📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
Correlation Coefficient Plot is primarily used for calculating and visualizing correlations (between samples or genes).
Create correlation coefficient heatmaps using corrplot package. Supports multiple visualization types (circle, color, number) and hierarchical clustering.
Main Use Cases:
- Assess overall consistency between samples or identify co-expression gene modules
- Provide basis for network analysis and module discovery
Key Features:
- Correlation matrix and heatmap
- Support for multiple correlation coefficients (Pearson/Spearman)
- Clustering and module visualization
Parameter Details
| Parameter | Meaning & Recommendations | Settings |
|---|---|---|
| Expression Data | 上传包含多个基因表达数据的文件 | Required Default: None |
| Correlation Method | Analysis Method Selection: Different methods have different distribution assumptions and robustness. Recommendation: For RNA-seq differential analysis, use DESeq2/edgeR; for microarrays, use limma; for enrichment, use hypergeometric/ORA or GSEA. | Optional Default: spearman |
| Display Type | 选择相关矩阵的展示方式 | Optional Default: circle |
| Hierarchical Clustering | Clustering Method: Determines how samples/genes are merged into clusters. Recommendation: Hierarchical clustering (complete/average) is intuitive; k-means suits larger samples with predefined cluster numbers. | Optional Default: true |
| Significance Marks | 是否在图上标记统计显著的相关 | Optional Default: true |
| Color Scheme | Color Palette: Improves figure readability and distinguishes groups. Recommendation: Use higher contrast palettes for many categories; use gradients for continuous variables. | Optional Default: RdBu |
Input File Requirements
Please ensure your input file format is correct, as this is fundamental to successful analysis. Typically, you need to provide a data matrix containing gene/protein expression values.
This tool does not currently provide sample files. Please refer to the common format: typically CSV or TXT files with the first column as gene/protein IDs, followed by expression values for each sample.
Frequently Asked Questions
Q: How long does the analysis take?
A: Estimated execution time is approximately 90 seconds, depending on your data size and current server load.
Q: What if the analysis fails?
A: If the analysis fails, your credits will not be deducted. First, check if your input file format matches the sample file, or adjust parameters based on error messages. If the problem persists, please contact us through the page's feedback function.
Feedback Tips
We value your feedback! Please describe your issue or suggestion in detail, and we will reply as soon as possible.
My Feedback History
Login to view your feedback history