### Principal components analysis (PCA)

**Introduction**
Principal components analysis, PCA. Plotted by ggbiplot

**Input data instructions**
Matrix input data. The first row is group names, the second row is sample names, other rows are data. PC1 and PC2 is the first and the second Principal components (explainary extend of latent variable to the differences). Points represent samples, different colors represent different groups. Ellipses represent 68% confidence intervals of core regions. Arrows represent original variables, the directions of arrows represent correlation between original variable and principle components, lengths represent devotion of original data to principle components.

**Examples from papers**
**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?**

**203** papers cited SXplot (Google Scholoar). You can cite the original package, or using the following format:

Heatmap was plotted by http://www.bioinformatics.com.cn, a free online platform for data analysis and visualization.

**4) FAQs**