PCA
Definition
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional biological data into a smaller set of uncorrelated variables called principal components. In bioinformatics, PCA identifies patterns in complex datasets like gene expression profiles, proteomics data, or genomic sequences by capturing the maximum variance in the data. The first principal component accounts for the largest variance, with subsequent components capturing progressively less. PCA is essential for exploratory data analysis, batch effect detection, sample clustering, and identifying outliers in omics datasets. It simplifies visualization of multi-dimensional biological data while preserving the most significant variation patterns, making it invaluable for understanding relationships between samples, experimental conditions, or biological states.
Visualize PCA in Nodes Bio
Researchers can visualize PCA results as network graphs where samples or features cluster based on principal component scores. Nodes representing samples can be connected by similarity edges, revealing experimental groupings or biological subtypes. Gene or protein loadings from PCA can be mapped onto molecular interaction networks to identify which pathways or functional modules drive the observed variance, enabling integration of dimensionality reduction with network-based pathway analysis.
Visualization Ideas:
- Sample similarity networks colored by principal component scores
- Gene co-expression networks weighted by PCA loading contributions
- Multi-omics integration networks showing concordant variance patterns across data types
Example Use Case
A cancer researcher performs RNA-seq on 200 tumor samples across multiple subtypes. After applying PCA to 20,000 gene expression values, the first two principal components separate samples into distinct clusters corresponding to known cancer subtypes. By examining gene loadings on PC1, the researcher identifies that immune response genes contribute most to the variance. Visualizing these high-loading genes in a protein-protein interaction network reveals a central hub around interferon signaling, suggesting this pathway distinguishes aggressive from indolent tumors and could inform therapeutic stratification.