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53 results for “principal components analysis”
Data from: Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data
To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult.
FIGURE 4. Principal component analysis comparing Ancistrus falconensis n in Ancistrus falconensis n. sp. and A. gymnorhynchus Kner (Siluriformes: Loricariidae) from central Venezuelan Caribbean coastal streams
FIGURE 4. Principal component analysis comparing Ancistrus falconensis n. sp. and A. gymnorhynchus. PC2 and PC3 are Principal Component axes two and three.
FIGURE 2. Discriminant-function analysis summarizing principal components 1 – 6 in Two new species of lizards from the Liolaemus lineomaculatus section (Squamata: Iguania: Liolaemidae) from southern Patagonia
FIGURE 2. Discriminant-function analysis summarizing principal components 1 – 6, for all six species of the L. lineomaculatus group. Black circles: L. morandae sp. nov.; white circles: L. avilae sp. nov.; black squares: L. lineomaculatus; white squares: L. kolengh; gray triangles: L. hatcheri; black triangles: L. silvanae.
FIGURE 7. Principal components analysis using shape information for Galaxiella pusilla s.s in A review of Galaxiella pusilla (Mack) (Teleostei: Galaxiidae) in south-eastern Australia with a description of a new species
FIGURE 7. Principal components analysis using shape information for Galaxiella pusilla s.s. (black) and Galaxiella toourtkoourt (grey) generated from 12 landmarks for a) females (PC1 and PC2 explain 52.0% and 14.5% of the total variance, respectively), b) males (PC1 and PC2 explain 54.4% and 14.7% of the total variance, respectively), and shape changes associated with principal component 2 for c) females and d) males.
FIGURE 3. Principal component analysis showing the morphological separation among the 35 in A new species of the Craugastor podiciferus species group (Anura: Craugastoridae) from the premontane forest of southwestern Costa Rica
FIGURE 3. Principal component analysis showing the morphological separation among the 35 individuals of Craugastor gabbi sp. nov. (solid squares) from the premontane forest near the Costa Rican-Panamanian border and 155 individuals of C. stejnegerianus (open triangles) from the lowlands of South Pacific Costa Rica.
FIGURE 9. Principal Component Analysis showing morphometric data from C in Two new troglobitic Coarazuphium Gnaspini, Godoy & Vanin 1998 species of ground beetles from iron ore Brazilian caves (Coleoptera: Carabidae: Zuphiini)
FIGURE 9. Principal Component Analysis showing morphometric data from C. spinifemur new species (red dots); C. amazonicus new species (green triangles) and C. tapiaguassu (purple dots): AL, Antenna length; OBL, Overall body length; HL, Head length; HW, Head width; PL, Pronotum length; PW, Pronotum width; EL, Elytra length; EW, Elytra width; PF, Profemur length; PTI, Protibia length; PTA, Protarsus length; MSF, Mesofemur length; MSTI, Mesotibia length; MSTA, Mesotarsus length; MTF, Metafemur length; MTTI, Metatibia length; MTTA, Metatarsus length.
Fig. 23. Principal Components Analysis plot for 23 and 25 in Two new species of Vaejovis (Scorpiones: Vaejovidae) belonging to the mexicanus group from Aguascalientes, Mexico, with comments on the homology and function of the hemispermatophore
Fig. 23. Principal Components Analysis plot for 23 and 25 measurements taken from males and females, respectively. First and second PCA for males were body size and metasoma length (97% of variance explained), and for females it was patella and carapace lengths (92% of variance explained) [upper plate]. The variable vector dimensions that separate the principal components of males and females are shown in the lower plate.
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>
FIGURE 2. Principal components analysis. Size accounted for 90.1 in Revision of the horseface loaches (Cobitidae, Acantopsis), with descriptions of three new species from Southeast Asia
FIGURE 2. Principal components analysis. Size accounted for 90.1% of the obserVed Variance. The sheared second and third principal components (PC2 and PC3) accounted for 3.7% and 2.3% of the obserVed Variance, respectiVely. Body depth (0.46) and caudal-peduncle length (-0.43) had the highest loadings on sheared PC2. Pectoral-fin length (-0.49) had the highest loading on sheared PC3.
FIGURE 5. Principal components analysis depicting morphometric variables distinguishing northern subspecies, H. l in Phylogenetic structure of Holbrookia lacerata (Cope 1880) (Squamata: Phrynosomatidae): one species or two?
FIGURE 5. Principal components analysis depicting morphometric variables distinguishing northern subspecies, H. l. lacerata (green), from southern subspecies, H. l. subcaudalis (purple).
FIG. 1. Principal components analysis. Size accounted for 96.91 in Variation in the Arrow Loach, Nemacheilus masyae (Cypriniformes: Nemacheilidae), in Mainland Southeast Asia with Description of a New Species
FIG. 1. Principal components analysis. Size accounted for 96.91% of the observed variance. The sheared second and third principal components (PC2 and PC3) accounted for 0.85% and 0.51%, respectively, of the observed variance. Interorbital width (0.52) and pectoral-fin length (–0.42) had the highest loadings on sheared PCII; gape width (0.57) and body depth (–0.47) had the highest loadings on sheared PCIII. Filled circle ¼ individuals of N. zonatuS, empty circle ¼ individuals within the range described by Kottelat (1990) for N. maSYae, and X ¼ individuals within the range described by Kottelat (1990) for N. palliduS.
Figure 1. Sheared principal component analysis plot depicting morphometric variation among Cambarus aff. dubius, C. pauleyi, C in Cambarus loughmani, a new species of crayfish (Decapoda: Cambaridae) endemic to the pre-glacial Teays River Valley in West Virginia, USA
Figure 1. Sheared principal component analysis plot depicting morphometric variation among Cambarus aff. dubius, C. pauleyi, C. loughmani, and nominate C. dubius.
FIGURE 4. Principal component analysis. a in Intra- and interspecific analysis of first instar larval morphology in the genus Berberomeloe Bologna 1989 (Coleoptera: Meloidae)
FIGURE 4. Principal component analysis. a—Factor loadings of principal component analysis (BL: body length; HL: head length; HW: maximum head width; HW2: head width at the posterior bend; TL: thorax length; PTL: 1st thoracic segment length; AL: abdomen length; SA1L: antennal sensory appendix I length; SA1W: antennal sensory appendix I width; SA2L: antennal sensory appendix II length; SA2W: antennal sensory appendix II width;); b—Factor scores of the examined populations on the first two principal component; c—Factor scores of the two species on the first two principal component.
FIGURE 6. Principal component analysis of the nine call measurements for various species selections. A: all small Anthus species except A. cervinus; B: two types of calls of A. r. rubescens and A. [r.] japonicus. C: only common-type calls of A. r. rubescens and A. [r.] japonicus; D: A. petrosus and different A. spinoletta subspecies; E: A. petrosus and A. s. spinoletta. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE 6. Principal component analysis of the nine call measurements for various species selections. A: all small Anthus species except A. cervinus; B: two types of calls of A. r. rubescens and A. [r.] japonicus. C: only common-type calls of A. r. rubescens and A. [r.] japonicus; D: A. petrosus and different A. spinoletta subspecies; E: A. petrosus and A. s. spinoletta.
Data from: Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data
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Data from: Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix
Open the record for dataset details and reuse information.
PERMANOVA results from Principal component analysis of avian hind limb and foot morphometrics and the relationship between ecology and phylogeny
<p class="MsoNoSpacing">Principal component analysis has been used to test for similarities in ecology and life habit between modern and fossil birds, however, the two main portions of the hindlimb—the foot and the long bone elements—have not been examined separately. We examine the potential links between morphology, ecology, and phylogeny through a synthesis of phylogenetic paleoecological methods and morphospace analysis. Both hindlimb morphologies and species' ecologies exhibit extreme phylogenetic clumping, although these patterns are at least partially explainable by a Brownian motion style of evolution. Some morphologies are strongly correlated with particular ecologies, while some ecologies are occupied by a variety of morphologies. Within the morphospace analyses, the length of the hallux (toe I) is the most defining characteristic of the entire hindlimb. The foot and hindlimb are represented on different axes when all measurements are considered in an analysis, suggesting that these structures undergo morphological change separately from each other. Early birds tend to cluster together, representing an unspecialized basal foot morphotype and a hindlimb reliant on hip-driven, not knee-driven, locomotion. Direct links between morphology, ecology, and phylogeny are unclear and complicated, and may be biased due to sample size (~60 species). This study should be treated as a preliminary analysis that further studies, especially those examining the vast diversity of modern birds, can build upon.</p>
Data from: pcadapt: an R package to perform genome scans for selection based on principal component analysis
The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.
Figure 6. Principal components analysis for dataset 1 in The systematics of the dusky striped squirrel, Funambulus sublineatus (Waterhouse, 1838) (Rodentia: Sciuridae) and its relationships to Layard's squirrel, Funambulus layardi Blyth, 1849
Figure 6. Principal components analysis for dataset 1.
Figure 7. Principal components analysis for dataset 2 in The systematics of the dusky striped squirrel, Funambulus sublineatus (Waterhouse, 1838) (Rodentia: Sciuridae) and its relationships to Layard's squirrel, Funambulus layardi Blyth, 1849
Figure 7. Principal components analysis for dataset 2 including type material examined.
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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)
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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.