📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
Clinical Heatmap is primarily used for visualizing gene/sample expression patterns with heatmaps.
Create annotated heatmaps using pheatmap with clinical information. Supports sample sorting by clinical variables and row/column clustering options.
Main Use Cases:
- Display expression patterns of differential genes across different samples
- Perform unsupervised clustering of samples to assist in identifying subtypes or outliers
Key Features:
- Hierarchical clustering/row-column scaling
- Custom color schemes and annotation bars
- Support for selecting top genes to display
Parameter Details
| Parameter | Meaning & Recommendations | Settings |
|---|---|---|
| Expression Matrix | 上传基因表达矩阵（行为基因，列为样本） | Required Default: None |
| Clinical Data | 上传包含样本临床信息的文件 | Required Default: None |
| Sort By Variable | 输入临床信息文件中的列名 | Optional Default: None |
| Cluster Genes | 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 |
| Cluster Samples | 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: false |
| Heatmap Colors | Color Palette: Improves figure readability and distinguishes groups. Recommendation: Use higher contrast palettes for many categories; use gradients for continuous variables. | Optional Default: BlueWhiteRed |
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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