acore.exploratory_analysis package#

get_histogram_series(s: Series, bins: ndarray) → Series[source]#

calculate_coefficient_variation(df: DataFrame) → Series[source]#

Compute the coefficient of variation (CV) for each column in a DataFrame.

The coefficient of variation is defined as the ratio of the standard deviation to the mean, expressed as a percentage. This function uses the biased standard deviation (normalization by N) as implemented in scipy.stats.variation.

Parameters:: df (pandas.DataFrame) – Input DataFrame containing numeric values (e.g., log2-transformed data). Each column will be processed independently.
Returns:: Series containing the coefficient of variation (in percent) for each column. The index corresponds to the columns of the input DataFrame.
Return type:: pandas.Series

See also

scipy.stats.variation: Function used to compute the coefficient of variation.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> calculate_coefficient_variation(df)
A   40.825
B   16.330
Name: coef_of_var, dtype: float64

calculate_coef_of_var_and_mean(df: DataFrame) → DataFrame[source]#

Calculate coefficient of variation and mean for each column in the dataframe, the mean calculated on both log2 and linear scale.

Parameters:: df (pd.DataFrame) – The input dataframe containing the linear values (non-log transformed).
Returns:: A dataframe with columns ‘mean_log2’, ‘mean’ and ‘coef_of_var’ for each column in the input dataframe.
Return type:: pd.DataFrame

get_coefficient_variation(data: DataFrame, drop_columns: list[str] | None = None, group: str = 'group')[source]#

Extracts the coefficients of variation in each group.

Parameters:

data – pandas.DataFrame with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’). The values should be the original intensities for massspectrometry-based measurements.
drop_columns (list) – column labels to be dropped from the dataframe
group (str) – column label containing group identifiers.

Returns:

Pandas dataframe with columns ‘name’ (protein identifier), ‘x’ (coefficient of variation), ‘y’ (mean) and ‘group’.

Example:

result = get_coefficient_variation(data, drop_columns=['sample', 'subject'], group='group')

extract_number_missing(data, min_valid, drop_cols=['sample'], group='group')[source]#

Counts how many valid values exist in each column and filters column labels with more valid values than the minimum threshold defined.

Parameters:

data – pandas DataFrame with group as rows and protein identifier as column.
group (str) – column label containing group identifiers. If None, number of valid values is counted across all samples, otherwise is counted per unique group identifier.
min_valid (int) – minimum number of valid values to be filtered.
drop_columns (list) – column labels to be dropped.

Returns:

List of column labels above the threshold.

Example:

result = extract_number_missing(data, min_valid=3, drop_cols=['sample'], group='group')

extract_percentage_missing(data, missing_max, drop_cols=['sample'], group='group', how='all')[source]#

Extracts ratio of missing/valid values in each column and filters column labels with lower ratio than the minimum threshold defined.

Parameters:

data – pandas dataframe with group as rows and protein identifier as column.
group (str) – column label containing group identifiers. If None, ratio is calculated across all samples, otherwise is calculated per unique group identifier.
missing_max (float) – maximum ratio of missing/valid values to be filtered.
how (str) – define if labels with a higher percentage of missing values than the threshold in any group (‘any’) or in all groups (‘all’) should be filtered

Returns:

List of column labels below the threshold.

Example::

result = extract_percentage_missing(data, missing_max=0.3,: drop_cols=[‘sample’], group=’group’)

run_pca(data, drop_cols=['sample', 'subject'], group='group', annotation_cols=['sample'], components=2, dropna=True)[source]#

Performs principal component analysis and returns the values of each component for each sample and each protein, and the loadings for each protein.

For information visit https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.

Parameters:

data – pandas dataframe with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’).
drop_cols (list) – column labels to be dropped from the dataframe.
group (str) – column label containing group identifiers.
annotation_cols (list) – list of columns to be added in the scatter plot annotation
components (int) – number of components to keep.
dropna (bool) – if True removes all columns with any missing values.

Returns:

tuple: 1) three pandas dataframes: components, loadings and variance; 2) xaxis and yaxis titles with components loadings for plotly.

Example:

result = run_pca(data, drop_cols=['sample', 'subject'], group='group',
                 components=2, dropna=True)

run_tsne(data, drop_cols=['sample', 'subject'], group='group', annotation_cols=['sample'], components=2, perplexity=40, max_iter=1000, init='pca', dropna=True)[source]#

Performs t-distributed Stochastic Neighbor Embedding analysis.

For more information visit https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html.

Parameters:

data – pandas dataframe with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’).
drop_cols (list) – column labels to be dropped from the dataframe.
group (str) – column label containing group identifiers.
components (int) – dimension of the embedded space.
annotation_cols (list) – list of columns to be added in the scatter plot annotation
perplexity (int) – related to the number of nearest neighbors that is used in other manifold learning algorithms. Consider selecting a value between 5 and 50.
max_iter (int) – maximum number of iterations for the optimization (at least 250).
init (str) – initialization of embedding (‘random’, ‘pca’ or numpy array of shape n_samples x n_components).
dropna (bool) – if True removes all columns with any missing values.

Returns:

Two dictionaries: 1) pandas dataframe with embedding vectors, 2) xaxis and yaxis titles for plotly.

Example:

result = run_tsne(data,
                  drop_cols=['sample', 'subject'],
                  group='group',
                  components=2,
                  perplexity=40,
                  max_iter=1000,
                  init='pca',
                  dropna=True
                )

run_umap(data, drop_cols=['sample', 'subject'], group='group', annotation_cols=['sample'], n_neighbors=10, min_dist=0.3, metric='cosine', dropna=True)[source]#

Performs Uniform Manifold Approximation and Projection.

For more information vist https://umap-learn.readthedocs.io.

Parameters:

data – pandas dataframe with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’).
drop_cols (list) – column labels to be dropped from the dataframe.
group (str) – column label containing group identifiers.
annotation_cols (list) – list of columns to be added in the scatter plot annotation
n_neighbors (int) – number of neighboring points used in local approximations of manifold structure.
min_dist (float) – controls how tightly the embedding is allowed compress points together.
metric (str) – metric used to measure distance in the input space.
dropna (bool) – if True removes all columns with any missing values.

Returns:

Two dictionaries: 1) pandas dataframe with embedding of the training data in low-dimensional space, 2) xaxis and yaxis titles for plotly.

Example:

result = run_umap(data,
                  drop_cols=['sample', 'subject'],
                  group='group',
                  n_neighbors=10,
                  min_dist=0.3,
                  metric='cosine',
                  dropna=True
                )

acore.exploratory_analysis package

Contents

acore.exploratory_analysis package#