Skip to content

Commit

Permalink
🎨 typehint on top level functions and linelenght formatting
Browse files Browse the repository at this point in the history
  • Loading branch information
@enryH
enryH committed Oct 14, 2024
1 parent c964d62 commit 11e8bfc
Showing 1 changed file with 65 additions and 35 deletions.
100 changes: 65 additions & 35 deletions acore/normalization_analysis.py
View file
Original file line number Diff line number Diff line change
@@ -1,17 +1,29 @@
import pandas as pd
from combat.pycombat import pycombat

# from combat.pycombat import pycombat
from sklearn import preprocessing


def combat_batch_correction(data, batch_col, index_cols):
def combat_batch_correction(
data: pd.DataFrame,
batch_col: str,
index_cols: list[str],
) -> pd.DataFrame:
"""
This function corrects processed data for batch effects. For more information visit: https://pypi.org/project/pycombat/
:param data: pandas dataframe with samples as rows and protein identifiers as columns.
This function corrects processed data for batch effects. For more information visit:
https://pypi.org/project/pycombat/
:param data: pandas.DataFrame with samples as rows and protein identifiers as columns.
:param batch_col: column with the batch identifiers
:param index_cols: list of columns that don't need to be corrected (i.e group)
:return: pandas dataframe with samples as rows and protein identifiers as columns.
:return: pandas.DataFrame with samples as rows and protein identifiers as columns.
Example::
result = combat_batch_correction(data, batch_col='batch', index_cols=['subject', 'sample', 'group'])
result = combat_batch_correction(
data,
batch_col="batch",
index_cols=["subject", "sample", "group"],
)
"""
df_corrected = pd.DataFrame()
index_cols = [c for c in index_cols if c != batch_col]
Expand All @@ -30,16 +42,21 @@ def combat_batch_correction(data, batch_col, index_cols):
return df_corrected


def normalize_data_per_group(data, group, method="median", normalize=None):
def normalize_data_per_group(
data: pd.DataFrame,
group: str | int | list[str | int],
method: str = "median",
normalize: str = None,
) -> pd.DataFrame:
"""
This function normalizes the data by group using the selected method
:param data: DataFrame with the data to be normalized (samples x features)
:param group_col: Column containing the groups
:param group_col: Column containing the groups, passed to pandas.DataFrame.groupby
:param str method: normalization method to choose among: median_polish, median,
quantile, linear
:param str normalize: whether the normalization should be done by 'features' (columns) or 'samples' (rows) (default None)
:return: Pandas dataframe.
:return: pandas.DataFrame.
Example::
Expand All @@ -53,15 +70,21 @@ def normalize_data_per_group(data, group, method="median", normalize=None):
return ndf


def normalize_data(data, method="median", normalize=None):
def normalize_data(
data: pd.DataFrame,
method: str = "median",
normalize: str = None,
):
"""
This function normalizes the data using the selected method
This function normalizes the data using the selected method. Normalizes only nummeric
data, but keeps the non-numeric columns in the output DataFrame.
:param data: DataFrame with the data to be normalized (samples x features)
:param str method: normalization method to choose among: median_polish, median,
quantile, linear
:param str normalize: whether the normalization should be done by 'features' (columns) or 'samples' (rows) (default None)
:return: Pandas dataframe.
:param str method: normalization method to choose among: median (default),
median_polish, median_zero, quantile, linear, zscore
:param str normalize: whether the normalization should be done by 'features' (columns)
or 'samples' (rows) (default None)
:return: pandas.DataFrame.
Example::
Expand Down Expand Up @@ -97,8 +120,9 @@ def median_zero_normalization(data, normalize="samples"):
This function normalizes each sample by using its median.
:param data:
:param str normalize: whether the normalization should be done by 'features' (columns) or 'samples' (rows)
:return: Pandas dataframe.
:param str normalize: whether the normalization should be done by 'features' (columns)
or 'samples' (rows)
:return: pandas.DataFrame.
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
Expand All @@ -118,14 +142,15 @@ def median_zero_normalization(data, normalize="samples"):

return normData


# ! Update docstring
def median_normalization(data, normalize="samples"):
"""
This function normalizes each sample by using its median.
:param data:
:param str normalize: whether the normalization should be done by 'features' (columns) or 'samples' (rows)
:return: Pandas dataframe.
:param str normalize: whether the normalization should be done by 'features' (columns)
or 'samples' (rows)
:return: pandas.DataFrame.
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
Expand All @@ -148,11 +173,13 @@ def median_normalization(data, normalize="samples"):

def zscore_normalization(data, normalize="samples"):
"""
This function normalizes each sample by using its mean and standard deviation (mean=0, std=1).
This function normalizes each sample by using its mean and standard deviation
(mean=0, std=1).
:param data:
:param str normalize: whether the normalization should be done by 'features' (columns) or 'samples' (rows)
:return: Pandas dataframe.
:param str normalize: whether the normalization should be done by 'features' (columns)
or 'samples' (rows)
:return: pandas.DataFrame.
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
Expand Down Expand Up @@ -181,7 +208,7 @@ def median_polish_normalization(data, max_iter=250):
:param data:
:param int max_iter: number of maximum iterations to prevent infinite loop.
:return: Pandas dataframe.
:return: pandas.DataFrame.
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
Expand Down Expand Up @@ -210,12 +237,12 @@ def median_polish_normalization(data, max_iter=250):
return normData


def quantile_normalization(data):
def quantile_normalization(data) -> pd.DataFrame:
"""
Applies quantile normalization to each column in pandas dataframe.
Applies quantile normalization to each column in pandas.DataFrame.
:param data: pandas dataframe with features as columns and samples as rows.
:return: Pandas dataframe
:param data: pandas.DataFrame with features as columns and samples as rows.
:return: pandas.DataFrame
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
Expand All @@ -236,18 +263,21 @@ def quantile_normalization(data):
return normdf


def linear_normalization(data, method="l1", normalize="samples"):
def linear_normalization(data, method="l1", normalize="samples") -> pd.DataFrame:
"""
This function scales input data to a unit norm. For more information visit https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html.
This function scales input data to a unit norm. For more information visit:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html
:param data: pandas dataframe with samples as rows and features as columns.
:param str method: norm to use to normalize each non-zero sample or non-zero feature (depends on axis).
:param str normalize: axis used to normalize the data along. If 'samples', independently normalize each sample, if 'features' normalize each feature.
:return: Pandas dataframe
:param data: pandas.DataFrame with samples as rows and features as columns.
:param str method: norm to use to normalize each non-zero sample or non-zero feature
(depends on axis).
:param str normalize: axis used to normalize the data along. If 'samples',
independently normalize each sample, if 'features' normalize each feature.
:return: pandas.DataFrame
Example::
data = pd.DataFrame({'a': [2,5,4,3,3], 'b':[4,4,6,5,3], 'c':[4,14,8,8,9]})
result = linear_normalization(data, method = "l1", by = 'feature')
result = linear_normalization(data, method = "l1", by = 'samples')
result
a b c
0 0.117647 0.181818 0.093023
Expand Down

0 comments on commit 11e8bfc

Please sign in to comment.