Plot¶
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doubletdetection.plot.
convergence
(clf, show=False, save=None, p_thresh=1e-07, voter_thresh=0.9)[source]¶ Produce a plot showing number of cells called doublet per iter
Parameters: - clf (BoostClassifier object) – Fitted classifier
- show (bool, optional) – If True, runs plt.show()
- save (str, optional) – filename for saved figure, figure not saved by default
- p_thresh (float, optional) – hypergeometric test p-value threshold that determines per iteration doublet calls
- voter_thresh (float, optional) – fraction of iterations a cell must be called a doublet
Returns: matplotlib figure
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doubletdetection.plot.
normalize_counts
(raw_counts, pseudocount=0.1)[source]¶ Normalize count array. Default normalizer used by BoostClassifier.
Parameters: - raw_counts (ndarray) – count data
- pseudocount (float, optional) – Count to add prior to log transform.
Returns: Normalized data.
Return type: ndarray
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doubletdetection.plot.
threshold
(clf, show=False, save=None, log10=True, log_p_grid=None, voter_grid=None, v_step=2, p_step=5)[source]¶ - Produce a plot showing number of cells called doublet across
- various thresholds
Parameters: - clf (BoostClassifier object) – Fitted classifier
- show (bool, optional) – If True, runs plt.show()
- save (str, optional) – If provided, the figure is saved to this filepath.
- log10 (bool, optional) – Use log 10 if true, natural log if false.
- log_p_grid (ndarray, optional) – log p-value thresholds to use. Defaults to np.arange(-100, -1). log base decided by log10
- voter_grid (ndarray, optional) – Voting thresholds to use. Defaults to np.arange(0.3, 1.0, 0.05).
- p_step (int, optional) – number of xlabels to skip in plot
- v_step (int, optional) – number of ylabels to skip in plot
Returns: matplotlib figure
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doubletdetection.plot.
umap_plot
(raw_counts, labels, n_components=30, show=False, save=None, normalizer=<function normalize_counts>, random_state=None)[source]¶ Produce a umap plot of the data with doublets in black.
Count matrix is normalized and dimension reduced before plotting.Parameters: - raw_counts (array-like) – Count matrix, oriented cells by genes.
- labels (ndarray) – predicted doublets from predict method
- n_components (int, optional) – number of PCs to use prior to UMAP
- show (bool, optional) – If True, runs plt.show()
- save (str, optional) – filename for saved figure, figure not saved by default
- normalizer ((ndarray) -> ndarray, optional) – Method to normalize raw_counts. Defaults to normalize_counts, included in this package. Note: To use normalize_counts with its pseudocount parameter changed from the default 0.1 value to some positive float new_var, use: normalizer=lambda counts: doubletdetection.normalize_counts(counts, pseudocount=new_var)
- random_state (int, optional) – If provided, passed to PCA and UMAP
Returns: matplotlib figure ndarray: umap reduction