TumorArchetypeR
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| Auteurs principaux: | , |
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2026
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| _version_ | 1866901698183692288 |
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| author | Lütge, Mechthild Nassiri, Sina |
| author_facet | Lütge, Mechthild Nassiri, Sina |
| contents | <p>This package provides a complete workflow to discover robust tumor archetypes. It automates the following key steps:</p> <ul> <li>Scoring: Computes enrichment scores for gene signatures using ssGSEA or loads pre-calculated scores.</li> <li>Normalization: Optionally normalizes scores by tumor purity and scales signatures to ensure equal contribution.</li> <li>Dimensionality Reduction: Performs PCA on the enrichment scores.</li> <li>Clustering: Applies Louvain clustering across a user-defined grid of parameters (e.g., different variance cutoffs and k-neighbors).</li> <li>Characterization: Evaluates each clustering result using internal metrics, cluster stability, and association with clinical variables.</li> </ul> <p>Finally, the package helps you select an optimal clustering result and train a robust single-sample classifier that can be used to apply the derived subtypes to new datasets.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18933914 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TumorArchetypeR Lütge, Mechthild Nassiri, Sina Tumor Microenvironment <p>This package provides a complete workflow to discover robust tumor archetypes. It automates the following key steps:</p> <ul> <li>Scoring: Computes enrichment scores for gene signatures using ssGSEA or loads pre-calculated scores.</li> <li>Normalization: Optionally normalizes scores by tumor purity and scales signatures to ensure equal contribution.</li> <li>Dimensionality Reduction: Performs PCA on the enrichment scores.</li> <li>Clustering: Applies Louvain clustering across a user-defined grid of parameters (e.g., different variance cutoffs and k-neighbors).</li> <li>Characterization: Evaluates each clustering result using internal metrics, cluster stability, and association with clinical variables.</li> </ul> <p>Finally, the package helps you select an optimal clustering result and train a robust single-sample classifier that can be used to apply the derived subtypes to new datasets.</p> |
| title | TumorArchetypeR |
| topic | Tumor Microenvironment |
| url | https://doi.org/10.5281/zenodo.18933914 |