From b63f2db37e3ca822866e1030fcb15e21579f8916 Mon Sep 17 00:00:00 2001 From: d33bs Date: Mon, 20 May 2024 10:05:44 -0600 Subject: [PATCH 1/8] add yaml threshold input for find_outliers --- poetry.lock | 62 ++++++++++++- pyproject.toml | 1 + src/cosmicqc/analyze.py | 69 +++++++++++++-- src/cosmicqc/data/qc_thresholds_default.yml | 16 ++++ tests/test_analyze.py | 97 +++++++++++++++++++++ 5 files changed, 239 insertions(+), 6 deletions(-) create mode 100644 src/cosmicqc/data/qc_thresholds_default.yml diff --git a/poetry.lock b/poetry.lock index 59a6c67..bd4dc3d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -275,6 +275,66 @@ files = [ {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, ] +[[package]] +name = "pyyaml" +version = "6.0.1" +description = "YAML parser and emitter for Python" +optional = false +python-versions = ">=3.6" +files = [ + {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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a/pyproject.toml b/pyproject.toml index 8f59353..6a73b0b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ python = ">=3.9,<3.13" pandas = "^2.2.2" scipy = "^1.13.0" pyarrow = "^16.0.0" +pyyaml = "^6.0.1" [tool.poetry.group.dev.dependencies] pytest = "^8.2.0" diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index 759027b..b5172ce 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -3,15 +3,24 @@ """ import operator +import pathlib from functools import reduce -from typing import Dict, List +from typing import Dict, List, Optional, Union import pandas as pd +import yaml from scipy.stats import zscore as scipy_zscore +DEFAULT_QC_THRESHOLD_FILE = ( + f"{pathlib.Path(__file__).parent!s}/data/qc_thresholds_default.yml" +) + def find_outliers( - df: pd.DataFrame, feature_thresholds: Dict[str, float], metadata_columns: List[str] + df: pd.DataFrame, + metadata_columns: List[str], + feature_thresholds: Union[Dict[str, float], str], + feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, ) -> pd.DataFrame: """ This function uses z-scoring to format the data for detecting outlier @@ -23,18 +32,31 @@ def find_outliers( Args: df: pd.DataFrame Data frame with converted output from CytoTable. + metadata_columns: List[str] + List of metadata columns that should be outputted with the outlier data. feature_thresholds: Dict[str, float] - Dictionary with the feature name(s) as the key(s) and their assigned + 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. - metadata_columns: List[str] - List of metadata columns that should be outputted with the outlier data. + 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. Returns: pd.DataFrame: Outlier data frame for the given conditions. """ + + if isinstance(feature_thresholds, str): + feature_thresholds = read_thresholds_set_from_file( + feature_thresholds=feature_thresholds, + feature_thresholds_file=feature_thresholds_file, + ) + # Create z-score columns for each feature to reference during outlier detection zscore_columns = {} for feature in feature_thresholds: @@ -71,3 +93,40 @@ def find_outliers( # Return outliers DataFrame with specified columns return outliers_df[columns_to_include] + + +def read_thresholds_set_from_file( + feature_thresholds: str, feature_thresholds_file: str +): + """ + 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 (str): + A string specifying the feature thresholds. + feature_thresholds_file (str): + The path to the file containing feature thresholds. + + Returns: + dict: A dictionary containing the processed feature thresholds. + + Raises: + LookupError: If the file does not contain the specified feature_thresholds key. + """ + + with open(feature_thresholds_file, "r") as file: + thresholds = yaml.safe_load(file) + + 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] diff --git a/src/cosmicqc/data/qc_thresholds_default.yml b/src/cosmicqc/data/qc_thresholds_default.yml new file mode 100644 index 0000000..b312018 --- /dev/null +++ b/src/cosmicqc/data/qc_thresholds_default.yml @@ -0,0 +1,16 @@ +# defines threshold sets for running qc procedures as part of this project. +versions: + cellprofiler: ">=4.2.4" +thresholds: + # Set a negative threshold to identify both outlier small nuclei + # and low formfactor representing non-circular segmentations. + small_and_low_formfactor_nuclei: + Nuclei_AreaShape_Area: -1 + Nuclei_AreaShape_FormFactor: -1 + # find very elongated nuclei segmentations (above mean) + elongated_nuclei: + Nuclei_AreaShape_Eccentricity: 2 + # find large nuclei segmentations (above mean) and low formfactor + large_nuclei: + Nuclei_AreaShape_Area: 2 + Nuclei_AreaShape_FormFactor: -2 diff --git a/tests/test_analyze.py b/tests/test_analyze.py index 93103e1..49f949b 100644 --- a/tests/test_analyze.py +++ b/tests/test_analyze.py @@ -3,6 +3,7 @@ """ import pandas as pd +import pytest from cosmicqc import analyze @@ -183,3 +184,99 @@ def test_find_outliers_cfret(cytotable_CFReT_data_df: pd.DataFrame): 14811: "f01", }, } + + +def test_read_thresholds_set_from_file(): + """ + Tests read_thresholds_set_from_file + """ + + # test that an exception is raised on receiving a bad + # lookup value from the thresholds file. + with pytest.raises(LookupError): + analyze.read_thresholds_set_from_file( + feature_thresholds="bad_lookup_value", + feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, + ) + + # test default threshold sets + assert analyze.read_thresholds_set_from_file( + feature_thresholds="small_and_low_formfactor_nuclei", + feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, + ) == {"Nuclei_AreaShape_Area": -1, "Nuclei_AreaShape_FormFactor": -1} + + assert analyze.read_thresholds_set_from_file( + feature_thresholds="elongated_nuclei", + feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, + ) == {"Nuclei_AreaShape_Eccentricity": 2} + + assert analyze.read_thresholds_set_from_file( + feature_thresholds="large_nuclei", + feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, + ) == {"Nuclei_AreaShape_Area": 2, "Nuclei_AreaShape_FormFactor": -2} + + +def test_find_outliers_dict_and_default_config_cfret( + cytotable_CFReT_data_df: pd.DataFrame, +): + """ + Testing find_outliers with dictionary vs yaml threshold sets + using CytoTable CFReT data. + """ + + # metadata columns to include in output data frame + metadata_columns = [ + "Image_Metadata_Plate", + "Image_Metadata_Well", + "Image_Metadata_Site", + ] + + # test that the output is the same from dict vs yaml + pd.testing.assert_frame_equal( + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds={ + "Nuclei_AreaShape_Area": -1, + "Nuclei_AreaShape_FormFactor": -1, + }, + metadata_columns=metadata_columns, + ), + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds="small_and_low_formfactor_nuclei", + metadata_columns=metadata_columns, + ), + ) + + # test that the output is the same from dict vs yaml + pd.testing.assert_frame_equal( + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds={ + "Nuclei_AreaShape_Eccentricity": 2, + }, + metadata_columns=metadata_columns, + ), + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds="elongated_nuclei", + metadata_columns=metadata_columns, + ), + ) + + # test that the output is the same from dict vs yaml + pd.testing.assert_frame_equal( + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds={ + "Nuclei_AreaShape_Area": 2, + "Nuclei_AreaShape_FormFactor": -2, + }, + metadata_columns=metadata_columns, + ), + analyze.find_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds="large_nuclei", + metadata_columns=metadata_columns, + ), + ) From 5725f0bb4dd1139100912241ad85fd0e6fa93571 Mon Sep 17 00:00:00 2001 From: d33bs Date: Mon, 20 May 2024 16:35:53 -0600 Subject: [PATCH 2/8] rename file for compartment specificity Co-Authored-By: Jenna Tomkinson <107513215+jenna-tomkinson@users.noreply.github.com> --- src/cosmicqc/analyze.py | 2 +- ..._thresholds_default.yml => qc_nuclei_thresholds_default.yml} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename src/cosmicqc/data/{qc_thresholds_default.yml => qc_nuclei_thresholds_default.yml} (100%) diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index b5172ce..0c41cdd 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -12,7 +12,7 @@ from scipy.stats import zscore as scipy_zscore DEFAULT_QC_THRESHOLD_FILE = ( - f"{pathlib.Path(__file__).parent!s}/data/qc_thresholds_default.yml" + f"{pathlib.Path(__file__).parent!s}/data/qc_nuclei_thresholds_default.yml" ) diff --git a/src/cosmicqc/data/qc_thresholds_default.yml b/src/cosmicqc/data/qc_nuclei_thresholds_default.yml similarity index 100% rename from src/cosmicqc/data/qc_thresholds_default.yml rename to src/cosmicqc/data/qc_nuclei_thresholds_default.yml From 974ea86e642419d19aad75dfc6e57864e72b3f30 Mon Sep 17 00:00:00 2001 From: d33bs Date: Tue, 21 May 2024 16:52:57 -0600 Subject: [PATCH 3/8] add labeled outlier dataframe output capability --- src/cosmicqc/analyze.py | 167 ++++++++++++++++-- tests/conftest.py | 8 + .../test_identifier_outliers_output.parquet | Bin 0 -> 17106 bytes .../test_label_outliers_output.parquet | Bin 0 -> 11795441 bytes tests/test_analyze.py | 158 +++++++++++++++++ 5 files changed, 319 insertions(+), 14 deletions(-) create mode 100644 tests/data/coSMicQC/test_identifier_outliers_output.parquet create mode 100644 tests/data/coSMicQC/test_label_outliers_output.parquet diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index 0c41cdd..7078582 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -16,12 +16,12 @@ ) -def find_outliers( +def identify_outliers( df: pd.DataFrame, - metadata_columns: List[str], feature_thresholds: Union[Dict[str, float], str], feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, -) -> pd.DataFrame: + include_threshold_scores: bool = False, +) -> pd.Series: """ This function uses z-scoring to format the data for detecting outlier nuclei or cells using specific CellProfiler features. Thresholds are @@ -32,8 +32,6 @@ def find_outliers( Args: df: pd.DataFrame Data frame with converted output from CytoTable. - 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 @@ -47,10 +45,19 @@ def find_outliers( defined within a file. Returns: - pd.DataFrame: - Outlier data frame for the given conditions. + pd.Series, df: + Outlier series with booleans based on whether outliers were detected + or not for use within other functions. """ + outlier_df = df.copy() + + thresholds_name = ( + f"outlier_{feature_thresholds}" + if isinstance(feature_thresholds, str) + else "outlier_custom" + ) + if isinstance(feature_thresholds, str): feature_thresholds = read_thresholds_set_from_file( feature_thresholds=feature_thresholds, @@ -62,7 +69,7 @@ def find_outliers( for feature in feature_thresholds: if feature not in df.columns: raise ValueError(f"Feature '{feature}' does not exist in the DataFrame.") - df[f"Z_Score_{feature}"] = scipy_zscore(df[feature]) + outlier_df[f"Z_Score_{feature}"] = scipy_zscore(df[feature]) zscore_columns[feature] = f"Z_Score_{feature}" # Create outlier detection conditions for each feature @@ -71,15 +78,76 @@ def find_outliers( # For positive thresholds, look for outliers that are # that number of std "above" the mean if threshold > 0: - condition = df[zscore_columns[feature]] > threshold + condition = outlier_df[zscore_columns[feature]] > threshold # For negative thresholds, look for outliers that are # that number of std "below" the mean else: - condition = df[zscore_columns[feature]] < threshold + condition = outlier_df[zscore_columns[feature]] < threshold conditions.append(condition) + # create a boolean pd.series identifier for dataframe + # based on all conditions for use within other functions. + + return ( + reduce(operator.and_, conditions) + if not include_threshold_scores + else pd.concat( + [ + outlier_df[zscore_columns.values()], + pd.DataFrame({thresholds_name: reduce(operator.and_, conditions)}), + ], + axis=1, + ) + ) + + +def find_outliers( + df: pd.DataFrame, + metadata_columns: List[str], + feature_thresholds: Union[Dict[str, float], str], + feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, +) -> pd.DataFrame: + """ + This function uses identify_outliers to return a dataframe + with only the outliers and provided metadata columns. + + Args: + df: pd.DataFrame + Data frame with converted output from CytoTable. + 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. + + Returns: + pd.DataFrame: + Outlier data frame for the given conditions. + """ + + if isinstance(feature_thresholds, str): + feature_thresholds = read_thresholds_set_from_file( + feature_thresholds=feature_thresholds, + feature_thresholds_file=feature_thresholds_file, + ) + # Filter DataFrame for outliers using all conditions - outliers_df = df[reduce(operator.and_, conditions)] + outliers_df = df[ + # use identify outliers as a mask on the full dataframe + identify_outliers( + df=df, + feature_thresholds=feature_thresholds, + feature_thresholds_file=feature_thresholds_file, + ) + ] # Print outliers count and range for each feature print("Number of outliers:", outliers_df.shape[0]) @@ -95,8 +163,75 @@ def find_outliers( return outliers_df[columns_to_include] +def label_outliers( + df: pd.DataFrame, + feature_thresholds: Optional[Union[Dict[str, float], str]] = None, + feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, +) -> pd.Series: + """ + 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: pd.DataFrame + Data frame with converted output from CytoTable. + 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. + + Returns: + pd.Series: + Outlier series with booleans based on whether outliers were detected + or not for use within other functions. + """ + + # for single outlier processing + if isinstance(feature_thresholds, (str, dict)): + return pd.concat( + [ + df, + identify_outliers( + df=df, + feature_thresholds=feature_thresholds, + feature_thresholds_file=feature_thresholds_file, + include_threshold_scores=True, + ), + ], + axis=1, + ) + + elif feature_thresholds is None: + labeled_df = pd.concat( + [df] + + [ + identify_outliers( + df=df, + feature_thresholds=thresholds, + feature_thresholds_file=feature_thresholds_file, + include_threshold_scores=True, + ) + for thresholds in read_thresholds_set_from_file( + feature_thresholds_file=feature_thresholds_file, + ) + ], + axis=1, + ) + return labeled_df.loc[:, ~labeled_df.columns.duplicated()] + + def read_thresholds_set_from_file( - feature_thresholds: str, feature_thresholds_file: str + feature_thresholds_file: str, feature_thresholds: Optional[str] = None ): """ Reads a set of feature thresholds from a specified file. @@ -106,10 +241,11 @@ def read_thresholds_set_from_file( the thresholds set from the file. Args: - feature_thresholds (str): - A string specifying the feature thresholds. 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. @@ -121,6 +257,9 @@ def read_thresholds_set_from_file( with open(feature_thresholds_file, "r") as file: thresholds = yaml.safe_load(file) + if feature_thresholds is None: + return thresholds["thresholds"] + if feature_thresholds not in thresholds["thresholds"]: raise LookupError( ( diff --git a/tests/conftest.py b/tests/conftest.py index 3adc367..a8f796a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -16,3 +16,11 @@ def fixture_cytotable_CFReT_df(): return pd.read_parquet( "tests/data/cytotable/CFRet_data/test_localhost231120090001_converted.parquet" ) + + +@pytest.fixture(name="basic_outlier_dataframe") +def fixture_basic_outlier_dataframe(): + """ + Creates basic example data for use in tests + """ + return pd.DataFrame({"example_feature": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}) diff --git a/tests/data/coSMicQC/test_identifier_outliers_output.parquet 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z+~5924{n$Ax$psI%p`ppZ%U+3OJm8uif4Q)RG+yN5O3JWh+M}%Z%F=ye1z{ra{Jw| z;FIwb3W`EYg77ackUOvSNPlbk9oG06-&%jW#`igH3i^zaY8rf(12NY{`b4Lx4+*`r{mARecVrHPvbv-%OCwC_mABF{QF=3@xOol z_49u}7WlEij|F}#@MD1=3;bB%#{xeV__4r`1%52>V}TzF{8-?}0zVe`vA~Z7ek|}~ WfgcO}f3Uz`|N77W`m4>(^Zy5@sT=wL literal 0 HcmV?d00001 diff --git a/tests/test_analyze.py b/tests/test_analyze.py index 49f949b..bd19ac6 100644 --- a/tests/test_analyze.py +++ b/tests/test_analyze.py @@ -7,6 +7,26 @@ from cosmicqc import analyze +def test_find_outliers_basic(basic_outlier_dataframe: pd.DataFrame): + """ + Testing find_outliers with basic/simulated data. + """ + + # add metadata to basic data + metadata_column_name = "Image_Metadata_Plate" + basic_outlier_dataframe[metadata_column_name] = "A" + + # assert that we have the output we expect + assert analyze.find_outliers( + df=basic_outlier_dataframe, + feature_thresholds={"example_feature": 1}, + metadata_columns=[metadata_column_name], + ).to_dict(orient="dict") == { + "example_feature": {8: 9, 9: 10}, + "Image_Metadata_Plate": {8: "A", 9: "A"}, + } + + def test_find_outliers_cfret(cytotable_CFReT_data_df: pd.DataFrame): """ Testing find_outliers with CytoTable CFReT data. @@ -215,6 +235,17 @@ def test_read_thresholds_set_from_file(): feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, ) == {"Nuclei_AreaShape_Area": 2, "Nuclei_AreaShape_FormFactor": -2} + assert analyze.read_thresholds_set_from_file( + feature_thresholds_file=analyze.DEFAULT_QC_THRESHOLD_FILE, + ) == { + "small_and_low_formfactor_nuclei": { + "Nuclei_AreaShape_Area": -1, + "Nuclei_AreaShape_FormFactor": -1, + }, + "elongated_nuclei": {"Nuclei_AreaShape_Eccentricity": 2}, + "large_nuclei": {"Nuclei_AreaShape_Area": 2, "Nuclei_AreaShape_FormFactor": -2}, + } + def test_find_outliers_dict_and_default_config_cfret( cytotable_CFReT_data_df: pd.DataFrame, @@ -280,3 +311,130 @@ def test_find_outliers_dict_and_default_config_cfret( metadata_columns=metadata_columns, ), ) + + +def test_label_outliers( + basic_outlier_dataframe: pd.DataFrame, + cytotable_CFReT_data_df: pd.DataFrame, +): + # test basic single-column result + assert analyze.label_outliers( + df=basic_outlier_dataframe, feature_thresholds={"example_feature": 1} + ).to_dict(orient="dict") == { + "example_feature": { + 0: 1, + 1: 2, + 2: 3, + 3: 4, + 4: 5, + 5: 6, + 6: 7, + 7: 8, + 8: 9, + 9: 10, + }, + "Z_Score_example_feature": { + 0: -1.5666989036012806, + 1: -1.2185435916898848, + 2: -0.8703882797784892, + 3: -0.5222329678670935, + 4: -0.17407765595569785, + 5: 0.17407765595569785, + 6: 0.5222329678670935, + 7: 0.8703882797784892, + 8: 1.2185435916898848, + 9: 1.5666989036012806, + }, + "outlier_custom": { + 0: False, + 1: False, + 2: False, + 3: False, + 4: False, + 5: False, + 6: False, + 7: False, + 8: True, + 9: True, + }, + } + + # test single-column result + test_df = analyze.label_outliers( + df=cytotable_CFReT_data_df, feature_thresholds="large_nuclei" + ) + pd.testing.assert_frame_equal( + test_df, + pd.read_parquet( + path="tests/data/coSMicQC/test_label_outliers_output.parquet", + columns=test_df.columns.tolist(), + ), + ) + + # test full dataset + pd.testing.assert_frame_equal( + analyze.label_outliers(df=cytotable_CFReT_data_df), + pd.read_parquet(path="tests/data/coSMicQC/test_label_outliers_output.parquet"), + ) + + +def test_identify_outliers( + basic_outlier_dataframe: pd.DataFrame, + cytotable_CFReT_data_df: pd.DataFrame, +): + """ + Tests identify_outliers + """ + + assert analyze.identify_outliers( + df=basic_outlier_dataframe, + feature_thresholds={"example_feature": 1}, + include_threshold_scores=True, + ).to_dict(orient="dict") == { + "Z_Score_example_feature": { + 0: -1.5666989036012806, + 1: -1.2185435916898848, + 2: -0.8703882797784892, + 3: -0.5222329678670935, + 4: -0.17407765595569785, + 5: 0.17407765595569785, + 6: 0.5222329678670935, + 7: 0.8703882797784892, + 8: 1.2185435916898848, + 9: 1.5666989036012806, + }, + "outlier_custom": { + 0: False, + 1: False, + 2: False, + 3: False, + 4: False, + 5: False, + 6: False, + 7: False, + 8: True, + 9: True, + }, + } + + pd.testing.assert_frame_equal( + analyze.identify_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds="large_nuclei", + include_threshold_scores=True, + ), + pd.read_parquet("tests/data/coSMicQC/test_identifier_outliers_output.parquet"), + ) + + identified_df = analyze.identify_outliers( + df=cytotable_CFReT_data_df, + feature_thresholds="large_nuclei", + ) + pd.testing.assert_series_equal( + identified_df, + pd.read_parquet( + "tests/data/coSMicQC/test_identifier_outliers_output.parquet", + columns=["outlier_large_nuclei"], + )["outlier_large_nuclei"], + check_names=False, + ) From 679770081713268d9d798934c5771100e07a7ef9 Mon Sep 17 00:00:00 2001 From: d33bs Date: Wed, 22 May 2024 15:17:20 -0600 Subject: [PATCH 4/8] add documentation and typing --- pyproject.toml | 6 +++++- src/cosmicqc/analyze.py | 40 +++++++++++++++++++++++++++------------- 2 files changed, 32 insertions(+), 14 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6a73b0b..64a5088 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,12 +47,16 @@ select = [ # flake8-comprehensions "C4", # flake8-simplify - "SIM" + "SIM", + # flake8-annotations + "ANN" ] [tool.ruff.lint.per-file-ignores] # Ignore `E402` and `F401` (unused imports) in all `__init__.py` files "__init__.py" = ["E402", "F401"] +# ignore typing rules for tests +"tests/*" = ["ANN201"] # set dynamic versioning capabilities for project [tool.poetry-dynamic-versioning] diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index 1d73a42..51e4d2e 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -21,7 +21,7 @@ def identify_outliers( feature_thresholds: Union[Dict[str, float], str], feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, include_threshold_scores: bool = False, -) -> pd.Series: +) -> Union[pd.Series, pd.DataFrame]: """ This function uses z-scoring to format the data for detecting outlier nuclei or cells using specific CellProfiler features. Thresholds are @@ -47,12 +47,14 @@ def identify_outliers( defined within a file. Returns: - pd.Series, df: + Union[pd.Series, pd.DataFrame]: Outlier series with booleans based on whether outliers were detected or not for use within other functions. """ - outlier_df = df + # create a copy of the dataframe to ensure + # we don't modify the supplied dataframe inplace. + outlier_df = df.copy() thresholds_name = ( f"outlier_{feature_thresholds}" @@ -87,14 +89,15 @@ def identify_outliers( condition = outlier_df[zscore_columns[feature]] < threshold conditions.append(condition) - # create a boolean pd.series identifier for dataframe - # based on all conditions for use within other functions. - return ( + # create a boolean pd.series identifier for dataframe + # based on all conditions for use within other functions. reduce(operator.and_, conditions) if not include_threshold_scores + # otherwise, provide the threshold zscore col and the above column else pd.concat( [ + # grab only the outlier zscore columns from the outlier_df outlier_df[zscore_columns.values()], pd.DataFrame({thresholds_name: reduce(operator.and_, conditions)}), ], @@ -169,7 +172,8 @@ def label_outliers( df: pd.DataFrame, feature_thresholds: Optional[Union[Dict[str, float], str]] = None, feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, -) -> pd.Series: + include_threshold_scores: bool = True, +) -> pd.DataFrame: """ This function uses z-scoring to format the data for detecting outlier nuclei or cells using specific CellProfiler features. Thresholds are @@ -191,15 +195,18 @@ def label_outliers( 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 = True + Whether to include the scores in addition to whether an outlier + was detected or not. Returns: - pd.Series: - Outlier series with booleans based on whether outliers were detected - or not for use within other functions. + pd.DataFrame: + Full dataframe with optional scores and outlier boolean column. """ # for single outlier processing if isinstance(feature_thresholds, (str, dict)): + # return the outlier dataframe for one threshold rule return pd.concat( [ df, @@ -207,34 +214,39 @@ def label_outliers( df=df, feature_thresholds=feature_thresholds, feature_thresholds_file=feature_thresholds_file, - include_threshold_scores=True, + include_threshold_scores=include_threshold_scores, ), ], axis=1, ) + # 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=True, + 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 a deduplicated columns by name return labeled_df.loc[:, ~labeled_df.columns.duplicated()] 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. @@ -256,9 +268,11 @@ def read_thresholds_set_from_file( 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"] From 4c2e13853fc62efce64ad6d2b5e153717874b290 Mon Sep 17 00:00:00 2001 From: Dave Bunten Date: Wed, 29 May 2024 10:37:12 -0600 Subject: [PATCH 5/8] Update src/cosmicqc/analyze.py Co-authored-by: Jenna Tomkinson <107513215+jenna-tomkinson@users.noreply.github.com> --- src/cosmicqc/analyze.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index 51e4d2e..911fb55 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -175,11 +175,7 @@ def label_outliers( include_threshold_scores: bool = True, ) -> pd.DataFrame: """ - 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. +Use identify_outliers to label the original dataset for where a cell passed or failed the quality control condition(s). Args: df: pd.DataFrame From 7dbe98915423c141a456cc536c472544827c6234 Mon Sep 17 00:00:00 2001 From: d33bs Date: Wed, 29 May 2024 10:55:54 -0600 Subject: [PATCH 6/8] linting --- src/cosmicqc/analyze.py | 47 +++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index 911fb55..d1e18f8 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -175,29 +175,30 @@ def label_outliers( include_threshold_scores: bool = True, ) -> pd.DataFrame: """ -Use identify_outliers to label the original dataset for where a cell passed or failed the quality control condition(s). - - Args: - df: pd.DataFrame - Data frame with converted output from CytoTable. - 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 = True - Whether to include the scores in addition to whether an outlier - was detected or not. - - Returns: - pd.DataFrame: - Full dataframe with optional scores and outlier boolean column. + Use identify_outliers to label the original dataset for + where a cell passed or failed the quality control condition(s). + + Args: + df: pd.DataFrame + Data frame with converted output from CytoTable. + 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 = True + Whether to include the scores in addition to whether an outlier + was detected or not. + + Returns: + pd.DataFrame: + Full dataframe with optional scores and outlier boolean column. """ # for single outlier processing From fba2853a09f4e8bf13abd4793186d4f303b353dc Mon Sep 17 00:00:00 2001 From: d33bs Date: Thu, 30 May 2024 20:01:52 -0600 Subject: [PATCH 7/8] change default, add test, correct label_outliers --- src/cosmicqc/analyze.py | 27 ++++++++++++++++------ tests/test_analyze.py | 50 +++++++++++++++++++++++++++++++++++++---- 2 files changed, 66 insertions(+), 11 deletions(-) diff --git a/src/cosmicqc/analyze.py b/src/cosmicqc/analyze.py index d1e18f8..9f33c4b 100644 --- a/src/cosmicqc/analyze.py +++ b/src/cosmicqc/analyze.py @@ -172,7 +172,7 @@ def label_outliers( df: pd.DataFrame, feature_thresholds: Optional[Union[Dict[str, float], str]] = None, feature_thresholds_file: Optional[str] = DEFAULT_QC_THRESHOLD_FILE, - include_threshold_scores: bool = True, + include_threshold_scores: bool = False, ) -> pd.DataFrame: """ Use identify_outliers to label the original dataset for @@ -192,7 +192,7 @@ def label_outliers( 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 = True + include_threshold_scores: bool = False Whether to include the scores in addition to whether an outlier was detected or not. @@ -204,14 +204,27 @@ def label_outliers( # 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, + ) return pd.concat( [ df, - identify_outliers( - df=df, - feature_thresholds=feature_thresholds, - feature_thresholds_file=feature_thresholds_file, - include_threshold_scores=include_threshold_scores, + ( + identified_outliers + if isinstance(identified_outliers, pd.DataFrame) + else pd.DataFrame( + { + ( + f"outlier_{feature_thresholds}" + if isinstance(feature_thresholds, str) + else "outlier_custom" + ): identified_outliers + } + ) ), ], axis=1, diff --git a/tests/test_analyze.py b/tests/test_analyze.py index bd19ac6..6c7a879 100644 --- a/tests/test_analyze.py +++ b/tests/test_analyze.py @@ -317,9 +317,15 @@ def test_label_outliers( basic_outlier_dataframe: pd.DataFrame, cytotable_CFReT_data_df: pd.DataFrame, ): - # test basic single-column result + """ + Tests label_outliers + """ + + # test basic single-column result with zscores assert analyze.label_outliers( - df=basic_outlier_dataframe, feature_thresholds={"example_feature": 1} + df=basic_outlier_dataframe, + feature_thresholds={"example_feature": 1}, + include_threshold_scores=True, ).to_dict(orient="dict") == { "example_feature": { 0: 1, @@ -359,9 +365,43 @@ def test_label_outliers( }, } + # test for case when zscores are excluded + assert analyze.label_outliers( + df=basic_outlier_dataframe, + feature_thresholds={"example_feature": 1}, + include_threshold_scores=False, + ).to_dict(orient="dict") == { + "example_feature": { + 0: 1, + 1: 2, + 2: 3, + 3: 4, + 4: 5, + 5: 6, + 6: 7, + 7: 8, + 8: 9, + 9: 10, + }, + "outlier_custom": { + 0: False, + 1: False, + 2: False, + 3: False, + 4: False, + 5: False, + 6: False, + 7: False, + 8: True, + 9: True, + }, + } + # test single-column result test_df = analyze.label_outliers( - df=cytotable_CFReT_data_df, feature_thresholds="large_nuclei" + df=cytotable_CFReT_data_df, + feature_thresholds="large_nuclei", + include_threshold_scores=True, ) pd.testing.assert_frame_equal( test_df, @@ -373,7 +413,9 @@ def test_label_outliers( # test full dataset pd.testing.assert_frame_equal( - analyze.label_outliers(df=cytotable_CFReT_data_df), + analyze.label_outliers( + df=cytotable_CFReT_data_df, include_threshold_scores=True + ), pd.read_parquet(path="tests/data/coSMicQC/test_label_outliers_output.parquet"), ) From c20789f5a2a37dbd487067a17f9f720a28834a2e Mon Sep 17 00:00:00 2001 From: d33bs Date: Thu, 30 May 2024 20:03:02 -0600 Subject: [PATCH 8/8] remove 3.8 from tests --- .github/workflows/run-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index fd9940e..e2a1b16 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -23,7 +23,7 @@ jobs: run_tests: strategy: matrix: - python_version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python_version: ["3.9", "3.10", "3.11", "3.12"] os: [ubuntu-22.04, macos-13] runs-on: ${{ matrix.os }} env:

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