Classes
Normalization
A collection of lightweight normalization utilities (PQN, SNV, MSC and their combinations). Methods accept either pandas DataFrames or array-like inputs and always return DataFrames.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 100)) >>> norm_spectra = Normalization.pqn_normalization(spectra) >>> norm_spectra.shape (10, 100)
Methods
pqn_normalization(df: Union[pd.DataFrame, np.ndarray])
Probabilistic Quotient Normalization (PQN).
Args:
dfSpectral data with samples as rows and variables as columns.Returns: PQN-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.pqn_normalization(spectra) >>> norm_spectra.shape (10, 50)
snv_normalization(df: Union[pd.DataFrame, np.ndarray])
Standard Normal Variate (column-wise mean centering and scaling).
Args:
dfSpectral data with samples as rows and variables as columns.Returns: SNV-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.snv_normalization(spectra) >>> norm_spectra.shape (10, 50)
msc_normalization(df: Union[pd.DataFrame, np.ndarray])
Multiplicative Scatter Correction.
Args:
dfSpectral data with samples as rows and variables as columns.Returns: MSC-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.msc_normalization(spectra) >>> norm_spectra.shape (10, 50)
snv_msc_normalization(df: Union[pd.DataFrame, np.ndarray])
Apply SNV followed by MSC-style column centering.
Args:
dfSpectral data with samples as rows and variables as columns.Returns: SNV+MSC-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.snv_msc_normalization(spectra) >>> norm_spectra.shape (10, 50)
snv_pqn_normalization(df: Union[pd.DataFrame, np.ndarray])
Apply SNV followed by PQN normalization.
Args:
dfSpectral data with samples as rows and variables as columns.Returns: SNV+PQN-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.snv_pqn_normalization(spectra) >>> norm_spectra.shape (10, 50)
snv_msc_pqn_normalization(df: Union[pd.DataFrame, np.ndarray])
Apply SNV, MSC-style centering, then PQN.
Args:
dfSpectral data with samples as rows and variables as columns.Returns: SNV+MSC+PQN-normalized DataFrame of the same shape.
Examples: >>> import numpy as np >>> import pandas as pd >>> from metbit.preprocessing.normalize import Normalization >>> spectra = pd.DataFrame(np.random.rand(10, 50)) >>> norm_spectra = Normalization.snv_msc_pqn_normalization(spectra) >>> norm_spectra.shape (10, 50)