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EPIraction

gentropy.datasource.intervals.epiraction.IntervalsEpiraction

Interval dataset from EPIraction.

Source code in src/gentropy/datasource/intervals/epiraction.py 
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class IntervalsEpiraction:
    """Interval dataset from EPIraction."""

    PMID: ClassVar[str] = "40027634"

    @staticmethod
    def read(spark: SparkSession, path: str) -> DataFrame:
        """Read EPIraction dataset (tsv with header).

        Args:
            spark (SparkSession): Spark session
            path (str): Path to the EPIraction dataset (tsv files)

        Returns:
            DataFrame: Raw EPIraction DataFrame.
        """
        return (
            spark.read.option("delimiter", "\t")
            .option("mode", "DROPMALFORMED")
            .option("header", "true")
            .csv(path)
        )

    @classmethod
    def parse(
        cls: type[IntervalsEpiraction],
        raw_epiraction_df: DataFrame,
        target_index: TargetIndex,
    ) -> Intervals:
        """Parse EPIraction Intervals.

        Args:
            raw_epiraction_df (DataFrame): Raw EPIraction DataFrame.
            target_index (TargetIndex): Target index.

        Returns:
            Intervals: Parsed Intervals dataset.
        """
        if "#chr" in raw_epiraction_df.columns:
            chr_col = "#chr"
        else:
            chr_col = "chr"
        base = (
            raw_epiraction_df.withColumn(
                "studyId",
                f.regexp_extract(f.input_file_name(), r"([^/]+)\.bed\.gz$", 1),
            )
            .withColumn("chromosome", normalize_chromosome(f.col(chr_col)))
            .withColumn("start", f.col("start").cast("long"))
            .withColumn("end", f.col("end").cast("long"))
            .withColumnRenamed("TargetGeneEnsemblID", "geneId")
            .withColumnRenamed("CellType", "biosampleName")
            .withColumnRenamed("Score", "score")
            .withColumn("score", f.col("score").cast("double"))
            .withColumnRenamed("class", "intervalType")
            .withColumn("intervalType", f.lower(f.trim(f.col("intervalType"))))
            .withColumn(
                "resourceScore",
                f.array(
                    f.struct(
                        f.lit("H3K27ac").alias("name"),
                        f.col("H3K27ac").cast("float").alias("value"),
                    ),
                    f.struct(
                        f.lit("Open").alias("name"),
                        f.col("Open").cast("float").alias("value"),
                    ),
                    f.struct(
                        f.lit("Cofactor").alias("name"),
                        f.col("Cofactor").cast("float").alias("value"),
                    ),
                    f.struct(
                        f.lit("CTCF").alias("name"),
                        f.col("CTCF").cast("float").alias("value"),
                    ),
                    f.struct(
                        f.lit("HiC_contacts").alias("name"),
                        f.col("HiC_contacts").cast("float").alias("value"),
                    ),
                    f.struct(
                        f.lit("abc_tissue").alias("name"),
                        f.col("abc_tissue").cast("float").alias("value"),
                    ),
                ),
            )
        )
        tss_lut = target_index.tss_lut()

        joined = base.alias("iv").join(tss_lut.alias("ti"), on="geneId", how="left")
        parsed = (
            joined.withColumn(
                "distanceToTss",
                Intervals.distance_to_tss(
                    f.col("iv.start"),
                    f.col("iv.end"),
                    f.col("iv.intervalType"),
                    f.col("ti.tss"),
                ),
            )
            .withColumn(
                "intervalId",
                Intervals.generate_identifier(Intervals.id_cols),
            )
            .withColumn("qualityControls", f.array().cast("array<string>"))
        )

        return Intervals(
            _df=(
                parsed.select(
                    f.col("chromosome"),
                    f.col("start"),
                    f.col("end"),
                    f.col("geneId"),
                    f.col("score"),
                    f.col("distanceToTss"),
                    f.col("resourceScore"),
                    f.lit(IntervalDataSource.EPIRACTION.value).alias("datasourceId"),
                    f.col("intervalType"),
                    f.lit(cls.PMID).alias("pmid"),
                    f.col("biosampleName"),
                    f.lit(None).cast(t.StringType()).alias("biosampleFromSourceId"),
                    f.lit(None).cast(t.StringType()).alias("biosampleId"),
                    f.col("studyId"),
                    f.col("intervalId"),
                    f.col("qualityControls"),
                )
            ),
            _schema=Intervals.get_schema(),
        )

parse(raw_epiraction_df: DataFrame, target_index: TargetIndex) -> Intervals classmethod

Parse EPIraction Intervals.

Parameters:

Name Type Description Default
raw_epiraction_df DataFrame

Raw EPIraction DataFrame.

 required
target_index TargetIndex

Target index.

 required

Returns:

Name Type Description
Intervals Intervals

Parsed Intervals dataset.
Source code in src/gentropy/datasource/intervals/epiraction.py 
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@classmethod
def parse(
    cls: type[IntervalsEpiraction],
    raw_epiraction_df: DataFrame,
    target_index: TargetIndex,
) -> Intervals:
    """Parse EPIraction Intervals.

    Args:
        raw_epiraction_df (DataFrame): Raw EPIraction DataFrame.
        target_index (TargetIndex): Target index.

    Returns:
        Intervals: Parsed Intervals dataset.
    """
    if "#chr" in raw_epiraction_df.columns:
        chr_col = "#chr"
    else:
        chr_col = "chr"
    base = (
        raw_epiraction_df.withColumn(
            "studyId",
            f.regexp_extract(f.input_file_name(), r"([^/]+)\.bed\.gz$", 1),
        )
        .withColumn("chromosome", normalize_chromosome(f.col(chr_col)))
        .withColumn("start", f.col("start").cast("long"))
        .withColumn("end", f.col("end").cast("long"))
        .withColumnRenamed("TargetGeneEnsemblID", "geneId")
        .withColumnRenamed("CellType", "biosampleName")
        .withColumnRenamed("Score", "score")
        .withColumn("score", f.col("score").cast("double"))
        .withColumnRenamed("class", "intervalType")
        .withColumn("intervalType", f.lower(f.trim(f.col("intervalType"))))
        .withColumn(
            "resourceScore",
            f.array(
                f.struct(
                    f.lit("H3K27ac").alias("name"),
                    f.col("H3K27ac").cast("float").alias("value"),
                ),
                f.struct(
                    f.lit("Open").alias("name"),
                    f.col("Open").cast("float").alias("value"),
                ),
                f.struct(
                    f.lit("Cofactor").alias("name"),
                    f.col("Cofactor").cast("float").alias("value"),
                ),
                f.struct(
                    f.lit("CTCF").alias("name"),
                    f.col("CTCF").cast("float").alias("value"),
                ),
                f.struct(
                    f.lit("HiC_contacts").alias("name"),
                    f.col("HiC_contacts").cast("float").alias("value"),
                ),
                f.struct(
                    f.lit("abc_tissue").alias("name"),
                    f.col("abc_tissue").cast("float").alias("value"),
                ),
            ),
        )
    )
    tss_lut = target_index.tss_lut()

    joined = base.alias("iv").join(tss_lut.alias("ti"), on="geneId", how="left")
    parsed = (
        joined.withColumn(
            "distanceToTss",
            Intervals.distance_to_tss(
                f.col("iv.start"),
                f.col("iv.end"),
                f.col("iv.intervalType"),
                f.col("ti.tss"),
            ),
        )
        .withColumn(
            "intervalId",
            Intervals.generate_identifier(Intervals.id_cols),
        )
        .withColumn("qualityControls", f.array().cast("array<string>"))
    )

    return Intervals(
        _df=(
            parsed.select(
                f.col("chromosome"),
                f.col("start"),
                f.col("end"),
                f.col("geneId"),
                f.col("score"),
                f.col("distanceToTss"),
                f.col("resourceScore"),
                f.lit(IntervalDataSource.EPIRACTION.value).alias("datasourceId"),
                f.col("intervalType"),
                f.lit(cls.PMID).alias("pmid"),
                f.col("biosampleName"),
                f.lit(None).cast(t.StringType()).alias("biosampleFromSourceId"),
                f.lit(None).cast(t.StringType()).alias("biosampleId"),
                f.col("studyId"),
                f.col("intervalId"),
                f.col("qualityControls"),
            )
        ),
        _schema=Intervals.get_schema(),
    )

read(spark: SparkSession, path: str) -> DataFrame staticmethod

Read EPIraction dataset (tsv with header).

Parameters:

Name Type Description Default
spark SparkSession

Spark session

 required
path str

Path to the EPIraction dataset (tsv files)

 required

Returns:

Name Type Description
DataFrame DataFrame

Raw EPIraction DataFrame.
Source code in src/gentropy/datasource/intervals/epiraction.py 
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@staticmethod
def read(spark: SparkSession, path: str) -> DataFrame:
    """Read EPIraction dataset (tsv with header).

    Args:
        spark (SparkSession): Spark session
        path (str): Path to the EPIraction dataset (tsv files)

    Returns:
        DataFrame: Raw EPIraction DataFrame.
    """
    return (
        spark.read.option("delimiter", "\t")
        .option("mode", "DROPMALFORMED")
        .option("header", "true")
        .csv(path)
    )
  • 2025-10-28
  • 2025-10-31
  • Contributors