R code template utilised for the PCA analysis. It has no anotations. Article: Tryptophan Levels as a Marker of Auxins and Nitric Oxide signaling Authored by: Pedro López-Gómez; Edward Smith; Pedro Bota; Alfonso Cornejo; Marina Urra; Javier Buezo; Jose F. Moran

R Code for PCA analysis

Edmunds et al./FACS data for PCA/Exp_56

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.

This data publication contains a map of biological reserves, in the conterminous United States (US), larger than 500 hectares managed by four US federal agencies: Bureau of Land Management (BLM), Fishand Wildlife Service (FWS), Forest Service (FS), and National Parks Service (NPS). Within such US Federal Protection Network, only federal areas in conservation classifications corresponding to The World Conservation Union categories I to IV (IUCN 1994) are included. These categories include designated, candidate, and officially recommended wilderness areas; forest reserves; natural areas and landmarks; wildlife refuges; cooperative management and protection areas; and national parks, preserves, monuments, and conservation areas. This data publication also includes maps of the loadings of the first three axes of the Principal Component Analysis (PCA) used to characterize the climate space of the conterminous United States of America (CONUS). The PCA was performed using climate variables depicting annual and seasonal trends in temperature, precipitation, moisture index, relative humidity, as well as growing degree days and growing season length.These data were used within a quantitative classification that stratified the climatic variability of the conterminous United States to (a) evaluate the characteristics and rarity of the climate in federally managed areas, (b) determine cases where climate is not well represented by the network of protected federal land (i.e., a climate gap analysis).Original metadata date was 06/24/2014. Minor metadata updates were made on 12/13/2016 and 12/11/2024.

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.

This dataset comprises example slices, histograms and principal components analyses of all cores analyzed.

I6YEU0_MYCTU Pyruvate carboxylase

PCA of genes differentially expressed in VCF and VNCF.

Population genetic studies in non-model systems increasingly use next-generation sequencing to obtain more loci, but such methods also generate more missing data that may affect downstream analyses. Here we focus on the Principal Component Analysis (PCA) which has been widely used to explore and visualize population structure with mean-imputed missing data. We simulated data of different population models with various total missingness (1%, 10%, 20%) introduced either randomly or biased among individuals or populations. We found that individuals biased with missing data would be dragged away from their real population clusters to the origin of PCA plots, making them indistinguishable from true admixed individuals and potentially leading to misinterpreted population structure. We also generated empirical data of the big brown bat (Eptesicus fuscus) using restriction site-associated DNA sequencing (RADseq). We filtered three data sets with 19.12%, 9.87%, and 1.35% total missingness, all showing nonrandom missing data with biased individuals dragged towards the PCA origin, consistent with results from simulations. We highlight the importance of considering missing data effects on PCA in non-model systems where nonrandom missing data are common due to varying sample quality. To help detect missing data effects, we suggest to 1) plot PCA with a color gradient showing per sample missingness, 2) interpret samples close to the PCA origin with extra caution, 3) explore filtering parameters with and without the missingness-biased samples, and 4) use complementary analyses (e.g., model-based methods) to cross-validate PCA results and help interpret population structure.

Code for PCA

The objective of the experiment was to compare the proteome of EPS-urine from PCa and BPH patients.

Members of this family are LysR-family transcription factors associated with operons for catabolism of protocatechuate . Members occur only in proteobacteria.

Supporting dataset for Figure 1a: Principal Component Analysis (PCA) of transcriptomic profiles in sweet potato leaves under drought stress.

This dataset accompanies the article:

**“Unveiling Stage-Specific Flavonoid Dynamics Underlying Drought Tolerance in Sweet Potato (*Ipomoea batatas* L.) via Integrative Transcriptomic and Metabolomic Analyses”**

Figure 1a illustrates PCA results based on global gene expression patterns from sweet potato leaves sampled under control and drought stress at two developmental stages. The PCA highlights clear clustering between treatment groups, indicating distinct transcriptomic responses.

### This dataset includes:
- Gene expression matrix (Sheet 1 of `PCA_transcriptomics_data.xlsx`)
- Sample group metadata (Sheet 2 of the same file)
- R script used for PCA analysis and figure generation (`PCA_transcriptomics_plot.R`)
- Final PCA figure (`PCA_transcriptomics_plot.pdf`)
- Metadata (`README.md`) and license file (`LICENSE`)

This resource enables full reproducibility of Figure 1a and facilitates open reuse in plant drought transcriptomics research.

Dataset

This dataset was created by Melville Pais

Released under CC0: Public Domain

Contents

50 paired benign/cancer samples from prostate tissue generated in 2 different runs - on 3 plates on the IonTorrent Proton. Total of 200 fastq.gz single end runs. Read length ~300 bp. %GC 44 Sequences per file approx 1 Mio.

Collection description

None

Subject species

homo sapiens

Modality

fMRI-BOLD

Cognitive paradigm (task)

movie watching task

Map type

Z

The slow-fast continuum is known to structure variation in life-history strategies across species. Within populations, it is also assumed to structure individual life histories, yet evidence of its existence remains unclear. We formally assessed the presence of a slow-fast continuum of life histories both within populations and across species using detailed individual-based data for 17 bird and mammal species with contrasting life histories. We estimated adult lifespan, age at first reproduction, breeding frequency and fecundity, and identified the main axes of variation using Principal Component Analyses. The slow-fast continuum was the main axis of life-history variation across species, but within populations individual variation did not follow the slow-fast continuum in any species. This suggests that individual life histories are neither slow nor fast, but rather follow an idiosyncratic pattern across species because of relative differences in the importance of processes such as sto...

Content

PCA was performed on Preprocessing-1 of Titanic Dataset and this Dataset correspond to the projection of 8 of the features.

Dataset corresponding Kernel : https://www.kaggle.com/spektrum/intro-pca-kmeans-and-t-sne-on-titanic-dataset

This dataset is about: Principal components analyses (PCA) of invertebrate groups inhabiting a Macrocystis integrifolia bed off Chipana (northern Chile). 180 quadrats