opendvp.pl.imputation_qc

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opendvp.pl.imputation_qc#

imputation_qc(adata, unimputed_layer='unimputed', return_fig=False, ax=None, highlight_genes=None, show_highlighted_genes_names=True)#

Generate a quality control plot to visualize the effect of imputation.

This function creates a scatter plot comparing the mean expression of genes before and after imputation. The x-axis represents the mean of the raw (unimputed) data, and the y-axis shows the difference between the raw mean and the imputed mean. This helps to identify genes that were most affected by the imputation process. The plot also includes a 2D histogram and kernel density estimate to visualize the distribution of data points.

Parameters:

  • adata (AnnData) – An AnnData object containing the imputed data in adata.X and the unimputed data in a specified layer.

  • unimputed_layer (str (default: 'unimputed')) – The name of the layer in adata.layers that contains the unimputed data. Defaults to “unimputed”.

  • return_fig (bool (default: False)) – If True, returns the matplotlib Figure object. Defaults to False.

  • ax (Axes | None (default: None)) – A matplotlib Axes object to plot on. If None, a new figure and axes are created. Defaults to None.

  • highlight_genes (list[str] | None (default: None)) – A list of gene names to highlight in the plot. These genes will be plotted in a different color. Defaults to None.

  • show_highlighted_genes_names (bool (default: True)) – If True, displays the names of the highlighted genes on the plot. Defaults to True.

Return type:

A matplotlib Figure object if return_fig is True, otherwise None.

Examples

>>> import anndata
>>> import numpy as np
>>> import pandas as pd
>>> from opendvp.pl import imputation_qc

>>> # Create a dummy AnnData object
>>> n_obs, n_vars = 100, 50
>>> X_imputed = np.random.rand(n_obs, n_vars)
>>> X_raw = X_imputed.copy()
>>> # Introduce some NaNs to simulate unimputed data
>>> X_raw[np.random.choice([True, False], size=X_raw.shape, p=[0.1, 0.9])] = np.nan
>>> adata = anndata.AnnData(X_imputed)
>>> adata.layers["unimputed"] = X_raw
>>> adata.var_names = [f"Gene_{i}" for i in range(n_vars)]
>>> adata.obs_names = [f"Cell_{i}" for i in range(n_obs)]

>>> # Generate the QC plot
>>> imputation_qc(adata, highlight_genes=["Gene_5", "Gene_10"])
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