📋 Analysis Log
Error Information
📁 Main Task Files (for subsequent analysis) （Inherited from parent）
📊 Task Summary
Result Files
Tool Introduction & Use Cases
PCA is primarily used for PCA dimensionality reduction and visualization of sample differences.
Principal Component Analysis for dimension reduction
Main Use Cases:
- Quickly check if samples separate by groups and identify potential outliers
- Assess batch effects and dominant sources of variation
Key Features:
- PCA dimensionality reduction and coordinate output
- Group/batch coloring and confidence ellipses
- Variance contribution rate statistics
Parameter Details
| Parameter | Meaning & Recommendations | Settings |
|---|---|---|
| Grouping File | Experimental Grouping/Control Setting: Defines comparison groups (two or more) and reference group. Recommendation: Reference group is typically "control/untreated". Ensure grouping matches sample annotations. | 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 120 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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