FACSPy.ml.supervisedGating.run_data_setup

FACSPy.ml.supervisedGating.run_data_setup#

supervisedGating.run_data_setup(gated_samples, layer, scaling='StandardScaler', n_cells=None)#

Public method to run the data setup. Extracts gated samples and scales the data.

Parameters:

  • gated_samples (list[str]) – A list of sample_IDs of gated samples

  • layer (str) – The slot in adata.layers that contains the data to be used for training the classifier.

  • scaling (Optional[str]) – Scaling method. Can be one of MinMaxScaler, StandardScaler or RobustScaler.

  • n_cells (Optional[int]) – If integer, the indicated number of cells will be subsampled for training.

Return type:

None

Examples

>>> adata = fp.create_dataset([...])
>>> gating = fp.ml.supervisedGating(dataset)
>>> gating.run_data_setup(
...     gated_samples = ["1", "2", "3"],
...     layer = "compensated",
...     scaling = "StandardScaler"
... )
>>> gating.setup_classifier("DecisionTreeClassifier")
>>> gating.tune_hyperparameters(
...    method = "HalvingRandomSearchCV",
...    grid = {"max_depth": [10,20,100]}
... )
>>> gating.setup_classifier("DecisionTreeClassifier")
>>> sampler = GateSampler(...)
>>> gating.sample_cells(sampler)
>>> gating.train()
>>> gating.gate_dataset()
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