Principal Component Analysis (PCA) is a commonly used dimensionality reduction method, mainly used for data processing, analysis, and feature extraction. The purpose is to project high-dimensional data into a low dimensional space through linear transformation, while preserving the main information and features in the data as much as possible. For example, reducing the high-dimensional abundance table of multiple features to two dimensions facilitates the comparison of overall differences between groups.

Input

Gene abundance table

A tab separated text file containing row and column names, with each column representing a sample and each row representing a feature (gene, species, metabolite,etc.).

Group Information Table

A tab separated text file containing row and column names, with column names fixed at (SampleID, Category). The first column is the sample name, and the second column is the corresponding group for the sample.

Output

The samples in the same group in the figure use the same color and pattern. The closer the distance between two samples, the more similar the species composition structure. Therefore, samples with high community structure similarity tend to cluster together, while samples with significant community differences will be far apart。