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--- |
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license: cc-by-nc-nd-4.0 |
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tags: |
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- biology |
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- single_cell |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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--- |
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# Converge-SC for Embeddings: How to use? |
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## Task Description |
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Single-cell embeddings are vector representations of cells that capture their biological characteristics in a high-dimensional space. These embeddings encapsulate gene expression patterns, allowing for efficient computational analysis, visualization, and comparison of cells. |
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The task is to generate embeddings for single-cell RNA-seq data using the pre-trained Converge-SC model. These embeddings can be used for downstream analysis tasks such as clustering, visualization, integration, and more. |
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## Basic Usage |
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The `examples` folder under the tab `files and versions` contains both the notebook and the gene mapping json file. |
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Go to the `examples/get_embeddings.ipynb` notebook to see how to generate embeddings for your single-cell data. |
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## Pipeline Description |
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The pipeline uses the pre-trained Converge-SC model to generate embeddings for each cell in your dataset. The workflow involves: |
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1. Loading your single-cell data (as an AnnData object) |
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2. Preprocessing and normalizing the data |
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3. Loading the pre-trained Converge-SC model and tokenizer |
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4. Generating embeddings for each cell |
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5. Storing the embeddings for downstream tasks |
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## Input Data Requirements |
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Your data should be in the form of an AnnData object (.h5ad file) with: |
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1. Expression Data: Gene expression measurements in adata.X |
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2. Gene Information: Gene identifiers in adata.var_names |
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## Preprocessing Steps |
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Before generating embeddings, you should preprocess your data: |
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1. Normalization: Normalize your data to a common scale |
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```python |
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import scanpy as sc |
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# Normalize to 10,000 counts per cell |
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sc.pp.normalize_total(adata, target_sum=1e4) |
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sc.pp.log1p(adata) # Log-transform the data |
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``` |
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2. Gene Name Mapping: Converge-SC's vocabulary is in gene symbols, not ENSEMBL IDs, so you'll need to map ENSEMBL IDs to gene symbols if applicable |
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```python |
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import json |
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# Load the mapping file |
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with open('examples/ensembl_to_gene_symbol.json', 'r') as file: |
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ensg_to_symbol = json.load(file) |
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# Map gene names |
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adata.var_names = adata.var_names.map(lambda col: ensg_to_symbol.get(col, col)) |
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``` |
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## Generating Embeddings |
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### Load model and tokenizer |
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```python |
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model = AutoModel.from_pretrained('ConvergeBio/ConvergeSC-embeddings', token='your_token_here', trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained('ConvergeBio/ConvergeSC-embeddings', token='your_token_here', trust_remote_code=True) |
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``` |
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### Compute Embeddings |
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```python |
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tokenized_cell = tokenizer(gene_names, expression_values=gene_values) |
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embedding = model(**tokenized_cell) |
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``` |
