ehrapy.preprocessing.qc_metrics#

ehrapy.preprocessing.qc_metrics(adata, qc_vars=(), layer=None, inplace=True)[source]#

Calculates various quality control metrics.

Uses the original values to calculate the metrics and not the encoded ones. Look at the return type for a more in depth description of the calculated metrics.

Parameters:

adata (AnnData) – Annotated data matrix.
qc_vars (Collection[str]) – Optional List of vars to calculate additional metrics for.
layer (Optional[str]) – Layer to use to calculate the metrics.
inplace (bool) – Whether to add the metrics to obs/var or to solely return a Pandas DataFrame.

Return type:

Optional[DataFrame]

Returns:

Pandas DataFrame of all calculated QC metrics.

Observation level metrics include:

missing_values_abs: Absolute amount of missing values.
missing_values_pct: Relative amount of missing values in percent.

Feature level metrics include:

missing_values_abs: Absolute amount of missing values.
missing_values_pct: Relative amount of missing values in percent.
mean: Mean value of the features.
median: Median value of the features.
std: Standard deviation of the features.
min: Minimum value of the features.
max: Maximum value of the features.

Examples

>>> import ehrapy as ep
>>> import seaborn as sns
>>> import matplotlib.pyplot as plt
>>> adata = ep.dt.mimic_2(encoded=True)
>>> ep.pp.qc_metrics(adata)
>>> sns.displot(adata.obs["missing_values_abs"])
>>> plt.show()