diff --git a/malariagen_data/anoph/sample_metadata.py b/malariagen_data/anoph/sample_metadata.py index 520c45a34..74b4f9def 100644 --- a/malariagen_data/anoph/sample_metadata.py +++ b/malariagen_data/anoph/sample_metadata.py @@ -1,6 +1,6 @@ import io from itertools import cycle -from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union +from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union import ipyleaflet # type: ignore import numpy as np @@ -54,7 +54,39 @@ def __init__( # Initialize cache attributes. self._cache_sample_metadata: Dict = dict() - def _general_metadata_paths(self, *, sample_sets: List[str]) -> Dict[str, str]: + def _parse_metadata_paths( + self, + metadata_paths_func: Callable[[List[str]], Dict[str, str]], + parse_metadata_func: Callable[[str, Union[bytes, Exception]], pd.DataFrame], + sample_sets: Optional[base_params.sample_sets] = None, + ) -> pd.DataFrame: + # Normalise input parameters. + sample_sets_prepped = self._prep_sample_sets_param(sample_sets=sample_sets) + del sample_sets + + # Obtain paths for all files we need to fetch. + file_paths: Mapping[str, str] = metadata_paths_func(sample_sets_prepped) + + # Fetch all files. N.B., here is an optimisation, this allows us to fetch + # multiple files concurrently. + files: Mapping[str, Union[bytes, Exception]] = self.read_files( + paths=file_paths.values(), on_error="return" + ) + + # Parse files into DataFrames. + dfs = [] + for sample_set in sample_sets_prepped: + path = file_paths[sample_set] + data = files[path] + df = parse_metadata_func(sample_set, data) + dfs.append(df) + + # Concatenate all DataFrames. + df_ret = pd.concat(dfs, axis=0, ignore_index=True) + + return df_ret + + def _general_metadata_paths(self, sample_sets: List[str]) -> Dict[str, str]: paths = dict() for sample_set in sample_sets: release = self.lookup_release(sample_set=sample_set) @@ -64,7 +96,7 @@ def _general_metadata_paths(self, *, sample_sets: List[str]) -> Dict[str, str]: return paths def _parse_general_metadata( - self, *, sample_set: str, data: Union[bytes, Exception] + self, sample_set: str, data: Union[bytes, Exception] ) -> pd.DataFrame: if isinstance(data, bytes): dtype = { @@ -117,33 +149,105 @@ def _parse_general_metadata( def general_metadata( self, sample_sets: Optional[base_params.sample_sets] = None ) -> pd.DataFrame: - # Normalise input parameters. - sample_sets_prepped = self._prep_sample_sets_param(sample_sets=sample_sets) - del sample_sets - - # Obtain paths for all files we need to fetch. - file_paths: Mapping[str, str] = self._general_metadata_paths( - sample_sets=sample_sets_prepped + return self._parse_metadata_paths( + metadata_paths_func=self._general_metadata_paths, + parse_metadata_func=self._parse_general_metadata, + sample_sets=sample_sets, ) - # Fetch all files. N.B., here is an optimisation, this allows us to fetch - # multiple files concurrently. - files: Mapping[str, Union[bytes, Exception]] = self.read_files( - paths=file_paths.values(), on_error="return" + def _sequence_qc_metadata_paths(self, sample_sets: List[str]) -> Dict[str, str]: + paths = dict() + for sample_set in sample_sets: + release = self.lookup_release(sample_set=sample_set) + release_path = self._release_to_path(release=release) + path = ( + f"{release_path}/metadata/curation/{sample_set}/sequence_qc_stats.csv" + ) + paths[sample_set] = path + return paths + + @property + def _sequence_qc_metadata_dtype(self): + # Note: tests expect an ordered dictionary. + # Note: insertion order in dictionary keys is guaranteed since Python 3.7 + # Note: using nullable dtypes (e.g. Int64 instead of int64) to allow missing data. + + dtype = { + "sample_id": "object", + "mean_cov": "Float64", + "median_cov": "Int64", + "modal_cov": "Int64", + } + + for contig in sorted(self.config["CONTIGS"]): + dtype[f"mean_cov_{contig}"] = "Float64" + dtype[f"median_cov_{contig}"] = "Int64" + dtype[f"mode_cov_{contig}"] = "Int64" + + dtype.update( + { + "frac_gen_cov": "Float64", + "divergence": "Float64", + "contam_pct": "Float64", + "contam_LLR": "Float64", + } ) - # Parse files into dataframes. - dfs = [] - for sample_set in sample_sets_prepped: - path = file_paths[sample_set] - data = files[path] - df = self._parse_general_metadata(sample_set=sample_set, data=data) - dfs.append(df) + return dtype - # Concatenate all dataframes. - df_ret = pd.concat(dfs, axis=0, ignore_index=True) + def _parse_sequence_qc_metadata( + self, sample_set: str, data: Union[bytes, Exception] + ) -> pd.DataFrame: + if isinstance(data, bytes): + # Get the dtype of the constant columns. + dtype = self._sequence_qc_metadata_dtype - return df_ret + # Read the CSV using the dtype dict. + df = pd.read_csv(io.BytesIO(data), dtype=dtype, na_values="") + + return df + + elif isinstance(data, FileNotFoundError): + # Sequence QC metadata are missing for this sample set, + # so return a blank DataFrame. + + # Copy the sample ids from the general metadata. + df_general = self.general_metadata(sample_sets=sample_set) + df = df_general[["sample_id"]].copy() + + # Add the sequence QC columns with appropriate missing values. + # For each column, set the value to either NA or NaN. + for c, dtype in self._sequence_qc_metadata_dtype.items(): + if pd.api.types.is_integer_dtype(dtype): + # Note: this creates a column with dtype int64. + df[c] = -1 + else: + # Note: this creates a column with dtype float64. + df[c] = np.nan + + # Set the column data types. + df = df.astype(self._sequence_qc_metadata_dtype) + + return df + + else: + raise data + + @check_types + @doc( + summary=""" + Access sequence QC metadata for one or more sample sets. + """, + returns="A pandas DataFrame, one row per sample.", + ) + def sequence_qc_metadata( + self, sample_sets: Optional[base_params.sample_sets] = None + ) -> pd.DataFrame: + return self._parse_metadata_paths( + metadata_paths_func=self._sequence_qc_metadata_paths, + parse_metadata_func=self._parse_sequence_qc_metadata, + sample_sets=sample_sets, + ) @property def _cohorts_analysis(self): @@ -154,7 +258,7 @@ def _cohorts_analysis(self): # config. return self.config.get("DEFAULT_COHORTS_ANALYSIS") - def _cohorts_metadata_paths(self, *, sample_sets: List[str]) -> Dict[str, str]: + def _cohorts_metadata_paths(self, sample_sets: List[str]) -> Dict[str, str]: cohorts_analysis = self._cohorts_analysis # Guard to ensure this function is only ever called if a cohort # analysis is configured for this data resource. @@ -212,7 +316,7 @@ def _cohorts_metadata_dtype(self): return dtype def _parse_cohorts_metadata( - self, *, sample_set: str, data: Union[bytes, Exception] + self, sample_set: str, data: Union[bytes, Exception] ) -> pd.DataFrame: if isinstance(data, bytes): # Parse CSV data. @@ -265,34 +369,12 @@ def cohorts_metadata( ) -> pd.DataFrame: self._require_cohorts_analysis() - # Normalise input parameters. - sample_sets_prepped = self._prep_sample_sets_param(sample_sets=sample_sets) - del sample_sets - - # Obtain paths for all files we need to fetch. - file_paths: Mapping[str, str] = self._cohorts_metadata_paths( - sample_sets=sample_sets_prepped - ) - - # Fetch all files. N.B., here is an optimisation, this allows us to fetch - # multiple files concurrently. - files: Mapping[str, Union[bytes, Exception]] = self.read_files( - paths=file_paths.values(), on_error="return" + return self._parse_metadata_paths( + metadata_paths_func=self._cohorts_metadata_paths, + parse_metadata_func=self._parse_cohorts_metadata, + sample_sets=sample_sets, ) - # Parse files into dataframes. - dfs = [] - for sample_set in sample_sets_prepped: - path = file_paths[sample_set] - data = files[path] - df = self._parse_cohorts_metadata(sample_set=sample_set, data=data) - dfs.append(df) - - # Concatenate all dataframes. - df_ret = pd.concat(dfs, axis=0, ignore_index=True) - - return df_ret - @property def _aim_analysis(self): if self._aim_analysis_override: @@ -302,7 +384,7 @@ def _aim_analysis(self): # config. return self.config.get("DEFAULT_AIM_ANALYSIS") - def _aim_metadata_paths(self, *, sample_sets: List[str]) -> Dict[str, str]: + def _aim_metadata_paths(self, sample_sets: List[str]) -> Dict[str, str]: aim_analysis = self._aim_analysis # Guard to ensure this function is only ever called if an AIM # analysis is configured for this data resource. @@ -316,7 +398,7 @@ def _aim_metadata_paths(self, *, sample_sets: List[str]) -> Dict[str, str]: return paths def _parse_aim_metadata( - self, *, sample_set: str, data: Union[bytes, Exception] + self, sample_set: str, data: Union[bytes, Exception] ) -> pd.DataFrame: assert self._aim_metadata_columns is not None assert self._aim_metadata_dtype is not None @@ -360,34 +442,12 @@ def aim_metadata( ) -> pd.DataFrame: self._require_aim_analysis() - # Normalise input parameters. - sample_sets_prepped = self._prep_sample_sets_param(sample_sets=sample_sets) - del sample_sets - - # Obtain paths for all files we need to fetch. - file_paths: Mapping[str, str] = self._aim_metadata_paths( - sample_sets=sample_sets_prepped - ) - - # Fetch all files. N.B., here is an optimisation, this allows us to fetch - # multiple files concurrently. - files: Mapping[str, Union[bytes, Exception]] = self.read_files( - paths=file_paths.values(), on_error="return" + return self._parse_metadata_paths( + metadata_paths_func=self._aim_metadata_paths, + parse_metadata_func=self._parse_aim_metadata, + sample_sets=sample_sets, ) - # Parse files into dataframes. - dfs = [] - for sample_set in sample_sets_prepped: - path = file_paths[sample_set] - data = files[path] - df = self._parse_aim_metadata(sample_set=sample_set, data=data) - dfs.append(df) - - # Concatenate all dataframes. - df_ret = pd.concat(dfs, axis=0, ignore_index=True) - - return df_ret - @check_types @doc( summary=""" @@ -465,13 +525,29 @@ def sample_metadata( except KeyError: with self._spinner(desc="Load sample metadata"): - # Build a dataframe from all available metadata. + ## Build a single DataFrame using all available metadata. + + # Get the general sample metadata. df_samples = self.general_metadata(sample_sets=prepped_sample_sets) + + # Merge with the sequence QC metadata. + df_sequence_qc = self.sequence_qc_metadata( + sample_sets=prepped_sample_sets + ) + + # Note: merging can change column dtypes + df_samples = df_samples.merge( + df_sequence_qc, on="sample_id", sort=False, how="left" + ) + + # If available, merge with the AIM metadata. if self._aim_analysis: df_aim = self.aim_metadata(sample_sets=prepped_sample_sets) df_samples = df_samples.merge( df_aim, on="sample_id", sort=False, how="left" ) + + # If available, merge with the cohorts metadata. if self._cohorts_analysis: df_cohorts = self.cohorts_metadata(sample_sets=prepped_sample_sets) df_samples = df_samples.merge( diff --git a/tests/anoph/conftest.py b/tests/anoph/conftest.py index 95f5eb006..06369b131 100644 --- a/tests/anoph/conftest.py +++ b/tests/anoph/conftest.py @@ -959,6 +959,7 @@ def __init__( releases: Tuple[str, ...], has_aims: bool, has_cohorts_by_quarter: bool, + has_sequence_qc: bool, ): self.fixture_dir = fixture_dir self.bucket = bucket @@ -970,6 +971,7 @@ def __init__( self.releases = releases self.has_aims = has_aims self.has_cohorts_by_quarter = has_cohorts_by_quarter + self.has_sequence_qc = has_sequence_qc # Clear out the fixture directories. shutil.rmtree(self.bucket_path, ignore_errors=True) @@ -1088,6 +1090,7 @@ def __init__(self, fixture_dir): releases=("3.0", "3.1"), has_aims=True, has_cohorts_by_quarter=True, + has_sequence_qc=True, ) def init_config(self): @@ -1193,7 +1196,15 @@ def init_genome_features(self): simulator = Gff3Simulator(contig_sizes=self.contig_sizes) self.genome_features = simulator.simulate_gff(path=path) - def write_metadata(self, release, release_path, sample_set, aim=True, cohorts=True): + def write_metadata( + self, + release, + release_path, + sample_set, + aim=True, + cohorts=True, + sequence_qc=True, + ): # Here we take the approach of using some of the real metadata, # but truncating it to the number of samples included in the # simulated data resource. @@ -1231,6 +1242,34 @@ def write_metadata(self, release, release_path, sample_set, aim=True, cohorts=Tr dst_path.parent.mkdir(parents=True, exist_ok=True) df_general_ds.to_csv(dst_path, index=False) + if sequence_qc: + # Create sequence QC metadata by sample from real metadata files. + src_path = ( + self.fixture_dir + / "vo_agam_release" + / release_path + / "metadata" + / "curation" + / sample_set + / "sequence_qc_stats.csv" + ) + df_sequence_qc_stats = pd.read_csv(src_path) + df_sequence_qc_stats_ds = ( + df_sequence_qc_stats.set_index("sample_id") + .loc[samples_ds] + .reset_index() + ) + dst_path = ( + self.bucket_path + / release_path + / "metadata" + / "curation" + / sample_set + / "sequence_qc_stats.csv" + ) + dst_path.parent.mkdir(parents=True, exist_ok=True) + df_sequence_qc_stats_ds.to_csv(dst_path, index=False) + if aim: # Create AIM metadata by sampling from some real metadata files. src_path = ( @@ -1803,6 +1842,7 @@ def __init__(self, fixture_dir): releases=("1.0",), has_aims=False, has_cohorts_by_quarter=False, + has_sequence_qc=True, ) def init_config(self): @@ -1899,7 +1939,7 @@ def init_genome_features(self): ) self.genome_features = simulator.simulate_gff(path=path) - def write_metadata(self, release, release_path, sample_set): + def write_metadata(self, release, release_path, sample_set, sequence_qc=True): # Here we take the approach of using some of the real metadata, # but truncating it to the number of samples included in the # simulated data resource. @@ -1936,6 +1976,34 @@ def write_metadata(self, release, release_path, sample_set): dst_path.parent.mkdir(parents=True, exist_ok=True) df_general_ds.to_csv(dst_path, index=False) + if sequence_qc: + # Create sequence QC metadata by sample from real metadata files. + src_path = ( + self.fixture_dir + / "vo_afun_release" + / release_path + / "metadata" + / "curation" + / sample_set + / "sequence_qc_stats.csv" + ) + df_sequence_qc_stats = pd.read_csv(src_path) + df_sequence_qc_stats_ds = ( + df_sequence_qc_stats.set_index("sample_id") + .loc[samples_ds] + .reset_index() + ) + dst_path = ( + self.bucket_path + / release_path + / "metadata" + / "curation" + / sample_set + / "sequence_qc_stats.csv" + ) + dst_path.parent.mkdir(parents=True, exist_ok=True) + df_sequence_qc_stats_ds.to_csv(dst_path, index=False) + # Create cohorts metadata by sampling from some real metadata files. src_path = ( self.fixture_dir diff --git a/tests/anoph/fixture/missing_metadata/config.json b/tests/anoph/fixture/missing_metadata/config.json index 1240d5c78..b8e4f0337 100644 --- a/tests/anoph/fixture/missing_metadata/config.json +++ b/tests/anoph/fixture/missing_metadata/config.json @@ -3,5 +3,12 @@ "3.0" ], "DEFAULT_AIM_ANALYSIS": "20220528", - "DEFAULT_COHORTS_ANALYSIS": "20230223" + "DEFAULT_COHORTS_ANALYSIS": "20230223", + "CONTIGS": [ + "2R", + "2L", + "3R", + "3L", + "X" + ] } diff --git 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+AR0013-Cx,26.57,26,26,26.72,26,26,26.46,26,27,25.75,26,26,26.24,26,26,28.67,27,27,0.94,0.018,0.232,116.554 +AR0026-C,23.17,20,14,23.15,20,15,21.0,18,13,24.86,22,17,23.95,21,16,24.15,20,14,0.943,0.019,2.049,2616.398 +AR0053-C,25.16,24,24,25.51,25,24,24.15,24,23,25.74,25,25,25.38,25,24,25.55,24,23,0.942,0.018,0.446,304.534 diff --git a/tests/anoph/fixture/vo_agam_release/v3/metadata/curation/AG1000G-BF-A/sequence_qc_stats.csv b/tests/anoph/fixture/vo_agam_release/v3/metadata/curation/AG1000G-BF-A/sequence_qc_stats.csv new file mode 100644 index 000000000..23cfc159f --- /dev/null +++ b/tests/anoph/fixture/vo_agam_release/v3/metadata/curation/AG1000G-BF-A/sequence_qc_stats.csv @@ -0,0 +1,182 @@ +sample_id,mean_cov,median_cov,modal_cov,mean_cov_2L,median_cov_2L,mode_cov_2L,mean_cov_2R,median_cov_2R,mode_cov_2R,mean_cov_3L,median_cov_3L,mode_cov_3L,mean_cov_3R,median_cov_3R,mode_cov_3R,mean_cov_X,median_cov_X,mode_cov_X,frac_gen_cov,divergence,contam_pct,contam_LLR +AB0085-Cx,30.62,31,31,30.72,31,31,30.43,31,31,30.53,31,31,30.47,31,31,31.4,30,30,0.935,0.022,1.193,1503.154 +AB0086-Cx,29.83,30,30,29.88,30,30,29.6,30,30,29.92,30,30,29.77,30,30,30.35,29,29,0.938,0.022,3.917,8025.882 +AB0087-C,38.23,38,38,37.82,37,37,37.85,38,38,38.13,38,38,38.3,38,38,40.02,38,36,0.941,0.02,0.655,682.111 +AB0088-C,24.32,24,24,24.2,24,24,24.11,24,24,24.05,24,24,24.14,24,24,25.9,24,24,0.939,0.02,1.023,829.1 +AB0089-Cx,16.93,17,17,16.78,16,16,16.6,16,16,16.92,17,17,16.93,17,17,18.12,17,17,0.934,0.02,0.414,140.574 +AB0090-C,36.42,37,37,36.63,37,37,36.22,37,37,36.14,37,37,36.06,37,37,37.76,36,36,0.941,0.02,0.533,469.754 +AB0091-C,31.62,32,32,31.62,32,32,31.4,32,32,31.3,32,32,31.45,32,32,33.13,32,31,0.942,0.02,0.814,829.483 +AB0092-C,31.28,31,32,31.19,31,32,31.07,31,32,31.02,31,32,30.93,31,32,33.24,32,32,0.939,0.02,0.695,666.495 +AB0094-Cx,36.23,36,37,36.12,36,36,35.93,36,36,36.17,36,37,36.02,36,37,37.8,36,36,0.938,0.02,0.371,276.03 +AB0095-Cx,34.16,34,34,34.09,34,34,33.71,34,34,34.06,34,35,33.89,34,34,36.2,35,34,0.94,0.02,0.527,486.483 +AB0096-C,38.89,39,39,38.86,39,39,38.35,39,39,39.24,39,40,38.83,39,40,39.86,39,39,0.941,0.022,2.538,6214.583 +AB0097-Cx,19.94,20,19,19.79,19,19,19.51,19,19,19.97,20,20,19.93,20,20,21.28,20,20,0.94,0.02,1.853,1798.585 +AB0098-Cx,35.37,35,34,35.0,34,34,34.24,34,34,35.57,35,35,35.65,35,34,37.97,36,34,0.938,0.02,0.611,599.565 +AB0099-Cx,30.98,31,31,30.8,31,31,30.72,31,31,30.55,31,32,30.91,31,31,32.85,31,31,0.94,0.02,0.859,887.232 +AB0100-C,33.97,34,34,33.75,34,34,33.52,34,34,33.8,34,34,33.87,34,34,36.06,34,34,0.94,0.02,1.046,1262.234 +AB0101-C,30.57,31,31,30.59,31,31,30.38,31,31,30.34,31,31,30.16,30,31,32.31,31,30,0.941,0.02,1.038,1118.188 +AB0103-C,33.89,34,34,33.89,34,34,33.5,34,34,33.73,34,35,33.82,34,34,35.38,34,34,0.936,0.022,0.784,816.042 +AB0104-Cx,28.93,29,29,29.05,29,29,28.64,29,29,28.87,29,29,28.74,29,29,29.99,29,29,0.934,0.022,0.785,693.358 +AB0109-C,34.79,35,35,34.59,34,34,34.14,34,34,34.66,35,35,34.71,35,35,37.28,35,35,0.942,0.02,1.159,1535.979 +AB0110-C,52.88,54,55,53.0,54,55,52.3,54,54,52.31,54,55,52.35,53,55,56.23,55,55,0.946,0.02,0.699,1085.615 +AB0111-C,31.89,32,32,31.82,32,32,31.6,32,32,31.72,32,32,31.64,32,32,33.58,32,31,0.939,0.02,0.487,361.481 +AB0112-C,38.12,38,39,38.15,38,39,37.92,38,39,37.64,38,39,37.67,38,39,40.34,39,38,0.944,0.02,0.629,675.052 +AB0113-C,31.91,32,32,31.71,32,32,31.46,32,32,31.61,32,32,31.78,32,32,34.24,32,32,0.942,0.02,0.88,899.811 +AB0114-C,54.43,55,56,53.68,54,55,53.36,54,56,54.31,55,57,54.25,55,56,59.22,57,57,0.947,0.02,0.671,1092.444 +AB0115-C,44.91,45,45,44.76,45,45,44.28,45,45,44.57,45,46,44.73,45,45,47.84,46,45,0.939,0.02,0.534,599.212 +AB0116-C,25.09,25,25,25.18,25,25,24.79,25,25,24.99,25,25,25.1,25,25,25.86,25,25,0.936,0.022,3.759,5860.007 +AB0117-C,25.07,25,27,26.31,26,27,26.16,26,27,26.09,26,27,26.29,26,27,14.92,13,13,0.934,0.021,0.911,763.493 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+AB0181-C,36.84,36,36,36.53,36,36,36.01,36,35,37.18,37,37,36.99,37,36,38.61,36,35,0.94,0.02,0.837,911.24 +AB0182-C,32.89,33,33,32.51,33,33,32.57,33,33,32.62,33,34,32.86,33,33,34.98,33,33,0.94,0.02,0.651,613.629 +AB0183-C,27.43,27,28,27.46,27,28,27.29,27,28,27.02,27,28,27.06,27,27,29.24,28,28,0.94,0.02,1.167,1244.127 +AB0184-C,29.43,29,30,29.56,30,30,29.29,30,30,29.14,29,30,29.01,29,30,30.95,30,30,0.939,0.02,0.754,661.221 +AB0185-Cx,41.03,41,42,40.98,41,42,40.57,41,42,40.78,41,42,40.72,41,42,43.39,42,41,0.942,0.02,0.748,932.829 +AB0186-C,30.36,30,31,30.34,30,31,30.13,30,31,29.94,30,31,30.05,30,31,32.41,31,30,0.94,0.02,0.646,517.133 +AB0187-C,33.96,34,34,33.87,34,34,33.64,34,35,33.55,34,35,33.66,34,34,36.34,35,34,0.94,0.02,0.923,1081.89 +AB0188-C,26.84,27,27,26.73,27,27,26.58,27,27,26.39,27,27,26.67,27,27,28.9,27,27,0.939,0.02,1.17,1196.982 +AB0189-C,40.44,40,41,39.8,40,40,39.68,40,40,40.7,41,41,40.81,41,41,42.34,40,39,0.941,0.02,1.29,2180.752 +AB0190-C,38.79,39,39,38.48,38,39,38.26,38,39,38.78,39,40,38.71,39,39,41.0,39,39,0.938,0.02,0.913,1141.478 +AB0191-Cx,34.94,35,35,34.66,34,34,34.38,34,35,34.83,35,35,35.03,35,35,36.91,35,34,0.935,0.02,0.592,590.074 +AB0192-C,35.67,36,36,35.35,35,36,35.1,35,36,35.34,36,36,35.66,36,36,38.36,36,36,0.941,0.02,0.626,598.193 +AB0193-C,34.06,34,35,34.12,34,35,34.03,34,35,33.52,34,35,33.64,34,35,35.9,35,35,0.942,0.02,0.946,1160.784 +AB0194-C,40.19,40,41,40.15,40,41,39.7,40,41,40.07,41,42,39.98,40,41,42.21,41,40,0.944,0.02,3.39,9866.195 +AB0196-C,34.98,35,35,35.0,35,35,34.28,34,35,34.72,35,35,34.79,35,35,37.59,36,35,0.939,0.02,0.94,1144.27 +AB0197-C,37.08,37,38,37.31,38,38,36.97,37,38,36.83,37,38,36.82,37,38,37.88,37,37,0.934,0.022,0.84,942.373 +AB0198-C,37.15,37,38,37.41,37,38,36.8,37,38,37.11,37,38,36.88,37,38,38.14,37,37,0.935,0.022,1.017,1148.867 +AB0199-C,33.23,33,34,33.35,33,34,32.96,33,34,33.09,33,34,33.02,33,34,34.4,33,33,0.934,0.022,1.232,1567.397 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+AB0284-C,31.19,31,32,31.43,32,32,31.27,32,32,30.85,31,32,30.92,31,32,31.75,31,31,0.934,0.022,0.984,893.48 diff --git a/tests/anoph/test_sample_metadata.py b/tests/anoph/test_sample_metadata.py index 25000aaa3..c5aecb063 100644 --- a/tests/anoph/test_sample_metadata.py +++ b/tests/anoph/test_sample_metadata.py @@ -51,7 +51,7 @@ def af1_sim_api(af1_sim_fixture): @pytest.fixture def missing_metadata_api(fixture_dir): # In this fixture, one of the sample sets (AG1000G-BF-A) has missing files - # for both AIM and cohorts metadata. + # for sequence QC, AIM and cohorts metadata. return AnophelesSampleMetadata( url=(fixture_dir / "missing_metadata").as_uri(), config_path="config.json", @@ -99,6 +99,7 @@ def general_metadata_expected_columns(): def validate_metadata(df, expected_columns): # Check column names. + # Note: insertion order in dictionary keys is guaranteed since Python 3.7 expected_column_names = list(expected_columns.keys()) assert df.columns.to_list() == expected_column_names @@ -157,6 +158,116 @@ def test_general_metadata_with_release(fixture, api: AnophelesSampleMetadata): assert len(df) == expected_len +def sequence_qc_metadata_expected_columns(ordered_contigs): + # Note: validate_metadata() expects an ordered dictionary. + # Note: insertion order in dictionary keys is guaranteed since Python 3.7 + + expected_columns = { + "sample_id": "O", + "mean_cov": "f", + "median_cov": "i", + "modal_cov": "i", + } + + for contig in ordered_contigs: + expected_columns[f"mean_cov_{contig}"] = "f" + expected_columns[f"median_cov_{contig}"] = "i" + expected_columns[f"mode_cov_{contig}"] = "i" + + expected_columns.update( + { + "frac_gen_cov": "f", + "divergence": "f", + "contam_pct": "f", + "contam_LLR": "f", + } + ) + + return expected_columns + + +@parametrize_with_cases("fixture,api", cases=".") +def test_sequence_qc_metadata_with_single_sample_set( + fixture, api: AnophelesSampleMetadata +): + # Set up test. + df_sample_sets = api.sample_sets().set_index("sample_set") + sample_count = df_sample_sets["sample_count"] + all_sample_sets = df_sample_sets.index.to_list() + sample_set = random.choice(all_sample_sets) + + # Call function to be tested. + df = api.sequence_qc_metadata(sample_sets=sample_set) + + # Check output. + validate_metadata( + df, sequence_qc_metadata_expected_columns(sorted(fixture.config["CONTIGS"])) + ) + expected_len = sample_count.loc[sample_set] + assert len(df) == expected_len + + +@parametrize_with_cases("fixture,api", cases=".") +def test_sequence_qc_metadata_with_multiple_sample_sets( + fixture, api: AnophelesSampleMetadata +): + # Set up the test. + df_sample_sets = api.sample_sets().set_index("sample_set") + sample_count = df_sample_sets["sample_count"] + all_sample_sets = df_sample_sets.index.to_list() + sample_sets = random.sample(all_sample_sets, 2) + + # Call function to be tested. + df = api.sequence_qc_metadata(sample_sets=sample_sets) + + # Check output. + validate_metadata( + df, sequence_qc_metadata_expected_columns(sorted(fixture.config["CONTIGS"])) + ) + expected_len = sum([sample_count.loc[s] for s in sample_sets]) + assert len(df) == expected_len + + +@parametrize_with_cases("fixture,api", cases=".") +def test_sequence_qc_metadata_with_release(fixture, api: AnophelesSampleMetadata): + # Set up the test. + release = random.choice(api.releases) + + # Call function to be tested. + df = api.sequence_qc_metadata(sample_sets=release) + + # Check output. + validate_metadata( + df, sequence_qc_metadata_expected_columns(sorted(fixture.config["CONTIGS"])) + ) + expected_len = api.sample_sets(release=release)["sample_count"].sum() + assert len(df) == expected_len + + +def test_sequence_qc_metadata_with_missing_file( + missing_metadata_api: AnophelesSampleMetadata, +): + # In this test, one of the sample sets (AG1000G-BF-A) has a missing file. + # We expect this to be filled with empty values. + api = missing_metadata_api + + # Set up test. + df_sample_sets = api.sample_sets().set_index("sample_set") + sample_count = df_sample_sets["sample_count"] + all_sample_sets = df_sample_sets.index.to_list() + + for sample_set in all_sample_sets: + # Call function to be tested. + df = api.sequence_qc_metadata(sample_sets=sample_set) + + # Check output. + validate_metadata( + df, sequence_qc_metadata_expected_columns(sorted(api.config["CONTIGS"])) + ) + expected_len = sample_count.loc[sample_set] + assert len(df) == expected_len + + def aim_metadata_expected_columns(): return { "sample_id": "O", @@ -372,8 +483,12 @@ def test_cohorts_metadata_with_missing_file( assert len(df) == expected_len -def sample_metadata_expected_columns(has_aims, has_cohorts_by_quarter): +def sample_metadata_expected_columns( + has_aims, has_cohorts_by_quarter, has_sequence_qc, ordered_contigs +): expected_columns = general_metadata_expected_columns() + if has_sequence_qc: + expected_columns.update(sequence_qc_metadata_expected_columns(ordered_contigs)) if has_aims: expected_columns.update(aim_metadata_expected_columns()) expected_columns.update( @@ -407,6 +522,8 @@ def test_sample_metadata_with_single_sample_set(fixture, api: AnophelesSampleMet sample_metadata_expected_columns( has_aims=fixture.has_aims, has_cohorts_by_quarter=fixture.has_cohorts_by_quarter, + has_sequence_qc=fixture.has_sequence_qc, + ordered_contigs=sorted(fixture.config["CONTIGS"]), ), ) expected_len = sample_count.loc[sample_set] @@ -432,6 +549,8 @@ def test_sample_metadata_with_multiple_sample_sets( sample_metadata_expected_columns( has_aims=fixture.has_aims, has_cohorts_by_quarter=fixture.has_cohorts_by_quarter, + has_sequence_qc=fixture.has_sequence_qc, + ordered_contigs=sorted(fixture.config["CONTIGS"]), ), ) expected_len = sum([sample_count.loc[s] for s in sample_sets]) @@ -452,6 +571,8 @@ def test_sample_metadata_with_release(fixture, api: AnophelesSampleMetadata): sample_metadata_expected_columns( has_aims=fixture.has_aims, has_cohorts_by_quarter=fixture.has_cohorts_by_quarter, + has_sequence_qc=fixture.has_sequence_qc, + ordered_contigs=sorted(fixture.config["CONTIGS"]), ), ) expected_len = api.sample_sets(release=release)["sample_count"].sum() @@ -486,7 +607,13 @@ def test_sample_metadata_with_duplicate_sample_sets( def test_sample_metadata_with_query(ag3_sim_api): df = ag3_sim_api.sample_metadata(sample_query="country == 'Burkina Faso'") validate_metadata( - df, sample_metadata_expected_columns(has_aims=True, has_cohorts_by_quarter=True) + df, + sample_metadata_expected_columns( + has_aims=True, + has_cohorts_by_quarter=True, + has_sequence_qc=True, + ordered_contigs=sorted(ag3_sim_api.config["CONTIGS"]), + ), ) assert (df["country"] == "Burkina Faso").all() @@ -499,12 +626,22 @@ def test_sample_metadata_with_indices(ag3_sim_api): df2 = ag3_sim_api.sample_metadata(sample_indices=indices) validate_metadata( df1, - sample_metadata_expected_columns(has_aims=True, has_cohorts_by_quarter=True), + sample_metadata_expected_columns( + has_aims=True, + has_cohorts_by_quarter=True, + has_sequence_qc=True, + ordered_contigs=sorted(ag3_sim_api.config["CONTIGS"]), + ), ) assert (df1["country"] == "Burkina Faso").all() validate_metadata( df2, - sample_metadata_expected_columns(has_aims=True, has_cohorts_by_quarter=True), + sample_metadata_expected_columns( + has_aims=True, + has_cohorts_by_quarter=True, + has_sequence_qc=True, + ordered_contigs=sorted(ag3_sim_api.config["CONTIGS"]), + ), ) assert (df2["country"] == "Burkina Faso").all() assert_frame_equal(df1, df2) @@ -549,7 +686,10 @@ def test_sample_metadata_with_missing_file( validate_metadata( df, sample_metadata_expected_columns( - has_aims=True, has_cohorts_by_quarter=True + has_aims=True, + has_cohorts_by_quarter=True, + has_sequence_qc=True, + ordered_contigs=sorted(api.config["CONTIGS"]), ), ) expected_len = sample_count.loc[sample_set] @@ -1037,6 +1177,7 @@ def cohort_data_expected_columns(): def validate_cohort_data(df, expected_columns): # Check column names. + # Note: insertion order in dictionary keys is guaranteed since Python 3.7 expected_column_names = list(expected_columns.keys()) assert df.columns.to_list() == expected_column_names