Comments (3)
@hxpGit512, you can check the Usage (Supplemental guidance -> generate a custom model) for details on how to train a model and save it into the existing model list. Also check out the celltypist.train function for the training parameters.
As an example, if you have an AnnData with highly variable genes, you can train the model using model = celltypist.train(adata[:, adata.var.highly_variable], 'cell_type_column', n_jobs = -1, max_iter = 500, check_expression = False)
followed by model.write(f"{celltypist.models.models_path}/some_model_name.pkl")
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@ChuanXu1 , Multiple annotated datasets can be trained in batches and then incorporated into one model ?
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@hxpGit512, do you mean multiple datasets with different annotations? If yes, you need to train and save each of them separately.
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Related Issues (20)
- " have completed preprocessing and cell clustering. " HOT 2
- codes for harmonizing the cell labels HOT 2
- the question about conf_score HOT 3
- CellTypist_Immune_Reference_v2 question HOT 2
- Immune_All_Low - model training question HOT 4
- Cluster-level label prediction HOT 1
- Running celltypist with concatenated dataset HOT 3
- Running the CellTypist training function celltypist.train on a subset of genes HOT 1
- ValueError in celltypist.annotate HOT 3
- is it available to generate model.pkl file from a marker gene list HOT 1
- Feature: Support Rapids-singlecell HOT 1
- Can not detect a neighborhood graph, will construct one before the over-clustering HOT 1
- multiple models HOT 3
- Question about over_clustering and conf_score? HOT 1
- Conflict with variable genes and model training HOT 1
- www.celltypist.org is down, cannot download models HOT 3
- Can not download the model HOT 2
- Very low confidence score even though labels are correct HOT 2
- unable to download models HOT 3
- Feature Request: Store freqs in annotation when majority_vote set to True HOT 2
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