Other features

List of features

gentropy.dataset.l2g_features.other.GeneCountFeature dataclass

Bases: L2GFeature

Counts the number of genes within a specified window size from the study locus.

Source code in src/gentropy/dataset/l2g_features/other.py

class GeneCountFeature(L2GFeature):
    """Counts the number of genes within a specified window size from the study locus."""

    feature_dependency_type = GeneIndex
    feature_name = "geneCount500kb"

    @classmethod
    def compute(
        cls: type[GeneCountFeature],
        study_loci_to_annotate: StudyLocus | L2GGoldStandard,
        feature_dependency: dict[str, Any],
    ) -> GeneCountFeature:
        """Computes the gene count feature.

        Args:
            study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
            feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size

        Returns:
            GeneCountFeature: Feature dataset
        """
        genomic_window = 500000
        gene_count_df = common_genecount_feature_logic(
            study_loci_to_annotate=study_loci_to_annotate,
            feature_name=cls.feature_name,
            genomic_window=genomic_window,
            **feature_dependency,
        )

        return cls(
            _df=convert_from_wide_to_long(
                gene_count_df,
                id_vars=("studyLocusId", "geneId"),
                var_name="featureName",
                value_name="featureValue",
            ),
            _schema=cls.get_schema(),
        )

compute(study_loci_to_annotate: StudyLocus | L2GGoldStandard, feature_dependency: dict[str, Any]) -> GeneCountFeature classmethod

Computes the gene count feature.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
feature_dependency	dict[str, Any]	Dictionary containing dependencies, with gene index and window size	required

Returns:

Name	Type	Description
GeneCountFeature	GeneCountFeature	Feature dataset

Source code in src/gentropy/dataset/l2g_features/other.py

@classmethod
def compute(
    cls: type[GeneCountFeature],
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    feature_dependency: dict[str, Any],
) -> GeneCountFeature:
    """Computes the gene count feature.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
        feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size

    Returns:
        GeneCountFeature: Feature dataset
    """
    genomic_window = 500000
    gene_count_df = common_genecount_feature_logic(
        study_loci_to_annotate=study_loci_to_annotate,
        feature_name=cls.feature_name,
        genomic_window=genomic_window,
        **feature_dependency,
    )

    return cls(
        _df=convert_from_wide_to_long(
            gene_count_df,
            id_vars=("studyLocusId", "geneId"),
            var_name="featureName",
            value_name="featureValue",
        ),
        _schema=cls.get_schema(),
    )

gentropy.dataset.l2g_features.other.ProteinGeneCountFeature dataclass

Bases: L2GFeature

Counts the number of protein coding genes within a specified window size from the study locus.

Source code in src/gentropy/dataset/l2g_features/other.py

class ProteinGeneCountFeature(L2GFeature):
    """Counts the number of protein coding genes within a specified window size from the study locus."""

    feature_dependency_type = GeneIndex
    feature_name = "proteinGeneCount500kb"

    @classmethod
    def compute(
        cls: type[ProteinGeneCountFeature],
        study_loci_to_annotate: StudyLocus | L2GGoldStandard,
        feature_dependency: dict[str, Any],
    ) -> ProteinGeneCountFeature:
        """Computes the gene count feature.

        Args:
            study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
            feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size

        Returns:
            ProteinGeneCountFeature: Feature dataset
        """
        genomic_window = 500000
        gene_count_df = common_genecount_feature_logic(
            study_loci_to_annotate=study_loci_to_annotate,
            feature_name=cls.feature_name,
            genomic_window=genomic_window,
            protein_coding_only=True,
            **feature_dependency,
        )

        return cls(
            _df=convert_from_wide_to_long(
                gene_count_df,
                id_vars=("studyLocusId", "geneId"),
                var_name="featureName",
                value_name="featureValue",
            ),
            _schema=cls.get_schema(),
        )

compute(study_loci_to_annotate: StudyLocus | L2GGoldStandard, feature_dependency: dict[str, Any]) -> ProteinGeneCountFeature classmethod

Computes the gene count feature.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
feature_dependency	dict[str, Any]	Dictionary containing dependencies, with gene index and window size	required

Returns:

Name	Type	Description
ProteinGeneCountFeature	ProteinGeneCountFeature	Feature dataset

Source code in src/gentropy/dataset/l2g_features/other.py

@classmethod
def compute(
    cls: type[ProteinGeneCountFeature],
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    feature_dependency: dict[str, Any],
) -> ProteinGeneCountFeature:
    """Computes the gene count feature.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
        feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size

    Returns:
        ProteinGeneCountFeature: Feature dataset
    """
    genomic_window = 500000
    gene_count_df = common_genecount_feature_logic(
        study_loci_to_annotate=study_loci_to_annotate,
        feature_name=cls.feature_name,
        genomic_window=genomic_window,
        protein_coding_only=True,
        **feature_dependency,
    )

    return cls(
        _df=convert_from_wide_to_long(
            gene_count_df,
            id_vars=("studyLocusId", "geneId"),
            var_name="featureName",
            value_name="featureValue",
        ),
        _schema=cls.get_schema(),
    )

gentropy.dataset.l2g_features.other.ProteinCodingFeature dataclass

Bases: L2GFeature

Indicates whether a gene is protein-coding within a specified window size from the study locus.

Source code in src/gentropy/dataset/l2g_features/other.py

class ProteinCodingFeature(L2GFeature):
    """Indicates whether a gene is protein-coding within a specified window size from the study locus."""

    feature_dependency_type = GeneIndex
    feature_name = "isProteinCoding"

    @classmethod
    def compute(
        cls: type[ProteinCodingFeature],
        study_loci_to_annotate: StudyLocus | L2GGoldStandard,
        feature_dependency: dict[str, Any],
    ) -> ProteinCodingFeature:
        """Computes the protein coding feature.

        Args:
            study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
            feature_dependency (dict[str, Any]): Dictionary containing dependencies, including gene index

        Returns:
            ProteinCodingFeature: Feature dataset with 1 if the gene is protein-coding, 0 otherwise
        """
        genomic_window = 1000000
        protein_coding_df = is_protein_coding_feature_logic(
            study_loci_to_annotate=study_loci_to_annotate,
            feature_name=cls.feature_name,
            genomic_window=genomic_window,
            **feature_dependency,
        )

        return cls(
            _df=convert_from_wide_to_long(
                protein_coding_df,
                id_vars=("studyLocusId", "geneId"),
                var_name="featureName",
                value_name="featureValue",
            ),
            _schema=cls.get_schema(),
        )

compute(study_loci_to_annotate: StudyLocus | L2GGoldStandard, feature_dependency: dict[str, Any]) -> ProteinCodingFeature classmethod

Computes the protein coding feature.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
feature_dependency	dict[str, Any]	Dictionary containing dependencies, including gene index	required

Returns:

Name	Type	Description
ProteinCodingFeature	ProteinCodingFeature	Feature dataset with 1 if the gene is protein-coding, 0 otherwise

Source code in src/gentropy/dataset/l2g_features/other.py

@classmethod
def compute(
    cls: type[ProteinCodingFeature],
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    feature_dependency: dict[str, Any],
) -> ProteinCodingFeature:
    """Computes the protein coding feature.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
        feature_dependency (dict[str, Any]): Dictionary containing dependencies, including gene index

    Returns:
        ProteinCodingFeature: Feature dataset with 1 if the gene is protein-coding, 0 otherwise
    """
    genomic_window = 1000000
    protein_coding_df = is_protein_coding_feature_logic(
        study_loci_to_annotate=study_loci_to_annotate,
        feature_name=cls.feature_name,
        genomic_window=genomic_window,
        **feature_dependency,
    )

    return cls(
        _df=convert_from_wide_to_long(
            protein_coding_df,
            id_vars=("studyLocusId", "geneId"),
            var_name="featureName",
            value_name="featureValue",
        ),
        _schema=cls.get_schema(),
    )

gentropy.dataset.l2g_features.other.CredibleSetConfidenceFeature dataclass

Bases: L2GFeature

Distance of the sentinel variant to gene TSS. This is not weighted by the causal probability.

Source code in src/gentropy/dataset/l2g_features/other.py

class CredibleSetConfidenceFeature(L2GFeature):
    """Distance of the sentinel variant to gene TSS. This is not weighted by the causal probability."""

    feature_dependency_type = [StudyLocus, VariantIndex]
    feature_name = "credibleSetConfidence"

    @classmethod
    def compute(
        cls: type[CredibleSetConfidenceFeature],
        study_loci_to_annotate: StudyLocus | L2GGoldStandard,
        feature_dependency: dict[str, Any],
    ) -> CredibleSetConfidenceFeature:
        """Computes the feature.

        Args:
            study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
            feature_dependency (dict[str, Any]): Dataset that contains the distance information

        Returns:
            CredibleSetConfidenceFeature: Feature dataset
        """
        full_credible_set = feature_dependency["study_locus"].df.select(
            "studyLocusId",
            "studyId",
            f.explode("locus.variantId").alias("variantId"),
            cls.score_credible_set_confidence(f.col("confidence")).alias(
                cls.feature_name
            ),
        )

        return cls(
            _df=convert_from_wide_to_long(
                (
                    study_loci_to_annotate.df.drop("studyLocusId")
                    # Annotate genes
                    .join(
                        feature_dependency["variant_index"].df.select(
                            "variantId",
                            f.explode("transcriptConsequences.targetId").alias(
                                "geneId"
                            ),
                        ),
                        on="variantId",
                        how="inner",
                    )
                    # Annotate credible set confidence
                    .join(full_credible_set, ["variantId", "studyId"])
                    .select("studyLocusId", "geneId", cls.feature_name)
                ),
                id_vars=("studyLocusId", "geneId"),
                var_name="featureName",
                value_name="featureValue",
            ),
            _schema=cls.get_schema(),
        )

    @classmethod
    def score_credible_set_confidence(
        cls: type[CredibleSetConfidenceFeature],
        confidence_column: Column,
    ) -> Column:
        """Expression that assigns a score to the credible set confidence.

        Args:
            confidence_column (Column): Confidence column in the StudyLocus object

        Returns:
            Column: A confidence score between 0 and 1
        """
        return (
            f.when(
                f.col("confidence")
                == CredibleSetConfidenceClasses.FINEMAPPED_IN_SAMPLE_LD.value,
                f.lit(1.0),
            )
            .when(
                f.col("confidence")
                == CredibleSetConfidenceClasses.FINEMAPPED_OUT_OF_SAMPLE_LD.value,
                f.lit(0.75),
            )
            .when(
                f.col("confidence")
                == CredibleSetConfidenceClasses.PICSED_SUMMARY_STATS.value,
                f.lit(0.5),
            )
            .when(
                f.col("confidence")
                == CredibleSetConfidenceClasses.PICSED_TOP_HIT.value,
                f.lit(0.25),
            )
            .when(
                f.col("confidence") == CredibleSetConfidenceClasses.UNKNOWN.value,
                f.lit(0.0),
            )
        )

compute(study_loci_to_annotate: StudyLocus | L2GGoldStandard, feature_dependency: dict[str, Any]) -> CredibleSetConfidenceFeature classmethod

Computes the feature.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
feature_dependency	dict[str, Any]	Dataset that contains the distance information	required

Returns:

Name	Type	Description
CredibleSetConfidenceFeature	CredibleSetConfidenceFeature	Feature dataset

Source code in src/gentropy/dataset/l2g_features/other.py

@classmethod
def compute(
    cls: type[CredibleSetConfidenceFeature],
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    feature_dependency: dict[str, Any],
) -> CredibleSetConfidenceFeature:
    """Computes the feature.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
        feature_dependency (dict[str, Any]): Dataset that contains the distance information

    Returns:
        CredibleSetConfidenceFeature: Feature dataset
    """
    full_credible_set = feature_dependency["study_locus"].df.select(
        "studyLocusId",
        "studyId",
        f.explode("locus.variantId").alias("variantId"),
        cls.score_credible_set_confidence(f.col("confidence")).alias(
            cls.feature_name
        ),
    )

    return cls(
        _df=convert_from_wide_to_long(
            (
                study_loci_to_annotate.df.drop("studyLocusId")
                # Annotate genes
                .join(
                    feature_dependency["variant_index"].df.select(
                        "variantId",
                        f.explode("transcriptConsequences.targetId").alias(
                            "geneId"
                        ),
                    ),
                    on="variantId",
                    how="inner",
                )
                # Annotate credible set confidence
                .join(full_credible_set, ["variantId", "studyId"])
                .select("studyLocusId", "geneId", cls.feature_name)
            ),
            id_vars=("studyLocusId", "geneId"),
            var_name="featureName",
            value_name="featureValue",
        ),
        _schema=cls.get_schema(),
    )

score_credible_set_confidence(confidence_column: Column) -> Column classmethod

Expression that assigns a score to the credible set confidence.

Parameters:

Name	Type	Description	Default
confidence_column	Column	Confidence column in the StudyLocus object	required

Returns:

Name	Type	Description
Column	Column	A confidence score between 0 and 1

Source code in src/gentropy/dataset/l2g_features/other.py

@classmethod
def score_credible_set_confidence(
    cls: type[CredibleSetConfidenceFeature],
    confidence_column: Column,
) -> Column:
    """Expression that assigns a score to the credible set confidence.

    Args:
        confidence_column (Column): Confidence column in the StudyLocus object

    Returns:
        Column: A confidence score between 0 and 1
    """
    return (
        f.when(
            f.col("confidence")
            == CredibleSetConfidenceClasses.FINEMAPPED_IN_SAMPLE_LD.value,
            f.lit(1.0),
        )
        .when(
            f.col("confidence")
            == CredibleSetConfidenceClasses.FINEMAPPED_OUT_OF_SAMPLE_LD.value,
            f.lit(0.75),
        )
        .when(
            f.col("confidence")
            == CredibleSetConfidenceClasses.PICSED_SUMMARY_STATS.value,
            f.lit(0.5),
        )
        .when(
            f.col("confidence")
            == CredibleSetConfidenceClasses.PICSED_TOP_HIT.value,
            f.lit(0.25),
        )
        .when(
            f.col("confidence") == CredibleSetConfidenceClasses.UNKNOWN.value,
            f.lit(0.0),
        )
    )

Common logic

gentropy.dataset.l2g_features.other.common_genecount_feature_logic(study_loci_to_annotate: StudyLocus | L2GGoldStandard, *, gene_index: GeneIndex, feature_name: str, genomic_window: int, protein_coding_only: bool = False) -> DataFrame

Computes the feature.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
gene_index	GeneIndex	Dataset containing information related to all genes in release.	required
feature_name	str	The name of the feature	required
genomic_window	int	The maximum window size to consider	required
protein_coding_only	bool	Whether to only consider protein coding genes in calculation.	False

Returns:

Name	Type	Description
DataFrame	DataFrame	Feature dataset

Source code in src/gentropy/dataset/l2g_features/other.py

def common_genecount_feature_logic(
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    *,
    gene_index: GeneIndex,
    feature_name: str,
    genomic_window: int,
    protein_coding_only: bool = False,
) -> DataFrame:
    """Computes the feature.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci
            that will be used for annotation
        gene_index (GeneIndex): Dataset containing information related to all genes in release.
        feature_name (str): The name of the feature
        genomic_window (int): The maximum window size to consider
        protein_coding_only (bool): Whether to only consider protein coding genes in calculation.

    Returns:
            DataFrame: Feature dataset
    """
    study_loci_window = (
        study_loci_to_annotate.df.withColumn(
            "window_start", f.col("position") - (genomic_window / 2)
        )
        .withColumn("window_end", f.col("position") + (genomic_window / 2))
        .withColumnRenamed("chromosome", "SL_chromosome")
    )
    gene_index_filter = gene_index.df

    if protein_coding_only:
        gene_index_filter = gene_index_filter.filter(
            f.col("biotype") == "protein_coding"
        )

    distinct_gene_counts = (
        study_loci_window.join(
            gene_index_filter.alias("genes"),
            on=(
                (f.col("SL_chromosome") == f.col("genes.chromosome"))
                & (f.col("genes.tss") >= f.col("window_start"))
                & (f.col("genes.tss") <= f.col("window_end"))
            ),
            how="inner",
        )
        .groupBy("studyLocusId")
        .agg(f.approx_count_distinct("geneId").alias(feature_name))
    )

    return (
        study_loci_window.join(
            gene_index_filter.alias("genes"),
            on=(
                (f.col("SL_chromosome") == f.col("genes.chromosome"))
                & (f.col("genes.tss") >= f.col("window_start"))
                & (f.col("genes.tss") <= f.col("window_end"))
            ),
            how="inner",
        )
        .join(distinct_gene_counts, on="studyLocusId", how="inner")
        .select("studyLocusId", "geneId", feature_name)
        .distinct()
    )

gentropy.dataset.l2g_features.other.is_protein_coding_feature_logic(study_loci_to_annotate: StudyLocus | L2GGoldStandard, *, gene_index: GeneIndex, feature_name: str, genomic_window: int) -> DataFrame

Computes the feature to indicate if a gene is protein-coding or not.

Parameters:

Name	Type	Description	Default
study_loci_to_annotate	StudyLocus | L2GGoldStandard	The dataset containing study loci that will be used for annotation	required
gene_index	GeneIndex	Dataset containing information related to all genes in release.	required
feature_name	str	The name of the feature	required
genomic_window	int	The maximum window size to consider	required

Returns:

Name	Type	Description
DataFrame	DataFrame	Feature dataset, with 1 if the gene is protein-coding, 0 if not.

Source code in src/gentropy/dataset/l2g_features/other.py

def is_protein_coding_feature_logic(
    study_loci_to_annotate: StudyLocus | L2GGoldStandard,
    *,
    gene_index: GeneIndex,
    feature_name: str,
    genomic_window: int,
) -> DataFrame:
    """Computes the feature to indicate if a gene is protein-coding or not.

    Args:
        study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci
            that will be used for annotation
        gene_index (GeneIndex): Dataset containing information related to all genes in release.
        feature_name (str): The name of the feature
        genomic_window (int): The maximum window size to consider

    Returns:
        DataFrame: Feature dataset, with 1 if the gene is protein-coding, 0 if not.
    """
    study_loci_window = (
        study_loci_to_annotate.df.withColumn(
            "window_start", f.col("position") - (genomic_window / 2)
        )
        .withColumn("window_end", f.col("position") + (genomic_window / 2))
        .withColumnRenamed("chromosome", "SL_chromosome")
    )
    return (
        study_loci_window.join(
            gene_index.df.alias("genes"),
            on=(
                (f.col("SL_chromosome") == f.col("genes.chromosome"))
                & (f.col("genes.tss") >= f.col("window_start"))
                & (f.col("genes.tss") <= f.col("window_end"))
            ),
            how="inner",
        )
        .withColumn(
            feature_name,
            f.when(f.col("biotype") == "protein_coding", f.lit(1)).otherwise(f.lit(0)),
        )
        .select("studyLocusId", "geneId", feature_name)
        .distinct()
    )

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• 2024-10-24
• 2024-10-25
• Contributors
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