**Function：**

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is often used to cluster samples and evaluate relationships of multiple samples.

**Input：**

① Matrix dataset file: the first column is gene names and the first row is sample names.

② Column ID: samples names for the principal component analysis. We will use all the samples in the uploaded file for PCA if no information here. Here the first column is sample names and the second column is group names. Samples in the same group will be given the same color.

③ Row ID: genes names for the principal component analysis. We will use all genes in the uploaded file for PCA if no information here.

**Parameters：**

Standardizing: It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. Standardizing can minimized the effect caused by huge differences between the data. We recommend standardizing the data.

**Output：**

A two-dimension PCA graph with PC1 as the x axis and PC2 as the y axis in PDF/PNG format.

*PCA analysis principle of parameters, the results and interpretation*

##### Select matrix file: Example

##### Samples and indexes of input needs analysis

z13-15 z14-8 yh2

protein-5 POD-10 SOD-5 SOD-10

Output：