X-loading values show positive and negative correlations responsible for the cluster formation along the first principal component (PC1) in the PCA score plot.

This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionality reduction on datasets consisting of multivariate probability distributions. Each plot of the image series used UA-PCA to project a dataset consisting of multivariate normal distributions. The covariance matrices of the dataset instances were scaled with different factors resulting in different UA-PCA projections. The projected probability distributions are displayed using isolines of their probability density functions. As the scaling value increases, the projection changes, showing the sensitivity of UA-PCA to changes in variance.

A visualization plot of a data set of molecular data is a useful tool for gaining insight into a set of molecules. In chemoinformatics, most visualization plots are of molecular descriptors, and the statistical model most often used to produce a visualization is principal component analysis (PCA). This paper takes PCA, together with four other statistical models (NeuroScale, GTM, LTM, and LTM-LIN), and evaluates their ability to produce clustering in visualizations not of molecular descriptors but of molecular fingerprints. Two different tasks are addressed: understanding structural information (particularly combinatorial libraries) and relating structure to activity. The quality of the visualizations is compared both subjectively (by visual inspection) and objectively (with global distance comparisons and local k-nearest-neighbor predictors). On the data sets used to evaluate clustering by structure, LTM is found to perform significantly better than the other models. In particular, the clusters in LTM visualization space are consistent with the relationships between the core scaffolds that define the combinatorial sublibraries. On the data sets used to evaluate clustering by activity, LTM again gives the best performance but by a smaller margin. The results of this paper demonstrate the value of using both a nonlinear projection map and a Bernoulli noise model for modeling binary data.

Plot of principal component axes against herbivory measures.

Additional file 5: Disease feature matrix.

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We used paleomagnetic results from Sites 998, 999, 1000, and 1001 to estimate the paleolatitude of the Caribbean region over the past 80 m.y. The data include remanence measurements of split-core sections (typically 1.5 m long) and discrete samples (6-12 cm**3 in volume) from volcanic and sedimentary rocks. From these, we computed 15 new paleolatitude estimates for Sites 999 and 1001 on the Caribbean plate and three new paleolatitude estimates for Site 998 on the Cayman Rise, currently on the southern North American plate. One estimate from Site 1001 is based on 230 measurements made along split-core sections of basalt after demagnetization of 20-25 mT. The other 17 estimates are based on principal component analysis of demagnetization data from 438 discrete paleomagnetic samples from sedimentary units. Where necessary, the 18 new paleolatitude estimates are corrected for a polarity ambiguity bias that occurs when averaging paleomagnetic data from drill cores that have shallow inclinations and are not azimuthally oriented. We also investigated the contribution of additional biases that may arise from a compaction-related inclination error, which could affect the sedimentary units, though not the basalt units. Several lines of evidence, including the lack of a correlation between porosity (or water content) and inclination, indicate that the inclination error is small, if present at all. The results from Sites 999 and 1001 indicate that the Caribbean plate was 5°-15° south of its current position at ~80 Ma, possibly placing it directly over the equator in the Late Cretaceous. Although the data do not preclude changes in the rate of northward motion over the past 80 m.y., they are consistent with a constant northward progression at a rate of 18 km/m.y. Given the uncertainties in the data, rates of northward motion could be as low as 8 km/m.y. or as high as 22 km/m.y. These results are compatible with several existing models for the evolution of the Caribbean plate, including those that have the Caribbean plate originating in the Pacific Ocean west of subduction zones active in the Central American region during the Cretaceous, and those that have the Caribbean plate originating within the Central American region, though more than 1000 km west of its current position relative to North and South America.

Figure S1: Scree plots showing the proportion of variation explained (percentage, %; Y-axis) by each principal component (PC; X-axis) in (A) Miscanthus sinensis, and (B) Miscanthus sacchariflorus. These PCs are from a principal component analysis conducted on 5,140 genome-wide markers.Figure S2: Phenotypic distribution of individuals in the study populations. Distributions of Miscanthus sinensis (blue), Miscanthus sacchariflorus (green), and the 09F2 population (orange) for traits Basal circumference (Bcirc; cm), Compressed circumference (Ccric; cm), Culm length (CmL; cm), Diameter of basal internode (DBI; mm), days to first heading (HD1; days), and Yield (Yld; g/plant). The median value of each population is represented in solid lines with colors corresponding to their respective populations. The trait values of the parental lines are represented in broken lines with blue corresponding to ‘Cosmopolitan Revert’ from M. sinensis, and green corresponding to ‘Robustus’ from M. sacchariflorus.Figure S3: Barplots showing the narrow-sense heritability (Y-axis) for basal circumference (Bcirc; cm), compressed circumference (Ccirc; cm), culm length (CmL; cm), days to first heading (HD1; days), and yield (Yld; g/plant) (X-axis), color coded based on the three populations considered in this study Miscanthus sinensis (Msi), Miscanthus sacchariflorus (Msa), and F2 breeding population (09F2).Figure S4: Principal component (PC) analysis of Miscanthus sacchariflorus and Miscanthus sinensis diversity panels. Open circles are individuals distributed along PC1 (X-axis) and PC2 (Y-axis) in (A) M. sacchariflorus and (B) M. sinensis. These PCs are from a principal component analysis conducted on 5,140 genome-wide markers. The diamond shapes represent the parents ‘Robustus’ from M. sacchariflorus and “Cosmopolitan Revert” from M. sinensis that were used to develop the interspecific F2 population (09F2). Color coding of the individuals was based on genetic clusters from previous analyses (Clark et al. 2014, 2018) conducted on these data.Figure S5: Heatmap showing the genetic relatedness using 5,140 genome-wide markers between accessions in the Miscanthus sinensis and Miscanthus sacchariflorus diversity panels and 09F2 breeding population. This heatmap is presented for (A) all three populations, (B) Msi only, (C) Msa only, and (D) 09F2 breeding population only.Figure S6: Distribution of individuals selected by CDmean on the Miscanthus sinensis (Msi) principal component axes for basal circumference (Bcirc), compressed circumference (Ccirc), culm length (CmL), days to first heading (HD1), and yield (Yld). The X-axis on each graph is principal component (PC) 1, while the Y-axis is PC2. Both PCs are from a principal component analysis of 5,140 genome-wide markers. The individuals selected by the CDmean procedure are colored, and the Msi parent of the 09F2 population, “Cosmopolitan Revert”, is indicated by a diamond.Figure S7: Distribution of individuals selected by CDmean on the Miscanthus sacchariflorus (Msa) principal component axes for basal circumference (Bcirc), compressed circumference (Ccirc), culm length (CmL), days to first heading (HD1), and yield (Yld). The X-axis on each graph is principal component (PC) 1, while the Y-axis is PC2. Both PCs are from a principal component analysis of 5,140 genome-wide markers. The individuals selected by the CDmean procedure are colored, and the Msa parent of the 09F2 population, “Robustus”, is indicated by a diamond.Figure S8: Linkage disequilibrium decay curves for (A) Miscanthus sinensis diversity panel and Miscanthus sacchariflorus within 50 kilobase (kb) window, (B) Miscanthus sinensis diversity panel, Miscanthus sacchariflorus, and 09F2 breeding population within 250 kb window, and (C) Miscanthus sinensis diversity panel, Miscanthus sacchariflorus, and 09F2 breeding population within a 2,000 kb window. On each graph, the X-axis is the physical distance between marker pairs and they Y-axis is the squared Pearson correlation between the markers.Figure S9: Comparison of using diversity panels and F2 populations as GS training sets for making predictions in simulated F2 populations. For each prediction accuracy of a given F2 population, either the diversity panels, or a stratified random sample of the remaining 49 F2 populations were used as training set. Thus, each boxplot represents a distribution of prediction accuracies (Y-axis) across 50 simulated interspecific F2 populations for traits with contrasting genetic architectures (X-axis). Each boxplot was colored based on the approach used to train the genomic selection model which are: Msa (all 598 individuals in the Miscanthus sacchariflorus panel), Msi (all 538 individuals in the Miscanthus sinensis panel), F2.6H (600 randomly selected individuals from the 50 simulated F2 populations), Msi.Msa (sum of the genomic estimated breeding values, or GEBVs, estimated from Msi and Msa panels), F2.1K (1,200 randomly selected individuals from the 50 simulated F2 populations), MM.F6H (sum of the GEBVs estimated from Msi and Msa panels, and 600 randomly selected individuals from the 50 simulated F2 populations), MM.F1K (sum of the GEBVs estimated from Msi and Msa panels, and 1,000 randomly selected individuals from the 50 simulated F2 populations), F2.10K (all the individuals (n=10,800) in the 50 simulated F2 populations), and MM.F9K (sum of the GEBVs estimated from Msi and Msa panels, and 10,800 individuals from the 50 simulated F2 populations). Traits were simulated using five different scenarios, namely: D.QTN (traits simulated with completely different QTN in Msi and Msa but with the same effect sizes), D.QTN.Msa (traits simulated with different QTNs in each of Msi and Msa, with Msa QTNs having large effects while Msi QTNs had small effects), D.QTN.Msi (traits simulated with different QTNs in each of Msi and Msa, with Msi QTNs having large effects while Msa QTNs had small effects), P.QTN (traits where with 50% of the QTNs were the same across Msi and Msa, while 50% were different), and S.QTN (traits simulated in Msi and Msa based on the same QTNs and same effect sizes). All simulated traits had 20 additive QTN, 0 dominance QTN, and 0 epistatic QTN, while the heritabilities are as presented in Table 2. The white dots represent the mean value of each distribution.Table S1: Summary statistics of phenotypic least squares means within Miscanthus sinensis and Miscanthus sacchariflorus diversity panels and the 09F2 breeding population.

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Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations

Provenance for this README

• File name: README.txt
• Authors: Keji Yuan, Rance Nault
• Date created: 2025-04-15
• Date modified: 2025-04-22

Dataset Version and Release History

• Current Version:
  • Number: 1.0.0
  • Date: 2025-04-22
  • Persistent identifier: https://doi.org/10.5061/dryad.k3j9kd5kz
  • Summary of changes: n/a
• Embargo Provenance: n/a
  • Scope of embargo: n/a
  • Embargo period: n/a

Dataset Attribution and Usage

• Dataset Title: Data for the article "Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations"
• Persistent Identifier: n/a
• Dataset Contributors:
  • Creators: Keji Yuan, Rance Nault
• Date of Issue: 2025-04-15
• Publisher: Michigan State University
• License: Use of these data is covered by the following license:
  • T...

• This dataset contains differential gene expression (DGE) analysis results derived from the GEO accession GSE166044. • The analysis was performed using the DESeq2 workflow for identifying significantly upregulated and downregulated genes. • Raw count data from the study were normalized and processed following standard RNA-seq best practices. • Quality assessment and exploratory analysis were conducted using Principal Component Analysis (PCA). • Gene expression clustering patterns were visualized using heatmaps generated from transformed count matrices. • Volcano plots were created to highlight differentially expressed genes based on fold change and statistical significance. • The dataset includes all scripts, result files, and visualization outputs generated through the R-based analysis pipeline. • This resource provides a complete reproducible workflow for understanding expression differences between experimental groups in GSE166044. • The provided R script automates normalization, variance stabilization, PCA plotting, heatmap creation, and volcano plot generation. • Researchers can use this dataset to reproduce the analysis, extend downstream biological interpretation, or use it as a reference for their own RNA-seq workflows.

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Supplemental Methods. Table S1. list of selected candidate genes, GO terms annotations, number of rare protein altering variants (PAV) contained in each gene, and gene-level P-values obtained from SKAT analysis. Table S2. list of rare PAV (MAF < 0.05) identified in CH cases and controls in 745 candidate genes. Table S3. P-values for single marker P-values of all tested SNPs. Table S4. logistic regression analysis. P-values for additive effect of SNPs controlling for sex as a covariate (P sex), and for sex, age and cigarettes per day (P sex, age, CPD). Table S5. Association analysis in CH cases and controls for significant migraine susceptibility SNPs emerged in meta-analysis. Figure S1. Principal component analysis (PCA) plot. Figure S2. Quantile-quantile (Q-Q) plot of Fisher’s exact test P-values for association with cluster headache (CH). Figure S3. Local association plots for chromosome 14 locus (A) and the chromosome 7 locus (B) [17, 18, 24, 41]. (ZIP 6890 kb)

Data for urban clustering used in the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation". We release two datasets for urban clustering based on data acquired in Santiago de Chile. The first dataset is computed at the level of urban blocks. The second dataset is computed at the level of individuals using a uniform sample of Santiago inhabitants. Both datasets comprises features based on social characteristics (e.g., SES), land use, and aesthetic visual perception of the city. The features of each data unit (blocks or individuals) are provided using row packing (each row is a data unit) in CSV files. We release PCA (Principal Components Analysis) features for both datasets.

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation. To understand the whole history of populations, it is ideal to capture the history of their range expansion with reference to the series of surrounding environments and to infer the multi-trait coadaptation. To this end, we propose a complementary approach; it is an exploratory analysis using up-to-date methods that integrates population genetic features and features of selection on multiple traits. First, we conduct correspondence analysis of site frequency spectra, traits and environments with auxiliary information of population-specific fixation index (FST). This visualizes the structure and the ages of populations and helps infer the history of range expansion, encountered environmental changes and selection on multiple traits. Next, we further investigate the inferred history using an admixture graph that describes the population split and admixture. Finally, principal component analysis of the selection on edge-by-trait (SET) matrix identifies multi-trait coadaptation and the associated edges of the admixture graph. We introduce a newly defined factor loadings of environmental variables in order to identify the environmental factors that caused the coadaptation. A numerical simulation of one-dimensional stepping-stone population expansion showed that the exploratory analysis reconstructed the pattern of the environmental selection that was missed by analysis of individual traits. Analysis of a public dataset of natural populations of black cottonwood in northwestern America identified the first principal component (PC) coadaptation of photosynthesis- vs growth-related traits responding to the geographical clines of temperature and daylength. The second PC coadaptation of volume-related traits suggested that soil condition was a limiting factor for above-ground environmental selection.