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pileup

CoolerBedGraph

Generates a bedgraph file from a cooler file created by interactions-store.

Attributes:

Name Type Description
cooler Cooler

Cooler file to use for bedgraph production
capture_name str

Name of capture probe being processed.
sparse bool

Only output bins with interactions.
only_cis bool

Only output cis interactions.
Source code in capcruncher/api/pileup.py 
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class CoolerBedGraph:
    """Generates a bedgraph file from a cooler file created by interactions-store.

    Attributes:
     cooler (cooler.Cooler): Cooler file to use for bedgraph production
     capture_name (str): Name of capture probe being processed.
     sparse (bool): Only output bins with interactions.
     only_cis (bool): Only output cis interactions.

    """

    def __init__(
        self,
        uri: str,
        sparse: bool = True,
        only_cis: bool = False,
        region_to_limit: str = None,
    ):
        """
        Args:
            uri (str): Path to cooler group in hdf5 file.
            sparse (bool, optional): Only output non-zero bins. Defaults to True.
        """
        self._sparse = sparse
        self._only_cis = only_cis

        logger.info(f"Reading {uri}")
        self._cooler = cooler.Cooler(uri)
        self.viewpoint_name = self._cooler.info["metadata"]["viewpoint_name"]
        self._viewpoint_bins = self._cooler.info["metadata"]["viewpoint_bins"]

        if len(self._viewpoint_bins) > 1:
            self.multiple_viewpoint_bins = True
            logger.warning(
                f"Viewpoint {self.viewpoint_name} has multiple bins! {self._viewpoint_bins}. Proceed with caution!"
            )
        else:
            self.multiple_viewpoint_bins = False

        self.viewpoint_chroms = self._cooler.info["metadata"]["viewpoint_chrom"]
        self.n_cis_interactions = self._cooler.info["metadata"]["n_cis_interactions"]
        logger.info(f"Processing {self.viewpoint_name}")

        if only_cis:
            pixels = []
            bins = []
            for chrom in self.viewpoint_chroms:
                _bins = self._cooler.bins().fetch(chrom)
                viewpoint_chrom_bins = self._bins["name"]
                _pixels = (
                    self._cooler.pixels()
                    .fetch(self.viewpoint_chroms)
                    .query(
                        "(bin1_id in @viewpoint_chrom_bins) and (bin2_id in @viewpoint_chrom_bins)"
                    )
                )
                _bins = self._cooler.bins().fetch(chrom)

                pixels.append(_pixels)
                bins.append(_bins)

            self._pixels = pd.concat(pixels)
            self._bins = pd.concat(bins)

        elif region_to_limit:
            self._pixels = self._cooler.pixels().fetch(region_to_limit)
            self._bins = self._cooler.bins().fetch(region_to_limit)

        else:
            self._pixels = self._cooler.pixels()[:]
            # TODO: Avoid this if possible as reading all bins into memory
            self._bins = self._cooler.bins()[:]

        # Ensure name column is present
        self._bins = (
            self._bins.assign(name=lambda df: df.index)
            if "name" not in self._bins.columns
            else self._bins
        )
        self._reporters = None

    def _get_reporters(self):
        logger.info("Extracting reporters")
        concat_ids = pd.concat([self._pixels["bin1_id"], self._pixels["bin2_id"]])
        concat_ids_filt = concat_ids.loc[lambda ser: ser.isin(self._viewpoint_bins)]
        pixels = self._pixels.loc[concat_ids_filt.index]

        df_interactions = pd.DataFrame()
        df_interactions["capture"] = np.where(
            pixels["bin1_id"].isin(self._viewpoint_bins),
            pixels["bin1_id"],
            pixels["bin2_id"],
        )

        df_interactions["reporter"] = np.where(
            pixels["bin1_id"].isin(self._viewpoint_bins),
            pixels["bin2_id"],
            pixels["bin1_id"],
        )

        df_interactions["count"] = pixels["count"].values

        return df_interactions.sort_values(["capture", "reporter"]).reset_index(
            drop=True
        )

    def extract_bedgraph(
        self, normalisation: Literal["raw", "n_cis", "region"] = "raw", **norm_kwargs
    ) -> pd.DataFrame:
        logger.info("Generating bedgraph")
        df_bdg = (
            self._bins.merge(
                self.reporters,
                left_on="name",
                right_on="reporter",
                how="inner" if self._sparse else "outer",
            )[["chrom", "start", "end", "count"]]
            .assign(count=lambda df: df["count"].fillna(0))
            .sort_values(["chrom", "start"])
        )

        # TODO: This is a hack to deal with multiple bins for a viewpoint
        if self.multiple_viewpoint_bins:
            gr_bdg = pr.PyRanges(
                df_bdg.rename(
                    columns={"chrom": "Chromosome", "start": "Start", "end": "End"}
                )
            )

            df_bdg = (
                gr_bdg.cluster()
                .df.groupby("Cluster")
                .agg(
                    {
                        "count": "sum",
                        "Start": "min",
                        "End": "max",
                        "Chromosome": "first",
                    }
                )
                .reset_index()
                .rename(
                    columns={"Start": "start", "End": "end", "Chromosome": "chrom"}
                )[["chrom", "start", "end", "count"]]
            )

        if not normalisation == "raw":
            logger.info("Normalising bedgraph")
            self._normalise_bedgraph(df_bdg, method=normalisation, **norm_kwargs)

        return df_bdg

    @property
    def reporters(self) -> pd.DataFrame:
        """Interactions with capture fragments/bins.

        Returns:
         pd.DataFrame: DataFrame containing just bins interacting with the capture probe.
        """

        if self._reporters is not None:
            return self._reporters
        else:
            self._reporters = self._get_reporters()
            return self._reporters

    def _normalise_bedgraph(
        self, bedgraph, scale_factor=1e6, method: str = "n_cis", region: str = None
    ) -> pd.DataFrame:
        """Normalises the bedgraph (in place).

        Uses the number of cis interactions to normalise the bedgraph counts.

        Args:
         scale_factor (int, optional): Scaling factor for normalisation. Defaults to 1e6.

        Returns:
         pd.DataFrame: Normalised bedgraph formatted DataFrame
        """

        if method == "raw":
            pass
        elif method == "n_cis":
            self._normalise_by_n_cis(bedgraph, scale_factor)
        elif method == "region":
            self._normalise_by_regions(bedgraph, scale_factor, region)

    def _normalise_by_n_cis(self, bedgraph, scale_factor: float):
        bedgraph["count"] = (bedgraph["count"] / self.n_cis_interactions) * scale_factor

    def _normalise_by_regions(self, bedgraph, scale_factor: float, regions: str):
        if not is_valid_bed(regions):
            raise ValueError(
                "A valid bed file is required for region based normalisation"
            )

        df_viewpoint_norm_regions = pd.read_csv(
            regions, sep="\t", names=["chrom", "start", "end", "name"]
        )
        df_viewpoint_norm_regions = df_viewpoint_norm_regions.loc[
            lambda df: df["name"].str.contains(self.viewpoint_name)
        ]

        counts_in_regions = []
        for region in df_viewpoint_norm_regions.itertuples():
            counts_in_regions.append(
                bedgraph.query(
                    "(chrom == @region.chrom) and (start >= @region.start) and (start <= @region.end)"
                )
            )

        df_counts_in_regions = pd.concat(counts_in_regions)
        total_counts_in_region = df_counts_in_regions["count"].sum()

        bedgraph["count"] = (bedgraph["count"] / total_counts_in_region) * scale_factor

    def to_pyranges(
        self, normalisation: Literal["raw", "n_cis", "region"] = "raw", **norm_kwargs
    ):
        return pr.PyRanges(
            self.extract_bedgraph(
                normalisation=normalisation, norm_kwargs=norm_kwargs
            ).rename(columns={"chrom": "Chromosome", "start": "Start", "end": "End"})
        )

reporters property

Interactions with capture fragments/bins.

Returns:

Type Description
DataFrame

pd.DataFrame: DataFrame containing just bins interacting with the capture probe.

__init__(uri, sparse=True, only_cis=False, region_to_limit=None)

Parameters:

Name Type Description Default
uri str

Path to cooler group in hdf5 file.

 required
sparse bool

Only output non-zero bins. Defaults to True.

 True
Source code in capcruncher/api/pileup.py 
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def __init__(
    self,
    uri: str,
    sparse: bool = True,
    only_cis: bool = False,
    region_to_limit: str = None,
):
    """
    Args:
        uri (str): Path to cooler group in hdf5 file.
        sparse (bool, optional): Only output non-zero bins. Defaults to True.
    """
    self._sparse = sparse
    self._only_cis = only_cis

    logger.info(f"Reading {uri}")
    self._cooler = cooler.Cooler(uri)
    self.viewpoint_name = self._cooler.info["metadata"]["viewpoint_name"]
    self._viewpoint_bins = self._cooler.info["metadata"]["viewpoint_bins"]

    if len(self._viewpoint_bins) > 1:
        self.multiple_viewpoint_bins = True
        logger.warning(
            f"Viewpoint {self.viewpoint_name} has multiple bins! {self._viewpoint_bins}. Proceed with caution!"
        )
    else:
        self.multiple_viewpoint_bins = False

    self.viewpoint_chroms = self._cooler.info["metadata"]["viewpoint_chrom"]
    self.n_cis_interactions = self._cooler.info["metadata"]["n_cis_interactions"]
    logger.info(f"Processing {self.viewpoint_name}")

    if only_cis:
        pixels = []
        bins = []
        for chrom in self.viewpoint_chroms:
            _bins = self._cooler.bins().fetch(chrom)
            viewpoint_chrom_bins = self._bins["name"]
            _pixels = (
                self._cooler.pixels()
                .fetch(self.viewpoint_chroms)
                .query(
                    "(bin1_id in @viewpoint_chrom_bins) and (bin2_id in @viewpoint_chrom_bins)"
                )
            )
            _bins = self._cooler.bins().fetch(chrom)

            pixels.append(_pixels)
            bins.append(_bins)

        self._pixels = pd.concat(pixels)
        self._bins = pd.concat(bins)

    elif region_to_limit:
        self._pixels = self._cooler.pixels().fetch(region_to_limit)
        self._bins = self._cooler.bins().fetch(region_to_limit)

    else:
        self._pixels = self._cooler.pixels()[:]
        # TODO: Avoid this if possible as reading all bins into memory
        self._bins = self._cooler.bins()[:]

    # Ensure name column is present
    self._bins = (
        self._bins.assign(name=lambda df: df.index)
        if "name" not in self._bins.columns
        else self._bins
    )
    self._reporters = None

_normalise_bedgraph(bedgraph, scale_factor=1000000.0, method='n_cis', region=None)

Normalises the bedgraph (in place).

Uses the number of cis interactions to normalise the bedgraph counts.

Parameters:

Name Type Description Default
scale_factor int

Scaling factor for normalisation. Defaults to 1e6.

 1000000.0

Returns:

Type Description
DataFrame

pd.DataFrame: Normalised bedgraph formatted DataFrame
Source code in capcruncher/api/pileup.py 
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def _normalise_bedgraph(
    self, bedgraph, scale_factor=1e6, method: str = "n_cis", region: str = None
) -> pd.DataFrame:
    """Normalises the bedgraph (in place).

    Uses the number of cis interactions to normalise the bedgraph counts.

    Args:
     scale_factor (int, optional): Scaling factor for normalisation. Defaults to 1e6.

    Returns:
     pd.DataFrame: Normalised bedgraph formatted DataFrame
    """

    if method == "raw":
        pass
    elif method == "n_cis":
        self._normalise_by_n_cis(bedgraph, scale_factor)
    elif method == "region":
        self._normalise_by_regions(bedgraph, scale_factor, region)