of ginger on the gingerols and gingerol-related metabolites.
Fig. 6 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 6. The influence of geographical distribution of ginger on metabolites content.
Data from: Multivariate analysis of electrophysiological diversity of Xenopus visual neurons during development and plasticity
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Data from: Multivariate analysis of genotype-phenotype association
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Data from: Comparing G: multivariate analysis of genetic variation in multiple populations
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.