| title: t-SNE (t-Distributed Stochastic Neighbor Embedding) (Transcriptome-centric, 3D) | |
| emoji: 🧬 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: python | |
| tags: | |
| - transcriptomics | |
| - dimensionality-reduction | |
| - tsne | |
| license: mit | |
| # t-SNE (t-Distributed Stochastic Neighbor Embedding) (Transcriptome-centric, 3D) | |
| Pre-trained t-SNE (t-Distributed Stochastic Neighbor Embedding) model for transcriptomics data compression, part of the TRACERx Datathon 2025 project. | |
| ## Model Details | |
| - **Method**: t-SNE (t-Distributed Stochastic Neighbor Embedding) | |
| - **Compression Mode**: Transcriptome-centric | |
| - **Output Dimensions**: 3 | |
| - **Training Data**: TRACERx open dataset (VST-normalized counts) | |
| ## Usage | |
| This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline. | |
| It will be automatically downloaded and cached when needed. | |
| ```python | |
| import joblib | |
| # Load the model bundle | |
| model_data = joblib.load("model.joblib") | |
| # Access components based on model type | |
| # See documentation for specific usage | |
| ``` | |
| ## Files | |
| - `model.joblib`: Model bundle containing fitted model and preprocessing parameters | |