📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
Discrete Survival Analysis is primarily used for single-cell data preprocessing, clustering, and annotation.
Features: 1. Survival analysis for multi-category discrete variables 2. Automatic calculation of Log-rank test p-value for group differences 3. Generate Kaplan-Meier survival curves 4. Optional risk table display 5. Suitable for tumor stage, grouping and other discrete variables 6. Input file requires: id (sample ID), futime (survival time), fustat (survival status, 0=censored, 1=death), and grouping variable
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 |
|---|---|---|
| Survival Data File | 包含id、futime（生存时间）、fustat（生存状态）和分组变量列的文件 | Required Default: None |
| Grouping Variable | 用于生存分析的离散型变量列名（如：Stage、Group等） | Required Default: Stage |
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.
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