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create_rank_response_table

Create a rank repsonse table from a dictionary which contains the configuration parameters. See docs at https://cmatkhan.github.io/callingCardsTools/file_format_specs/yeast_rank_response/ # noqa for details

Parameters:

Name Type Description Default
config_dict dict

A dictionary containing the configuration parameters

 required

Returns:

Name Type Description
tuple (DataFrame, DataFrame, DataFrame)

A tuple containing three DataFrames (see rank_response_summarize): 1. The input with repsonsive genes labeled and the random expectation column added 2. The random expectation DataFrame 3. The Rank Response DataFrame

Raises:

Type Description
KeyError

if the configuration dictionary is missing any of the required keys
FileExistsError

if the data files do not exist
AttributeError

if there are NA values in the effect or pvalue columns
ValueError

if there are incomplete cases in the data
Source code in callingcardstools/Analysis/yeast/rank_response/create_rank_response_table.py 
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def create_rank_response_table(config_dict: dict) -> (pd.DataFrame,
                                                      pd.DataFrame,
                                                      pd.DataFrame):
    """
    Create a rank repsonse table from a dictionary which contains the
    configuration parameters. See docs at
    https://cmatkhan.github.io/callingCardsTools/file_format_specs/yeast_rank_response/ # noqa
    for details

    Args:
        config_dict (dict): A dictionary containing the configuration
            parameters

    Returns:
        tuple: A tuple containing three DataFrames
            (see rank_response_summarize):
               1. The input with `repsonsive` genes labeled and the random
                    expectation column added
               2. The random expectation DataFrame
               3. The Rank Response DataFrame

    Raises:
        KeyError: if the configuration dictionary is missing any of the
            required keys
        FileExistsError: if the data files do not exist
        AttributeError: if there are NA values in the effect or pvalue columns
        ValueError: if there are incomplete cases in the data
    """
    # validate the configuration key/value pairs
    args = validate_config(config_dict)
    # read i the binding data
    try:
        binding_data = read_in_data(
            args['binding_data_path'],
            args['binding_identifier_col'],
            args['binding_effect_col'],
            args['binding_pvalue_col'],
            args['binding_source'],
            'binding')
    except (KeyError, FileExistsError, AttributeError) as exc:
        logger.error("Error reading in binding data: %s", exc)
        raise

    # read in the expression data
    try:
        expression_data = read_in_data(
            args['expression_data_path'],
            args['expression_identifier_col'],
            args['expression_effect_col'],
            args['expression_pvalue_col'],
            args['expression_source'],
            'expression')
    except (KeyError, FileExistsError, AttributeError) as exc:
        logger.error("Error reading in expression data: %s", exc)
        raise

    df = expression_data.merge(binding_data[['binding_effect',
                                             'binding_pvalue',
                                             'binding_source',
                                             'feature']],
                               how='inner',
                               on='feature')
    # test that there no incomplete cases. raise an error if there are
    if df.isnull().values.any():
        raise ValueError("There are incomplete cases in the data")

    logger.info('There are %s genes in the data after merging '
                'the %s binding data and '
                ' %s expression data',
                str(df.shape[0]),
                args['binding_source'],
                args['expression_source'])

    try:
        # the first two items in the return tuple aren't passed out of
        # this function, hence _, _
        _, _, rank_response_df = rank_response_ratio_summarize(
            df,
            effect_expression_thres=args['expression_effect_thres'],
            p_expression_thres=args['expression_pvalue_thres'],
            normalize=args['normalize'],
            bin_size=args['rank_bin_size'])
    except (KeyError) as exc:
        logger.error("Error summarizing data: %s", exc)
        raise

    return rank_response_df