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36 results for “Multivariate data analysis”
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.
<p><strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. </p><ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnosis of Depression: Data and code
<p><strong>In the archive are the datasets and code used to create the results in article "Application of Mass Multivariate Analysis on Neuroimaging Data Sets fo Precision Diagnosis of Depression". The methods is based on the Multivariate Linear toolbox mad F. Kherif’s lab and available on GitHub. <a href="https://github.com/LREN-CHUV/MLM">https://github.com/LREN-CHUV/MLM</a>. </strong></p> <p><strong>A total of 44 patients with a current psychotic (n=19) or depressive (n=25) episode were analyzed by MLM (details can be found in the article itself) utilizing resting sate fMRI, tasks fMRI, and anatomical T1w images. </strong></p> <p><strong>The data results are provided in MATLAB format, in a matrix called MLM.mat. The matrix includes the canonical variables for each of the three imaging modalities, chi-square statistics, and p-values for each brain region. Additionally, we provided a function MLM_plot_fun that allows mapping the statistics results to a canonical three-dimensional brain as per the article. </strong></p>
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Data from: Fossil abalone of Europe and their relationships with modern Haliotis (Haliotidae, Gastropoda): A multivariate analysis
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Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
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Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
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Data from: Comparative analysis of the multivariate genetic architecture of morphological traits in three species of Gomphocerine grasshoppers
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Data from: Multivariate ratio analysis reveals Trigonoderus pedicellaris Thomson (Hymenoptera, Chalcidoidea, Pteromalidae) as a valid species
We demonstrate by multivariate ratio analysis (MRA) the validity of two female colour morphs as separate species in what was previously regarded as a single species, Trigonoderus cyanescens (Förster, 1841) (Hymenoptera: Pteromalidae). As a result, T. pedicellaris Thomson, 1878 stat.r., is resurrected from synonymy under T. cyanescens and T. filatus binubilatus Erdős, 1960 syn.n. is synonymized with T. cyanescens. More than 20 characters were measured as part of two MRA dataset analyses. The first analysis excluded all measurements related to the gaster, whereas the second included gaster length, gaster breadth and seventh gaster tergite breadth. The first analysis revealed that the best separating morphometric ratios for the two species are head breadth:metatibia length and OOL:parastigma length, whereas the second analysis revealed OOL:gaster length as the second best separating ratio. The measurement error of all characters was below the admissible level of 30%. Gaster length proved to be a good character for separating the two groups, showed the lowest measurement error, and its percentage coefficient of variation was not greater than for other characters. This indicates that gaster length should not be discarded out-of-hand as a morphometric character in Pteromalidae. The variables that gave the best separating ratios included different body parts; therefore we suggest that the body of a specimen should be taken as a whole for use in MRA analyses, where each distance measurement can interact freely with any other. A key, figures and re-descriptions of T. cyanescens and T. pedicellaris are provided.
Data from: Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama
There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga, and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga; Cluster 2, including two individuals of M. gouazoubira; and Cluster 3, including a single individual of M. gouazoubira. The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama. Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups.
Data from: Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence
BACKGROUND: Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. OBJECTIVE: To study the potential association between allelic variants of dopamine D2 receptor (DRD2), ANKK1 (ankyrin repeat and kinase domain containing 1), dopamine D4 receptor (DRD4), Catechol-O-methyl transferase (COMT) and dopamine transporter (SLC6A3) genes and heroin dependence in Hungarian patients. METHODS: 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs): rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs) were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also provide a multivariate model for the associations among them implying Bayesian networks in Bayesian multilevel analysis. FINDINGS AND CONCLUSIONS: In single marker analysis the TaqIA (rs1800497) and TaqIB (rs1079597) variants were associated with heroin dependence. Moreover, -521 C/T SNP (rs1800955) of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A) allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462) of the DRD4 gene and this effect is mediated through the -521 C/T (rs1800955) polymorphism in the promoter.
Raw data and R code for the stone artifact analysis in "Lithic miniaturization in South China since the terminal Pleistocene: a multivariate analysis of lithic reduction from Fodongdi, Fulin and Xiqiaoshan"
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Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
<p>Parallel evolution of phenotypic traits is regarded as strong evidence for natural selection and has been studied extensively in a variety of taxa. However, we have limited knowledge of whether parallel evolution of host organisms is accompanied by parallel changes of their associated microbial communities (i.e., microbiotas), which are crucial for their hosts' ecology and evolution. Determining the extent of microbiota parallelism in nature can improve our ability to identify the factors that are associated with (putatively adaptive) shifts in microbial communities. While it has been emphasized that (non)parallel evolution is better considered as a quantitative continuum rather than a binary phenomenon, quantitative approaches have rarely been used to study microbiota parallelism. We advocate using multivariate vector analysis (i.e., phenotypic change vector analysis) to quantify direction and magnitude of microbiota changes and discuss the applicability of this approach for studying parallelism. We exemplify its use by reanalyzing gut microbiota data from multiple fish species that exhibit parallel shifts in trophic ecology. This approach provides an analytical framework for quantitative comparisons across host lineages, thereby providing the potential to advance our capacity to predict microbiota changes. Hence, we encourage the development and application of quantitative measures, such as multivariate vector analysis, to better understand the role of microbiota dynamics during their hosts' adaptive evolution, particularly in settings of parallel evolution.</p>
Estimating Modal Mineralogy using Raman Spectroscopy: Multivariate Analysis Models and Raman Cross-Section Proxies - Chapter 4 Data
<p>I provide supplementary data for Chapter 4 of my dissertation including electron microprobe analysis data as well as the Raman spectra and associated metadata of the mineral end-members, the mineral-mineral mixtures, and the diamond-mineral mixtures.</p>
Fig. 7 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 7. Multivariate data analysis (MVDA) of fresh and dried Peruvian ginger samples. A. Principal component analysis (PCA) showed clear separation of fresh and dried ginger samples. B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed clear separation of fresh and dried ginger samples. C. Splot of OPLS-DA. D. VIP scores based on the metabolite data from fresh and dry ginger extracts.
Fig. 9 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 9. [6]-Gingerol (A) and [6]-shogaol (B) contents in μg/g fresh weight (μg/ g FW) among the tested fresh ginger samples using different extraction solvents. Error bars indicate mean ± SE for five replicates. Black asterisks indicate significance difference from the Peruvian samples (*P <0.05, Student's t-test).
Fig. 5 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 5. The influence of geographical distribution of ginger on the gingerols and gingerol-related metabolites. The figure also describes the biosynthetic pathway of [6]-gingerol and hexahydrocurcumin and subsequent transformations. The opposed abundances of the precursors (i.e., phenylalanine and cinnamic acid) and end products (i.e., [6]-gingerol, hexahydrocurcumin, and gingerenone A and B) hypothesize that they have the same biosynthetic pathway.
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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.