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.

A two-dimensional PCA plot obtained from a multiple factor analysis (MFA) performed on all 22 populations using 142 bioclimatic variables retrieved from the WorldClim database.

SNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. PCA is not a single method that is always done the same way, but rather requires three choices which we explore as a three-way factorial: two kinds of PCA graphs by three SNP codings by six PCA variants. Our main three recommendations are simple and easily implemented: Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA (or AMMI1 if main effects are also of interest). We also document contemporary practices by a literature survey of 125 representative articles that apply PCA to SNP data, find that virtually none implement our recommendations. The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications.

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 a Differential Gene Expression (DGE) analysis of GSE44076.

The analysis compares tumor versus normal samples.

It uses the DESeq2 package for RNA-seq count data analysis.

The dataset includes quality control (QC) visualizations.

Principal Component Analysis (PCA) plots are provided for sample clustering.

Heatmaps illustrate the expression patterns of top differentially expressed genes.

EnhancedVolcano plots are included to visualize significant genes.

The dataset enables users to explore gene expression changes in colorectal cancer.

All R scripts and associated visualizations are included for reproducibility.

The workflow can be adapted for other RNA-seq datasets.

The dataset supports bioinformatics, transcriptomics, and cancer research studies.

It provides an educational resource for DESeq2-based RNA-seq analysis.

In the row for each variable, numbers indicate the strength of correlation of that variable with the eigenvector of each PC. When the absolute value of correlation coefficients was ≥ 0.3, they were considered important (bold font) in defining the PC. Variables loaded on PCs 1–3 below, appear in the PCA plot (Fig 4A) with an asterisk (*) to indicate they also project upward (since PC3 is perpendicular to axes for PCs 1 and 2).

Simulated 2D residual velocity fields in the inner German Bight were subjected to Principal Component Analysis (PCA). Residual currents were obtained from coastDat2 barotropic 2D simulations with the hydrodynamic model TRIM-NP V2.1.22 in barotropic 2D mode on a Cartesian grid (1.6km spatial resolution) stored on an hourly basis for the years 1948 - 2012 (doi:10.1594/WDCC/coastDat-2_TRIM-NP-2d) and later extended until August 2015. The present analysis refers to the period Jan 1958 - Aug 2015. The spatial domain considered is the region to the east of 6 degrees east and to the south of 55.6 degrees north. All grid nodes with a bathymetry of less than 10m were excluded. Residual velocities were calculated in two different ways: 1.) as 25h means, 2.) as monthly means. Both types of residual current data are available from * RESIDUAL_CURRENTS_195801_201508 The directory contains sub-directories for years and months. Daily residual currents for the 13th of September 1974, for instance, are stored in * RESIDUAL_CURRENTS_195801_201508/YEAR_1974/MONTH_09/TRIM2D_1974_09_13_means.nc while monthly mean residual currents for September 1974 are stored in: * RESIDUAL_CURRENTS_195801_201508/YEAR_1974/TRIM2D_1974_09_means.nc All current fields provided were interpolated from the original Cartesian model grid to a more convenient regular geographical grid (116x76 nodes). Mean residual currents are stored in: * mean_residual_currents.nc This data set contains residual velocities both on original Cartesian grid nodes and interpolated to the geographical grid. An example plot is provided: * mean_residual_currents.png For PCA, two residual velocity components from each of 12133 Cartesian grid nodes were combined into one data vector (length 2x12133), referring to 21061 daily or 692 monthly time levels. Results of two independent PCAs for either daily or monthly mean fields are stored in: * PCA_daily_residual_currents.nc * PCA_monthly_residual_currents.nc Files contain three leading Principal Components (PCs) and corresponding Emipirical Orthogonal Functions (EOFs). Again EOFs were also interpolated to a regular geographical grid. PC time series are also stored in plain ASCII format: * PCs_daily.txt * PCs_monthly.txt For monthly fields the number N of variables (N=2x12133) is much larger than the number T of time levels (T=692). Therefore, to reduce computational demands, the roles of time and space were formally interchanged. Having conducted the PCA the EOFs were then transformed back to the original spatial coordinates (cf. Section 12.2.6 in von Storch and Zwiers (1999), Statistical Analysis in Climate Research, Cambridge University Press). A much larger number of time levels made even this approach prohibitive for the full set of daily data. Therefore, PCAs were performed for six sub-periods (1958-1965, 1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015(Aug)) independently. EOFs obtained from these six sub-periods were then averaged to obtain EOFs representative for the whole period. Corresponding PCs were calculated by projecting daily fields onto these average EOFs. IMPORTANT: In contrast with PCA of monthly data, the PCA of daily data INVOLVES SOME APPROXIMATIONS! EOFs on the original nodes were normalized to have unit lengths. The following figures, * daily_EOF1.png * daily_EOF2.png * daily_EOF3.png show the first three EOFs obtained from daily data, assuming that corresponding PCs have the value of one standard deviation. The following two plots, * monthly_EOF1.png * monthly_EOF2.png show the leading EOFs for monthly mean data. EOF3 is omitted as it represents just a very small percentage of overall variance (1.7%).

We use version 12 (2022) of the V-Dem data (https://www.V-Dem.net) and apply standard principal component analysis (PCA). Following standard procedure, we normalized each V-Dem variable (i.e. centered it to a mean of zero and rescaled it to a variance of one) prior to performing PCA. For better readability of the plots, we rescaled all principal components uniformly such that the first component has a maximum absolute value of one (i.e. its values are bounded by [-1,1]) while preserving the mean of zero for all components. We further re-oriented each component such that its strongest loading is positive.

United States - Balance Sheet: Total Risk Based Capital (PCA Definition) was 2364706.75300 Mil. of U.S. $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Balance Sheet: Total Risk Based Capital (PCA Definition) reached a record high of 2364706.75300 in April of 2025 and a record low of 322350.26900 in January of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Balance Sheet: Total Risk Based Capital (PCA Definition) - last updated from the United States Federal Reserve on November of 2025.

(A) Microarray data were filtered for detectable probes and normalized with the BioConductor package vsn. Normalized data were used for calculation of pairwise distances and drawing of a heatmap by use of the BioConductor package geneplotter. Each column represents one sample and shows the correlation to all samples (including itself), with red for correlation = 1 and blue for the lowest observed correlation. Note the clear homogeneity in the samples from fertility classified heifers (HF, high fertile; SF, subfertile; IF, infertile). (B) PCA is a plot distribution indicating the source of greatest variation in the overall transcriptional profiles of the samples. Each symbol represents one replicate. Note the clear lack of separation of samples based on fertility classifications (HF, high fertile; SF, subfertile; IF, infertile).

United States - Balance Sheet: Tier 1 Risk Based Capital (PCA Definition) was 2271005.21400 Mil. of U.S. $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Balance Sheet: Tier 1 Risk Based Capital (PCA Definition) reached a record high of 2271005.21400 in April of 2025 and a record low of 147414.03800 in January of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Balance Sheet: Tier 1 Risk Based Capital (PCA Definition) - last updated from the United States Federal Reserve on October of 2025.

United States - Balance Sheet: Tier 1 Leverage Capital (PCA Definition) was 2271005.21400 Mil. of U.S. $ in April of 2025, according to the United States Federal Reserve. Historically, United States - Balance Sheet: Tier 1 Leverage Capital (PCA Definition) reached a record high of 2271005.21400 in April of 2025 and a record low of 147414.03800 in January of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Balance Sheet: Tier 1 Leverage Capital (PCA Definition) - last updated from the United States Federal Reserve on November of 2025.

In this lesson, students interpret a scatter plot showing the results of a principal components analysis (PCA). They view an interview with Dr. Stephanie Smith, who explains how PCA calculations work, and why she chose to use this analysis to visualize her data. Dr. Smith also discusses her journey becoming a scientist and describes a typical day at work.

IntroductionThe vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders.AimTo examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry.Methods192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique.ResultsIn the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes.ConclusionWe present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

Dataset

This dataset was created by DEV AHUJA

Released under Apache 2.0

Contents

Ancestry background of AoU IBD clusters. (A) PCA plot for all AoU NYC participants and reference panels. (B) PCA plots highlighting individuals belonging to the 14 IBD clusters detected in AoU NYC participants. (C) SCOPE analysis for AoU NYC participants labelled with the 14 IBD clusters. Each color represents global ancestry proportion of the five superpopulations (African, European, American, East Asian and South Asian) inferred using supervised mode in SCOPE.

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)df

Graph and download economic data for Balance Sheet: Total Risk Based Capital (PCA Definition) (QBPBSTRSKK) from Q1 1990 to Q2 2025 about capital and USA.

PCA is applied to SNP main effects and S×I interaction effects combined (S&S×I), and the portion of each is shown in the last two columns.