PCA analysis Service

Overview

Principal component analysis (PCA) transforms multivariate biological datasets into orthogonal axes of maximum variation. By decomposing covariance structures, it extracts dominant patterns to visualize sample clustering, detect technical artifacts, and reveal evolutionary relationships. This dimensionality-reduction approach efficiently simplifies complex omics data while preserving essential biological signals for downstream discovery. It serves as a fundamental tool for exploring the genetic diversity and evolutionary history of species, enabling the identification of major genetic axes that explain the largest proportion of the observed genetic variance.

Our PCA Analysis Service elevates your research with:

Dimensionality Reduction & Pattern Identification: Use advanced PCA to simplify genomic data, revealing hidden genetic patterns & population structures in lower dimensions.
Population Structure Inference: Precisely infer population structure & ancestry via genetic relationship visualization, uncovering migration, admixture & evolutionary insights.
Customizable Analysis Pipelines: Adapt analytical workflows to your research needs, be it single-population diversity, multi-population comparison, or complex trait genetics.
Integration with Other Analytical Tools: Seamlessly combine PCA results with other genetic analyses (e.g., phylogenetics, admixture mapping) for a holistic genetic architecture view.

What Is PCA Analysis

PCA analysis service is an advanced bioinformatics solution. PCA analysis aims to decode complex genetic structures and population relationships. By using the advanced principal component analysis algorithm, this service simplifies high-dimensional genetic data into a more manageable low-dimensional format, making it easier to visualize and interpret. We are a team composed of geneticists and bioinformaticians. We can identify potential genetic patterns, population stratification and genetic ancestor signals, otherwise these signals might still be hidden in the vast amount of genomic information.

How to Measure

1. Data Collection and Preparation

Genomic Data Acquisition: We begin by collecting high-quality genomic data from your samples of interest. This could encompass whole-genome sequencing, exome sequencing, or SNP array data. Our team collaborates closely with you to ensure the data is representative and adheres to our rigorous quality standards.
Data Cleaning and Filtering: After data collection, we undertake comprehensive cleaning and filtering processes. This involves eliminating low-quality reads, rectifying sequencing errors, and excluding SNPs with low call rates or high missing data. By ensuring the data is pristine and reliable, we set the stage for precise PCA analysis.

2. PCA Analysis

Data standardization: To eliminate the dimensional influence among different features, we first standardize the data, that is, change the mean of each feature to 0 and the standard deviation to 1.
Calculate the covariance matrix: The standardized data covariance matrix reflects the relationship among various features in the data.
Calculate eigenvalues and eigenvectors:Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors.
Select the principal components: Based on the magnitude of the eigenvalues, select the eigenvectors corresponding to the top k largest eigenvalues as the new basis vectors.
Data dimensionality reduction: Project the original data onto a low-dimensional space composed of principal components.

Figure 1: PCA analysis.

What Can We do

Data Compilation & Refinement

Diverse Data Collection: Access extensive genomic resources from public (e.g., 1000 Genomes Project) and private partnerships, covering diverse species for broad, representative datasets.
Meticulous Data Cleansing: Apply stringent quality control to assess data integrity, remove low-quality sequences, and correct errors, ensuring pristine, reliable data.

Population Structure Elucidation via PCA

Sophisticated PCA Implementation: Use advanced PCA algorithms to dissect genomic data, revealing population structures, ancestry, and genetic connections for a clear view of diversity and evolution.
Dynamic Visualization of Results: Create engaging visualizations (scatter plots, bar charts, heatmaps) to easily grasp genetic distinctions and communicate findings.

PCA Result Integration & Advanced Analysis

Seamless Integration with Other Analyses: Combine PCA results with admixture mapping, phylogenetics, and GWAS for a deeper understanding of genetic architecture and robust conclusions.
Customized Follow-up Analysis: Design tailored analyses based on PCA findings to explore genetic hypotheses, from trait-gene associations to evolutionary predictions.

Expert Interpretation & Reporting

In-depth Result Interpretation: Leverage geneticists' and bioinformaticians' expertise for detailed PCA result analysis, highlighting key patterns and biological insights.
Comprehensive Reporting: Prepare thorough, accessible reports summarizing findings, visualizations, and interpretations for effective communication and utilization.

Our Advantages

Data Compatibility Mastery: Capable of easily managing a wide range of data types, including SNP arrays, whole-genome sequences, exome data, etc.
Tailored Pipeline Customization: Customize algorithms for research topics.
Efficient Automated Processes: Complete automated process; The risk of human error has been reduced
Timely Delivery Guarantee: Arrange the most efficient analysis based on the research plan. Deliver to the customer as soon as possible.
Expert Support & Interpretation: Provide expert support and explanation. Help understand the research results

Applications

Genomics and Human Health

Population structure correction: Analyzing population differences among various regions.

Disease subtype discovery: Identifying molecular subtypes in complex diseases through sequencing data.

Transcriptomics

Cell type visualization: Visualizing clusters of different cell types or states within an tissue

Trajectory inference: Define the main variation axes (pseudo-time) of the development or differentiation process from the gene expression profile.

Microbiome and Ecology

Microbial community analysis: Visualizing β -diversity from 16S/ITS data

Environmental gradient analysis: Linking community changes with environmental variables.

Host-microbe interaction: Exploring the relationship between the host genotype or phenotype (for example, the expression of host immune genes) and the composition of the microbiome.

Agriculture and Plant Science

Germplasm characterization: Assessment of genetic diversity and kinship in breeding collections.

Trait association pre-screening: Reducing multicollinearity among phenotypic traits (such as wheat yield components) before association studies or genomic predictions.

Environmental response analysis: Determine genotypes with stable or plastic responses.

Demo

Figure 2: Bray-Curtis beta-diversity analysis at the family taxonomic level and PCA representation for the fecal microbiota (Lachance, 2024)

Case Study

Microbial signature of plaque and gut in acute coronary syndrome

Journal：Sci Rep

Published：2023

Gut microbiota is an emerging editable cardiovascular risk factor. We aim to investigate gut and coronary plaque microbiota, using fecal samples and angioplasty balloons from patients with acute coronary syndrome (ACS), chronic coronary syndrome (CCS) and control subjects.

This study examined bacterial communities in gut and coronary plaques by 16S rRNA sequencing and we performed droplet digital PCR analysis to investigate the gut relative abundance of the bacterial genes CutC/CntA involved in trimethylamine N-oxide synthesis. Linear discriminant analysis effect size (LEfSe) at the genus and species levels displayed gut enrichment in Streptococcus, Granulicatella and P. distasonis in ACS compared with CCS and controls; Roseburia, C. aerofaciens and F. prausnitzii were more abundant in controls than in patients.

Principal component analysis (PCA) of 41 differentially abundant gut taxa showed a clustering of the three groups. In coronary plaque, LEfSe at the genus level revealed an enrichment of Staphylococcus and Streptococcus in ACS, and Paracoccus in CCS, whereas PCA of 15 differentially abundant plaque taxa exhibited clustering of ACS and CCS patients. CutC and CntA genes were more abundant in ACS and CCS than in controls while no significant difference emerged between ACS and CCS. Our results indicate that ACS and CCS exhibit a different gut and plaque microbial signature, suggesting a possible role of these microbiotas in coronary plaque instability.

Figure 3: E Principal component analysis (PCA) based on deregulated OTUs in the gut.

FAQs

What is the ideal sample size for PCA analysis?

A: The recommended sample size varies based on your research goals and the genetic diversity of your study population. For basic PCA analysis, genomic data from at least 20-30 individuals can provide meaningful insights. However, for more nuanced population structure elucidation, especially in genetically diverse or admixed populations, larger sample sizes (50+ individuals) are advisable to enhance statistical power and accuracy.

Can PCA replace cluster analysis?

A: No! The two complement each other:

PCA: Exploratory Analysis, visualizing the global structure
Clustering: Defining discrete groups (such as cell types)

Best practice: Use PCA for dimensionality reduction → Use t-SNE/UMAP for visualization → Use Leiden algorithm for clustering.

Why don't my biological duplicate samples aggregate on the PCA plot?

A: Check the three common causes:

Missing preprocessing: No cross-sample normalization (e.g. DESeq2 vst transform)
Batch effect dominant: ComBat correction required
True biological heterogeneity: Individual differences outweigh treatment effects (Sample size is recommended)

References

Lachance G, Robitaille K, Laaraj J, et al. The gut microbiome-prostate cancer crosstalk is modulated by dietary polyunsaturated long-chain fatty acids. Nat Commun. 2024 Apr 23;15(1):3431. https://doi.org/10.1038/s41467-024-45332-w
Pisano E, Bugli F, Severino A, et al. Microbial signature of plaque and gut in acute coronary syndrome. Sci Rep. 2023 Sep 7;13(1):14775. https://www.nature.com/articles/s41598-023-41867-y

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.