📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
Multi-variable ROC Curve is primarily used for bioinformatics analysis.
Features: 1. Generate ROC curves for multiple predictors 2. Automatically calculate AUC (Area Under Curve) for each variable 3. Display multiple ROC curves in a single plot 4. Use different colors to distinguish variables 5. Show AUC values for each variable in legend 6. Suitable for diagnostic biomarker comparison and model evaluation 7. Input format: First column is outcome variable (binary 0/1), remaining columns are predictors 8. Supports CSV, TXT, XLSX formats
Main Use Cases:
- Explore data patterns and support subsequent in-depth analysis
Key Features:
- Standardized processing and result export
- Key charts and table output
- Can be used as input for subsequent analysis
Parameter Details
| Parameter | Meaning & Recommendations | Settings |
|---|---|---|
| Multi-variable Data File | 第一列为结果变量（0/1），其余列为预测变量 | Required Default: None |
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 60 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.
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