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FIGURE 4 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 4. Canonical Discriminant Analysis (CDA) plots for females. Specimens, sample centroids and group perimeters are represented. For details, percentage of variance explained, and other data, see text.
FIGURE 9 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 9. Plot showing the inverse relationship between altitude and dorsalia mean values in the studied D. clarkorum/D. dryada populations (Spearman Rank Correlation coefficient: ρ = -0.72; P=0.01).
FIGURE 7 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 7. UPGMA trees derived from Mahalanobis' distances among sample centroids. See text for interpretation (males and females together, Turkey and Georgia).
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.
Figure 5 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 5. Box plots with whiskers showing the variation among locomotion modes of percentage prediction errors (%PE) for the multiple regression analysis (MR; grey) and the single regression analysis (SR; white) equations adjusted in modern birds. Box length shows the interquartile range (25th and 75th percentiles). The horizontal line within boxes indicates the median. Vertical lines show the 5–95% confidence limits. Points indicate values outside these limits (i.e. outliers). Black horizontal lines between the grey and white boxes show the %PE estimates obtained with the equation based on humeral length (HL), the osteological variable less affected by ecological groupings (as shown by its lower F-statistic values; see Tables 6 and 7). %PE values greater than zero indicate an underestimation and %PE values lower than zero indicate an overestimation. A, %PE variation with respect to groups of aerial locomotion. Abbreviations: CF, continuous flapping; FG, flapping and gliding; FB, flapping and bounding; S, soaring. B, %PE variation with respect to groups of non-aerial locomotion. Abbreviations: A, aquatic; TG, terrestrial ground-dwelling; TN, terrestrial non-ground-dwelling; ATG, aquatic and grounddwelling; TGN, perching and ground-dwelling.
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O'Connor, Chiappe & Bell (2011) and divergence times are based on a 'literal' interpretation of the fossil record from Brockelhurst et al. (2012). Taxa abbreviations: Nth, Neornithes; Orph, Ornithuromorpha; Orn, Ornithothoraces; Orth, Ornithurae; Pyg, Pygostylia.
Figure 4 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 4. Biplots used in the selection of four variables for each fossil group of Mesozoic birds: humeral length (HL), femoral length (FL), diaphyseal craniocaudal width of ulna (dUW), and diaphyseal craniocaudal width of femur (dFWcc). Each predictor variable was plotted against a combination of the 14 remaining variables used for estimating body mass (BM; Table S3). The biplots show the regression line fitted for extant birds (grey circles) with the 95% confidence intervals for BM predictions (dotted lines). Following the procedure of selection of variables (see text), HL and FL were used for generating functions that can be applied to all fossil avian taxa. In contrast, dUW and dFWcc could not be incorporated into the functions adjusted for estimating BM in Archaeopterygidae and Enantiornithes, respectively.
Figure 3 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 3. Effects of weighting for the best-fitting multiple regression equation obtained from the modern data set (see Table 2). A, distribution of familiar residuals for unweighted data, and for data weighted by families. Each box plot with whiskers represents one family. Box length shows the interquartile range (25th and 75th percentiles). Horizontal lines indicate the 5–95% confidence limits. Asterisks show outliers. Abbreviations for families: Ac, Accipitridae; Ad, Alcedinidae; Ae, Aegothelidae; Al, Alcidae; An, Anatidae; Ap, Apodidae; Ar, Ardeidae; At, Artamidae; Au, Alaudidae; Ca, Caprimulgidae; Cc, Cacatuidae; Ch, Charadriidae; Ci, Ciconiidae; Co, Columbidae; Cn, Cinclidae; Cr, Coraciidae; Ct, Catharthidae; Cu, Cuculidae; Cv, Corvidae; Di, Diomedeidae; Fa, Falconidae; Fg, Fringillidae; Fr, Fregatidae; Ga, Gaviidae; Gr, Gruidae; He, Hemiprocnidae; Hi, Hirundinidae; La, Laridae; Ln, Lanidae; Me, Meleagridae; Mg, Megapodidae; Mo, Motacillidae; Mp, Meropidae; Ms, Musophagidae; Mu, Muscicapidae; Ot, Otididae; Pa, Paridae; Pc, Pelecanoididae; Pd, Podicipedidae; Pe, Pelecanidae; Ph, Phasianidae; Pic, Picidae; Pit, Pittidae; Pl, Phalacrocoracidae; Pn, Pandionidae; Po, Podargidae; Pr, Procellaridae; Ps, Psittacidae; Pt, Pteroclidae; Pu, Prunellidae; Ra, Rallidae; Re, Recurvirostridae; Ry, Rynchopidae; Sc, Stercoriidae; Sg, Strigidae; So, Scolopacidae; Sr, Sturnidae; St, Sternidae; Su, Sulidae; Sy, Sylviidae, Te, Tetraonidae; Th, Threskiornithidae; Tt, Tytonidae; Tu, Turdidae; Ty, Tyrannidae; Up, Upupidae. B, plot showing the variations of %MPE with the increase of individuals per family. The dashed line represents the unweighted multiple regression analysis (MR) and the grey line represents the weighted MR.
Figure 2 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 2. Illustration of osteological limb measurements used in this study and defined in Table 1: A, cranial aspect of the humerus; B, dorsal aspect of the ulna and the radius; C, dorsal aspect of the carpometacarpus; D, caudal aspect of the femur; E, caudal aspect of the tibiotarsus; F, cranial aspect of the tarsometarsus.
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity
<p>Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. </p>
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"
Open the record for dataset details and reuse information.
FIGURE 3. Multivariate analysis. A in Delimiting species within the Lysmata vittata (Stimpson, 1860) (Decapoda: Lysmatidae) species complex in a world full of invaders
FIGURE 3. Multivariate analysis. A, resultant biplot of Principal Component Analysis computed between Brazil (red) and USA (green) populations representing Lysmata vittata species complex based on 11 morphological characters showing the two first principal components, which represent 44.08% of total variation. B, resultant histogram of Discriminant Function Analysis values computed between Brazil (red) and USA (green) populations representing Lysmata vittata species complex based on 10 morphological characters.
Supplementary material 1 from: Rakotonirina JC, Csősz S, Fisher BL (2016) Revision of the Malagasy Camponotus edmondi species group (Hymenoptera, Formicidae, Formicinae): integrating qualitative morphology and multivariate morphometric analysis. ZooKeys 572: 81-154. https://doi.org/10.3897/zookeys.572.7177
Measurements of individual specimens : Explanation note: Basic measurements of individual specimens arranged by species code, collection code, and specimen code (unique identification number). See text for abbreviations.
PLATE 1 in Taxonomic Studies Using Multivariate Analysis of Eriobotrya Based on Morphological Traits
PLATE 1. Leaf morphologies of different species in Eriobotrya. Note: the number sequence is consistent with Table 1.
FIGURE 5 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 5. Known distribution of Pitcairnia robert-downsii González-Rocha, Espejo, López-Ferr. & M. Castillo.
FIGURE 1 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 1. Herbarium specimens of A) Pitcairnia palmeri, B) Pitcairnia robert-downsii, C) P. colimensis, and D) P. compostelae.
FIGURE 4 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 4. Pitcairnia robert-downsii González-Rocha, Espejo, López-Ferr. & M. Castillo, nom. et stat. nov. A–B) Detail of the inflorescence, C) habitat and D) dissected flower.
FIGURE 3 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 3. Dendrogram of the cluster analysis applied to the Euclidean distance matrix (data transformed to log 2) of 12 morphological variables, grouped by the UPGMA fusion technique. The abbreviations correspond to: P. col: P. colimensis, P. com: P. compostelae, P. lon: Pitcarnia palmeri var. longebracteata and P. pal: P. palmeri var. palmeri. * Indicates the nomenclatural types.
FIGURE 2 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 2. Scatterplot between the first two axes (cumulative variance of 66.7 %) derived from the principal component analysis applied to 12 morphological variables transformed to log 2. The symbols correspond to Pitcairnia colimensis (♦), P. compostelae (▲), P. palmeri var. longebracteata (●) and P. palmeri var. palmeri (■).
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
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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.