pyrnaither.normalization.normalizer module

pyrnaither.normalization.normalizer.b_score(header, dataset, args)

Perform B-score normalization on the dataset.

Return type:

Tuple[list, DataFrame]

Parameters:

• header (list)

• dataset (DataFrame)

• args (list)

Args:

header: The header of the dataset. dataset: The dataset to perform B-score normalization on. args: The arguments for the B-score normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.control_norm(header, dataset, args)

Perform control normalization on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform control normalization on. args: The arguments for the control normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.div_norm(header, dataset, args)

Perform division normalization on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform division normalization on. args: The arguments for the division normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.lowess_norm(header, dataset, args)

Perform lowess normalization on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform lowess normalization on. args: The arguments for the lowess normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.med_abs_dev(data)

Median absolute deviation. MAD = median(|X_i - median(X)|)

Return type:

float

Parameters:

data (ndarray)

pyrnaither.normalization.normalizer.median_polish(data, n_iter=10)

Return type:

Dict[str, float | ndarray[tuple[Any, ...], dtype[float]]]

Parameters:

• data (ndarray)

• n_iter (int)

Performs median polish on a 2-D array Args:

data: input 2-D array

Returns:

a dict, with:

ave: μ col: column effect row: row effect r: cell residue

pyrnaither.normalization.normalizer.quantile_normalization(header, dataset, args)

Perform quantile normalization on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform quantile normalization on. args: The arguments for the quantile normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.save_dataset(header, data, data_set_file)

Save the dataset to a file.

Return type:

None

Parameters:

• header (List[str])

• data (DataFrame)

• data_set_file (str)

Args:

header: The header of the dataset. data: The dataset to save. data_set_file: The file to save the dataset to.

pyrnaither.normalization.normalizer.save_old_intensity_columns(dataset, col4val)

Save the old intensity columns in the dataset.

Return type:

DataFrame

Parameters:

• dataset (DataFrame)

• col4val (str)

Args:

dataset: The dataset to save the old intensity columns in. col4val: The column name to save the old intensity columns in.

Returns:

The dataset with the old intensity columns saved.

pyrnaither.normalization.normalizer.subtract_background(header, dataset, args)

Perform background subtraction on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform background subtraction on. args: The arguments for the background subtraction.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.var_adjust(header, dataset, args)

Perform variance adjustment on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform variance adjustment on. args: The arguments for the variance adjustment.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.z_score(header, dataset, args)

Perform Z-score normalization on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform Z-score normalization on. args: The arguments for the Z-score normalization.

Returns:

A tuple containing the updated header and dataset.

pyrnaither.normalization.normalizer.z_score_per_screen(header, dataset, args)

Perform Z-score normalization per screen on the dataset.

Return type:

Tuple[List[str], DataFrame]

Parameters:

• header (List[str])

• dataset (DataFrame)

• args (List[Any])

Args:

header: The header of the dataset. dataset: The dataset to perform Z-score normalization per screen on. args: The arguments for the Z-score normalization per screen.

Returns:

A tuple containing the updated header and dataset.