Principal components analysis (PCA)
Introduction
PCA is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Input data instructions
1) Sample in rows, features (e.g. genes) in columns: The first column is sample names, the second column is group names (not pure integers), and the other columns are features. PC1, PC2 in the output figure are the first, and the second principal components (degree of explainary of latent variables to differences). ref: prcomp for PCA calculation, FactoMineR R package for plotting
2) Sample in cols, features (e.g. genes) in rows: The first row is sample names, the second row is group names (not pure integers), and the other rows are features.
scale the data by default.
Examples from papers
[Nature communications] Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer. Fig4h
1) How to plot?
1, Prepare data
2, Open with excel, and change into the same format as the example
3, Copy and paste data into the input frame
4, Select parameters
5, Submit and download figure files
2) Why NO figure generated?
Script need strigent input format, please read the instructions and examples carefully.
3) How to cite?
2000+ papers in (Google Scholar)
Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. PMID: 37943830.
4) FAQs