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chipexo_promoter_sig

Find the promoter signature of the chipexo data. This is calculated as the most significant peak in each promoter region.

Parameters:

Name Type Description Default
chipexo_data_path str

path to the chipexo allevents file.

 required
chipexo_orig_chr_convention str

chromosome convention of the chipexo allevents file.

 required
promoter_data_path str

path to the promoter data file.

 required
promoter_orig_chr_convention str

chromosome convention of the promoter data file.

 required
chrmap_data_path str

path to the chromosome map file.

 required
unified_chr_convention str

chromosome convention to convert to.

 required

Returns:

Type Description
DataFrame

pandas.DataFrame: A pandas DataFrame containing the promoter signature of the chipexo data.
Example

import pandas as pd import tempfile

Create temporary chipexo data file

with tempfile.NamedTemporaryFile(mode=’w+’, … suffix=’.tsv’) as chipexo_file: … _ = chipexo_file.write(‘chr\tcoord\tYPD_log2Fold\t’ … ‘ YPD_log2P\nchr1\t150\t2.0\t0.05\n’)

Create temporary promoter data file

with tempfile.NamedTemporaryFile(mode=’w+’, … suffix=’.tsv’) as promoter_file: … _ = promoter_file.write(‘chr\tstart\tend\t’ … ‘associated_feature\nchr1\t100\t’ … ‘200\tpromoter1\n’)

Create temporary chromosome map file

with tempfile.NamedTemporaryFile(mode=’w+’, … suffix=’.tsv’) as chrmap_file: … - = chrmap_file.write(‘chr\tucsc\nchr1\tchr1\n’)

Call the function

result = chipexo_promoter_sig(chipexo_file.name, ‘chr’, … promoter_file.name, ‘chr’, … chrmap_file.name, ‘ucsc’) isinstance(result, pd.DataFrame) True

Source code in callingcardstools/Analysis/yeast/chipexo_promoter_sig.py 
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def chipexo_promoter_sig(chipexo_data_path: str,
                         chipexo_orig_chr_convention: str,
                         promoter_data_path: str,
                         promoter_orig_chr_convention: str,
                         chrmap_data_path: str,
                         unified_chr_convention: str) -> pd.DataFrame:
    """
    Find the promoter signature of the chipexo data. This is calculated as
    the most significant peak in each promoter region.

    Args:
        chipexo_data_path (str): path to the chipexo allevents file.
        chipexo_orig_chr_convention (str): chromosome convention of the
            chipexo allevents file.
        promoter_data_path (str): path to the promoter data file.
        promoter_orig_chr_convention (str): chromosome convention of the
            promoter data file.
        chrmap_data_path (str): path to the chromosome map file.
        unified_chr_convention (str): chromosome convention to convert to.

    Returns:
        pandas.DataFrame: A pandas DataFrame containing the promoter
            signature of the chipexo data.

    Example:
        >>> import pandas as pd
        >>> import tempfile
        >>> # Create temporary chipexo data file
        >>> with tempfile.NamedTemporaryFile(mode='w+',
        ...                                  suffix='.tsv') as chipexo_file:
        ...     _ = chipexo_file.write('chr\\tcoord\\tYPD_log2Fold\\t'
        ...                        ' YPD_log2P\\nchr1\\t150\\t2.0\\t0.05\\n')
        >>> # Create temporary promoter data file
        >>> with tempfile.NamedTemporaryFile(mode='w+',
        ...                                  suffix='.tsv') as promoter_file:
        ...     _ = promoter_file.write('chr\\tstart\\tend\\t'
        ...                         'associated_feature\\nchr1\\t100\\t'
        ...                         '200\\tpromoter1\\n')
        >>> # Create temporary chromosome map file
        >>> with tempfile.NamedTemporaryFile(mode='w+',
        ...                                  suffix='.tsv') as chrmap_file:
        ...     - = chrmap_file.write('chr\\tucsc\\nchr1\\tchr1\\n')
        >>> # Call the function
        >>> result = chipexo_promoter_sig(chipexo_file.name, 'chr',
        ...                               promoter_file.name, 'chr',
        ...                               chrmap_file.name, 'ucsc')
        >>> isinstance(result, pd.DataFrame)
        True
    """
    # read in chrmap data
    chrmap_df = read_in_chrmap(chrmap_data_path,
                               {chipexo_orig_chr_convention,
                                promoter_orig_chr_convention,
                                unified_chr_convention})
    # read in promoter data
    promoter_df = read_in_promoter_data(promoter_data_path,
                                        promoter_orig_chr_convention,
                                        unified_chr_convention,
                                        chrmap_df)
    # read in chipexo data
    chipexo_df = read_in_chipexo_data(chipexo_data_path,
                                      chipexo_orig_chr_convention,
                                      unified_chr_convention,
                                      chrmap_df)

    # Step 1: Inner Join
    return pd.merge(promoter_df, chipexo_df,
                    on='chr',
                    how='inner')\
        .query('start <= chipexo_start <= end')\
        .groupby(['chr', 'start', 'end', 'name', 'strand'])\
        .agg(
        n_sig_peaks=pd.NamedAgg(column='chr',
                                aggfunc='count'),
        max_fc=pd.NamedAgg(column='YPD_log2Fold',
                           aggfunc='max'),
        min_pval=pd.NamedAgg(column='YPD_log2P',
                             aggfunc='min'))\
        .reset_index()