3D PCA Analysis is primarily used for PCA dimensionality reduction and visualization of sample differences.

Features: 1. Perform principal component analysis (PCA) on high-dimensional data 2. Extract first three principal components (PC1, PC2, PC3) 3. Generate 3D scatter plot to show sample distribution 4. Automatic coloring based on grouping information 5. Display variance explained by each principal component 6. Automatically generate legend 7. Suitable for visualizing multidimensional data such as gene expression and proteomics data 8. Input format: First column is group information, remaining columns are features (e.g., gene expression values) 9. Supports CSV, TXT, XLSX formats

Main Use Cases:

• Quickly check if samples separate by groups and identify potential outliers
• Assess batch effects and dominant sources of variation

Key Features:

• PCA dimensionality reduction and coordinate output
• Group/batch coloring and confidence ellipses
• Variance contribution rate statistics