📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
ROC Curve is primarily used for bioinformatics analysis.
Features: 1. Draw ROC curve to evaluate classifier performance 2. Automatically calculate and display AUC value (area under curve) 3. Evaluate diagnostic or predictive ability of variables 4. Suitable for binary classification problems (e.g., disease diagnosis, prognosis prediction) 5. Support continuous variables such as gene expression, clinical indicators 6. Input file requires: sample ID (first column as row names), classification label (0/1, second column), continuous variables (third column onwards)
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 |
|---|---|---|
| ROC Data File | 包含样本ID、分类标签（0/1）和连续型变量列的文件 | Required Default: None |
| Analysis Variable (Optional) | 用于ROC分析的变量列名，留空则使用第三列 | Optional 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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