relabel_chr_column

Given a data_df with column chr, a curr_chr_name_convention and a new_chr_name_convention, that are both columns of chrmap_df, join the chrmap to the data_df based on the curr_chr_name_convention and swap the values in the chr column to the new_chr_name_convention. relabel the new_chr_name_convention to chr and return the dataframe with columns in the same order as the input dataframe. If geonmic_only is set to True, only those chromosomes with type == ‘genomic’ are returned.

:param df: The dataframe to relabel. :type df: pd.DataFrame :param curr_chr_name_convention: The current chromosome name convention. :type curr_chr_name_convention: str :param new_chr_name_convention: The new chromosome name convention. :type new_chr_name_convention: str :param genomic_only: Whether to return only records with type == “genomic”. :type genomic_only: bool

:return: The relabeled dataframe. :rtype: pd.DataFrame

:raises ValueError: If the curr_chr_name_convention or new_chr_name_convention are not columns in chrmap_df.

:Example:

import pandas as pd data_df = pd.DataFrame({‘chr’: [‘chr1’, ‘chr2’, ‘chr3’], … ‘start’: [1, 2, 3],}) chrmap_df = pd.DataFrame({‘curr_chr_name_convention’: … [‘chr1’, ‘chr2’, ‘chr3’], … ‘new_chr_name_convention’: … [‘chrI’, ‘chrII’, ‘chrIII’]}) relabeled_df = relabel_chr_column(data_df, chrmap_df, … ‘curr_chr_name_convention’, … ‘new_chr_name_convention’) list(relabeled_df.columns) == [‘chr’, ‘start’] True list(relabeled_df[‘chr’]) == [‘chrI’, ‘chrII’, ‘chrIII’] True

Source code in callingcardstools/PeakCalling/yeast/read_in_data.py

def relabel_chr_column(
    data_df: pd.DataFrame,
    chrmap_df: pd.DataFrame,
    curr_chr_name_convention: str,
    new_chr_name_convention: str,
    genomic_only: bool = True,
) -> pd.DataFrame:
    """
    Given a `data_df` with column `chr`, a `curr_chr_name_convention` and
    a `new_chr_name_convention`, that are both columns of `chrmap_df`, join
    the `chrmap` to the `data_df` based on the `curr_chr_name_convention` and
    swap the values in the `chr` column to the `new_chr_name_convention`.
    relabel the `new_chr_name_convention` to `chr` and return the dataframe
    with columns in the same order as the input dataframe. If `geonmic_only` is
    set to `True`, only those chromosomes with type == 'genomic' are returned.

    :param df: The dataframe to relabel.
    :type df: pd.DataFrame
    :param curr_chr_name_convention: The current chromosome name convention.
    :type curr_chr_name_convention: str
    :param new_chr_name_convention: The new chromosome name convention.
    :type new_chr_name_convention: str
    :param genomic_only: Whether to return only records with type == "genomic".
    :type genomic_only: bool

    :return: The relabeled dataframe.
    :rtype: pd.DataFrame

    :raises ValueError: If the `curr_chr_name_convention` or
        `new_chr_name_convention` are not columns in `chrmap_df`.

    :Example:

    >>> import pandas as pd
    >>> data_df = pd.DataFrame({'chr': ['chr1', 'chr2', 'chr3'],
    ...                         'start': [1, 2, 3],})
    >>> chrmap_df = pd.DataFrame({'curr_chr_name_convention':
    ...                            ['chr1', 'chr2', 'chr3'],
    ...                           'new_chr_name_convention':
    ...                            ['chrI', 'chrII', 'chrIII']})
    >>> relabeled_df = relabel_chr_column(data_df, chrmap_df,
    ...                                   'curr_chr_name_convention',
    ...                                   'new_chr_name_convention')
    >>> list(relabeled_df.columns) == ['chr', 'start']
    True
    >>> list(relabeled_df['chr']) == ['chrI', 'chrII', 'chrIII']
    True
    """
    # check input
    if "chr_curr" in chrmap_df.columns:
        raise ValueError(
            "chr_curr cannot be a column in chrmap_df for the "
            "purposes of relabelling. rename that column in "
            "chrmap_df and resubmit"
        )
    if curr_chr_name_convention not in chrmap_df.columns:
        raise ValueError("curr_chr_name_convention " "must be a column in chrmap_df")
    if new_chr_name_convention not in chrmap_df.columns:
        raise ValueError("new_chr_name_convention " "must be a column in chrmap_df")

    # rename the current chr column to chr_curr to avoid any errors in
    # joining, if the old/new format is called 'chr'
    data_df = data_df.rename(columns={"chr": "chr_curr"})

    if curr_chr_name_convention != new_chr_name_convention:
        # join a subset of chrmap_df -- only the columns we need -- to data_df
        data_df = data_df.merge(
            chrmap_df[[curr_chr_name_convention, new_chr_name_convention, "type"]],
            left_on="chr_curr",
            right_on=curr_chr_name_convention,
        )
        # swap values in chr column
        data_df["chr_curr"] = data_df[new_chr_name_convention]
    else:
        data_df = data_df.merge(
            chrmap_df[[curr_chr_name_convention, "type"]],
            left_on="chr_curr",
            right_on=curr_chr_name_convention,
        )

    if genomic_only:
        data_df = data_df.query("type=='genomic'")

    return data_df.drop(
        columns=[new_chr_name_convention, curr_chr_name_convention, "type"]
    ).rename(columns={"chr_curr": "chr"})