"""
Module for detecting various quality control aspects from source data.
"""
import operator
import pathlib
from functools import reduce
from typing import Dict, List, Optional, Union
import pandas as pd
import yaml
from cytodataframe.frame import CytoDataFrame
from scipy.stats import zscore as scipy_zscore
DEFAULT_QC_THRESHOLD_FILE = (
f"{pathlib.Path(__file__).parent!s}/data/qc_nuclei_thresholds_default.yml"
)
[docs]
def identify_outliers(
df: Union[CytoDataFrame, pd.DataFrame, str],
feature_thresholds: Union[Dict[str, float], str],
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
include_threshold_scores: bool = False,
export_path: Optional[str] = None,
) -> Union[pd.Series, CytoDataFrame]:
"""
This function uses z-scoring to format the data for detecting outlier
nuclei or cells using specific CellProfiler features. Thresholds are
the number of standard deviations away from the mean, either above
(positive) or below (negative). We recommend making sure to not use a
threshold of 0 as that would represent the whole dataset.
Args:
df: Union[CytoDataFrame, pd.DataFrame, str]
DataFrame or file string-based filepath of a
Parquet, CSV, or TSV file with CytoTable output or similar data.
feature_thresholds: Dict[str, float]
One of two options:
A dictionary with the feature name(s) as the key(s) and their assigned
threshold for identifying outliers. Positive int for the threshold
will detect outliers "above" than the mean, negative int will detect
outliers "below" the mean.
Or a string which is a named key reference found within
the feature_thresholds_file yaml file.
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
An optional feature thresholds file where thresholds may be
defined within a file.
include_threshold_scores: bool
Whether to include the threshold scores in addition to whether
the threshold set passes per row.
export_path: Optional[str] = None
An optional path to export the data using CytoDataFrame export
capabilities. If None no export is performed.
Note: compatible exports are CSV's, TSV's, and parquet.
Returns:
Union[pd.Series, CytoDataFrame]:
Outlier series with booleans based on whether outliers were detected
or not for use within other functions.
"""
# Ensure the input is a CytoDataFrame, converting if necessary
df = CytoDataFrame(data=df)
# reference the df for a new outlier_df
outlier_df = df
# Define the naming scheme for z-score columns based on thresholds
thresholds_name = (
f"cqc.{feature_thresholds}"
if isinstance(feature_thresholds, str)
else "cqc.custom"
)
# If feature_thresholds is a string, load the thresholds from the specified file
if isinstance(feature_thresholds, str):
feature_thresholds = read_thresholds_set_from_file(
feature_thresholds=feature_thresholds,
feature_thresholds_file=feature_thresholds_file,
)
# Dictionary to store mappings of features to their z-score column names
zscore_columns = {}
for feature in feature_thresholds:
# Ensure the feature exists in the DataFrame
if feature not in df.columns:
raise ValueError(f"Feature '{feature}' does not exist in the DataFrame.")
# Construct the z-score column name
zscore_col = f"{thresholds_name}.Z_Score.{feature}"
# Calculate and store z-scores only if not already present
if zscore_col not in outlier_df:
outlier_df[zscore_col] = scipy_zscore(df[feature])
# Add the column name to the zscore_columns dictionary
zscore_columns[feature] = zscore_col
# Helper function to create outlier detection conditions
def create_condition(feature: str, threshold: float) -> pd.Series:
# Positive threshold checks for outliers above the mean
if threshold > 0:
return outlier_df[zscore_columns[feature]] > threshold
# Negative threshold checks for outliers below the mean
return outlier_df[zscore_columns[feature]] < threshold
# Generate outlier detection conditions for all features
conditions = [
create_condition(feature, threshold)
for feature, threshold in feature_thresholds.items()
]
# Construct the result based on whether threshold scores should be included
if include_threshold_scores:
# Extract z-score columns for each feature
zscore_df = outlier_df[list(zscore_columns.values())]
# Combine conditions into a single Series indicating outlier status
is_outlier_series = reduce(operator.and_, conditions).rename(
f"{thresholds_name}.is_outlier"
)
# Combine z-scores and outlier status into a single DataFrame
result = CytoDataFrame(
data=pd.concat([zscore_df, is_outlier_series], axis=1),
data_context_dir=df._custom_attrs["data_context_dir"],
data_mask_context_dir=df._custom_attrs["data_mask_context_dir"],
)
else:
# Combine conditions into a single Series of boolean values
result = reduce(operator.and_, conditions)
# Export the result if an export path is specified
if export_path is not None:
export_df = CytoDataFrame(result) if isinstance(result, pd.Series) else result
export_df.export(file_path=export_path)
# Return the resulting Series or DataFrame
return result
[docs]
def find_outliers(
df: Union[CytoDataFrame, pd.DataFrame, str],
metadata_columns: List[str],
feature_thresholds: Union[Dict[str, float], str],
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
export_path: Optional[str] = None,
) -> pd.DataFrame:
"""
This function uses identify_outliers to return a dataframe
with only the outliers and provided metadata columns.
Args:
df: Union[CytoDataFrame, pd.DataFrame, str]
DataFrame or file string-based filepath of a
Parquet, CSV, or TSV file with CytoTable output or similar data.
metadata_columns: List[str]
List of metadata columns that should be outputted with the outlier data.
feature_thresholds: Dict[str, float]
One of two options:
A dictionary with the feature name(s) as the key(s) and their assigned
threshold for identifying outliers. Positive int for the threshold
will detect outliers "above" than the mean, negative int will detect
outliers "below" the mean.
Or a string which is a named key reference found within
the feature_thresholds_file yaml file.
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
An optional feature thresholds file where thresholds may be
defined within a file.
export_path: Optional[str] = None
An optional path to export the data using CytoDataFrame export
capabilities. If None no export is performed.
Note: compatible exports are CSV's, TSV's, and parquet.
Returns:
pd.DataFrame:
Outlier data frame for the given conditions.
"""
# Resolve feature_thresholds if provided as a string
if isinstance(feature_thresholds, str):
feature_thresholds = read_thresholds_set_from_file(
feature_thresholds=feature_thresholds,
feature_thresholds_file=feature_thresholds_file,
)
# Determine the columns required for processing
required_columns = list(feature_thresholds.keys()) + metadata_columns
# Interpret the df as CytoDataFrame
df = CytoDataFrame(data=df)[required_columns]
# Filter DataFrame for outliers using identify_outliers
outliers_mask = identify_outliers(
# Select only the required columns from the DataFrame
df=df,
feature_thresholds=feature_thresholds,
feature_thresholds_file=feature_thresholds_file,
)
outliers_df = df[outliers_mask]
# Print outlier count and range for each feature
print(
"Number of outliers:",
outliers_df.shape[0],
f"({'{:.2f}'.format((outliers_df.shape[0] / df.shape[0])*100)}%)",
)
print("Outliers Range:")
for feature in feature_thresholds:
print(f"{feature} Min:", outliers_df[feature].min())
print(f"{feature} Max:", outliers_df[feature].max())
# Include metadata columns in the output DataFrame
result = outliers_df[required_columns]
# Export the file if specified
if export_path is not None:
result.export(file_path=export_path)
# Return the resulting DataFrame
return result
[docs]
def label_outliers(
df: Union[CytoDataFrame, pd.DataFrame, str],
feature_thresholds: Optional[Union[Dict[str, float], str]] = None,
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
include_threshold_scores: bool = False,
export_path: Optional[str] = None,
) -> CytoDataFrame:
"""
Use identify_outliers to label the original dataset for
where a cell passed or failed the quality control condition(s).
Args:
df: Union[CytoDataFrame, pd.DataFrame, str]
DataFrame or file string-based filepath of a
Parquet, CSV, or TSV file with CytoTable output or similar data.
feature_thresholds: Dict[str, float]
One of two options:
A dictionary with the feature name(s) as the key(s) and their assigned
threshold for identifying outliers. Positive int for the threshold
will detect outliers "above" than the mean, negative int will detect
outliers "below" the mean.
Or a string which is a named key reference found within
the feature_thresholds_file yaml file.
feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE,
An optional feature thresholds file where thresholds may be
defined within a file.
include_threshold_scores: bool = False
Whether to include the scores in addition to whether an outlier
was detected or not.
export_path: Optional[str] = None
An optional path to export the data using CytoDataFrame export
capabilities. If None no export is performed.
Note: compatible exports are CSV's, TSV's, and parquet.
Returns:
CytoDataFrame:
Full dataframe with optional scores and outlier boolean column.
"""
# interpret the df as CytoDataFrame
if not isinstance(df, CytoDataFrame):
df = CytoDataFrame(data=df)
# store the custom attributes
custom_attrs = dict(df._custom_attrs)
# for single outlier processing
if isinstance(feature_thresholds, (str, dict)):
# return the outlier dataframe for one threshold rule
identified_outliers = identify_outliers(
df=df,
feature_thresholds=feature_thresholds,
feature_thresholds_file=feature_thresholds_file,
include_threshold_scores=include_threshold_scores,
)
result = CytoDataFrame(
data=pd.concat(
[
df,
(
identified_outliers
if isinstance(identified_outliers, pd.DataFrame)
else CytoDataFrame(
{
(
f"cqc.{feature_thresholds}.is_outlier"
if isinstance(feature_thresholds, str)
else "cqc.custom.is_outlier"
): identified_outliers
}
)
),
],
axis=1,
),
# reuse the custom attributes
**custom_attrs,
)
# for multiple outlier processing
elif feature_thresholds is None:
# return the outlier dataframe for all threshold rules
labeled_df = pd.concat(
[df]
+ [
# identify outliers for each threshold rule
identify_outliers(
df=df,
feature_thresholds=thresholds,
feature_thresholds_file=feature_thresholds_file,
include_threshold_scores=include_threshold_scores,
)
# loop through each threshold rule
for thresholds in read_thresholds_set_from_file(
feature_thresholds_file=feature_thresholds_file,
)
],
axis=1,
)
# return a dataframe with deduplicated columns by name
result = CytoDataFrame(
labeled_df.loc[:, ~labeled_df.columns.duplicated()],
# reuse the custom attributes
**custom_attrs,
)
# export the file if specified
if export_path is not None:
result.export(file_path=export_path)
return result
[docs]
def read_thresholds_set_from_file(
feature_thresholds_file: str, feature_thresholds: Optional[str] = None
) -> Union[Dict[str, int], Dict[str, Dict[str, int]]]:
"""
Reads a set of feature thresholds from a specified file.
This function takes the path to a feature thresholds file and a
specific feature threshold string, reads the file, and returns
the thresholds set from the file.
Args:
feature_thresholds_file (str):
The path to the file containing feature thresholds.
feature_thresholds (Optional str, default None):
A string specifying the feature thresholds.
If we have None, return all thresholds.
Returns:
dict: A dictionary containing the processed feature thresholds.
Raises:
LookupError: If the file does not contain the specified feature_thresholds key.
"""
# open the yaml file
with open(feature_thresholds_file, "r") as file:
thresholds = yaml.safe_load(file)
# if no feature thresholds name is specified, return all thresholds
if feature_thresholds is None:
return thresholds["thresholds"]
if feature_thresholds not in thresholds["thresholds"]:
raise LookupError(
(
f"Unable to find threshold set by name {feature_thresholds}"
f" within {feature_thresholds_file}"
)
)
return thresholds["thresholds"][feature_thresholds]
coSMicQC