acore.correlation_analysis package#

class CorrelationCoefficient(coefficient, pvalue)[source]#

Bases: NamedTuple

coefficient: float#: Alias for field number 0

pvalue: float#: Alias for field number 1

count(value, /)#: Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)#

Return first index of value.

Raises ValueError if the value is not present.

corr_lower_triangle(df: DataFrame, **kwargs) → DataFrame[source]#: Compute the correlation matrix, returning only unique values (lower triangle). Passes kwargs to [pandas.DataFrame.corr](pandas.DataFrame.corr) method.

calculate_correlations(x, y, method='pearson')[source]#

Calculates a Spearman (nonparametric) or a Pearson (parametric) correlation coefficient and p-value to test for non-correlation.

Parameters:

x (numpy.ndarray) – array 1
y (numpy.ndarray) – array 2
method (str) – chooses which kind of correlation method to run

Returns:

Tuple with two floats, correlation coefficient and two-tailed p-value.

Example:

result = calculate_correlations(x, y, method='pearson')

run_correlation(df, alpha=0.05, subject=None, group='group', method='pearson', correction='fdr_bh', numeric_only=True, dropna=True)[source]#

This function calculates pairwise correlations for columns in dataframe, and returns it in the shape of a edge list with ‘weight’ as correlation score, and the ajusted p-values.

Parameters:

df – pandas dataframe with samples as rows and features as columns.
subject (str) – name of column containing subject identifiers.
group (str) – name of column containing group identifiers.
method (str) – method to use for correlation calculation (‘pearson’, ‘spearman’).
alpha (float) – error rate. Values velow alpha are considered significant.
correction (str) – type of correction see apply_pvalue_correction for methods
numeric_only (bool) – if True, only numeric columns are considered for correlation calculation.
dropna (bool) – if True, columns with NaN values are dropped before correlation calculation.

Returns:

Pandas dataframe with columns: ‘node1’, ‘node2’, ‘weight’, ‘padj’ and ‘rejected’.

Example:

result = run_correlation(df, alpha=0.05, subject='subject', group='group',
            method='pearson', correction='fdr_bh')

run_multi_correlation(df_dict, alpha=0.05, subject='subject', on=['subject', 'biological_sample'], group='group', method='pearson', correction='fdr_bh')[source]#

This function merges all input dataframes and calculates pairwise correlations for all columns.

Parameters:

df_dict (dict) – dictionary of pandas dataframes with samples as rows and features as columns.
subject (str) – name of the column containing subject identifiers.
group (str) – name of the column containing group identifiers.
on (list) – column names to join dataframes on (must be found in all dataframes).
method (str) – method to use for correlation calculation (‘pearson’, ‘spearman’).
alpha (float) – error rate. Values velow alpha are considered significant.
correction (str) – type of correction see apply_pvalue_correction for methods

Returns:

Pandas dataframe with columns: ‘node1’, ‘node2’, ‘weight’, ‘padj’ and ‘rejected’.

Example:

result = run_multi_correlation(df_dict, alpha=0.05, subject='subject',
    on=['subject', 'biological_sample'],
    group='group', method='pearson', correction='fdr_bh')

calculate_rm_correlation(df, x, y, subject)[source]#

Computes correlation and p-values between two columns a and b in df with repeated measures (rm).

Parameters:

df – pandas dataframe with subjects as rows and two features and columns.
x (str) – feature a name.
y (str) – feature b name.
subject – column name containing the covariate variable.

Returns:

Tuple with values for: feature a, feature b, correlation, p-value and degrees of freedom.

Example:

result = calculate_rm_correlation(df, x='feature a', y='feature b', subject='subject')

run_rm_correlation(df, alpha=0.05, subject='subject', correction='fdr_bh')[source]#

Computes pairwise repeated measurements correlations for all columns in dataframe, and returns results as an edge list with ‘weight’ as correlation score, p-values, degrees of freedom and ajusted p-values.

Parameters:

df – pandas dataframe with samples as rows and features as columns.
subject (str) – name of column containing subject identifiers.
alpha (float) – error rate. Values velow alpha are considered significant.
correction (str) – type of correction type see apply_pvalue_correction for methods

Returns:

Pandas dataframe with columns: ‘node1’, ‘node2’, ‘weight’, ‘pvalue’, ‘dof’, ‘padj’ and ‘rejected’.

Example:

result = run_rm_correlation(df, alpha=0.05, subject='subject', correction='fdr_bh')

calculate_pvalue_correlation_old(r: DataFrame, n_obs: int) → DataFrame[source]#

Calculate p-values for Pearson correlation using a all values from the correlation matrix.

Parameters:

r (pd.DataFrame) – Correlation matrix.
n_obs (int) – Number of observations used to calculate the correlation matrix (assumes no missing values).

Returns:

p-value matrix assuming fixed number of observations.

Return type:

pd.DataFrame

calculate_pvalue_correlation(r, n_obs)[source]#

Calculate p-values for Pearson correlation using a all values from the correlation matrix.

Tested against Pearson correlation using a all values from the correlation matrix, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html

Parameters:

r (pd.DataFrame) – Correlation matrix.
n_obs (int) – Number of observations used to calculate the correlation matrix (assumes no missing values).

Returns:

p-value matrix assuming fixed number of observations.

Return type:

pd.DataFrame

run_efficient_correlation(data, method='pearson')[source]#

Calculates pairwise correlations and returns lower triangle of the matrix with correlation values and p-values. For pearson correlation, p-values are calculated assuming a fixed number of observations.

Parameters:

data – pandas dataframe with samples as index and features as columns (numeric data only).
method (str) – method to use for correlation calculation (‘pearson’, ‘spearman’).

Returns:

Two numpy arrays: correlation and p-values.

Example:

result = run_efficient_correlation(data, method='pearson')

acore.correlation_analysis package

Contents

acore.correlation_analysis package#