Processed Signatures
This page hosts our in-house single-cell RNA (scRNA) signatures for various applications. Below, you will find details about the processed signatures, including their source, cell type information, gene count, and download links.
Available Signatures
| Dataset | Level | Number of Cell Types | Key Cell Types | Total Genes | Source | Download |
|---|---|---|---|---|---|---|
| Lung Cancer Signature | Level 1 (Major) | Placeholder | Epithelial, Immune, Stromal | Placeholder | Figshare | Download |
| Level 2 (Subtypes) | Placeholder | Basal, Luminal, T Cells, Fibroblasts | Placeholder | Figshare | ||
| Level 3 (Detailed) | Placeholder | CD4+ T cells, CD8+ T cells, CAFs | Placeholder | Figshare |
Signature File Contents
Each signature file is provided in a pickle format (.pickle) and contains the following data:
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Gene-Level Statistics:
- Mean Expression: Average gene expression values across all cells for each cell type.
- Variance: Variability in expression for each gene across cells.
- Standard Deviation: Standard deviation of gene expression for each gene within each cell type.
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Gene Lists:
- Total Genes: The total number of genes included in the dataset.
- Highly Variable Genes (HVGs): Genes identified as highly variable, which contribute significantly to the analysis.
- Differentially Expressed Genes (DEGs): Genes selected by AutoGenes, including the number of DEGs identified.
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Cell Types:
- A complete list of cell types present in the dataset, organized by hierarchical levels (e.g., major, subtype, detailed).
How to Use
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Select the desired signature from the table above.
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Click the Download button to access the file.
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Use the appropriate level based on your analysis requirements:
- Level 1 (Major): Broad categories of cell types.
- Level 2 (Subtypes): Subdivisions within major categories.
- Level 3 (Detailed): Detailed classification of cell types.
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Refer to the [Tutorial] for guidance on using these signatures in your workflows.
Notes
- The pickle file is Python-compatible. Users of other programming environments (e.g., R) may need to convert the file using a Python script.
- Ensure that gene identifiers in the signature match those in your bulk RNA-seq data to avoid discrepancies during analysis.
- This page will be regularly updated as new signatures are added. If you have feedback or specific requests, please contact us.
Feedback: For specific datasets or additional levels of detail, please reach out to our team through the contact form on the main site.