🐛 fix various type annotation bugs (intersphinx linking)

need to be valid types (typos) or module.objects (missing module name)

acore/correlation_analysis.py

@@ -12,8 +12,8 @@ def calculate_correlations(x, y, method='pearson'):
     """
     Calculates a Spearman (nonparametric) or a Pearson (parametric) correlation coefficient and p-value to test for non-correlation.
 
-    :param ndarray x: array 1
-    :param ndarray y: array 2
+    :param numpy.ndarray x: array 1
+    :param numpy.ndarray y: array 2
     :param str method: chooses which kind of correlation method to run
     :return: Tuple with two floats, correlation coefficient and two-tailed p-value.
 
@@ -37,8 +37,8 @@ def run_correlation(df, alpha=0.05, subject='subject', group='group', method='pe
     :param str subject: name of column containing subject identifiers.
     :param str group: name of column containing group identifiers.
     :param str method: method to use for correlation calculation ('pearson', 'spearman').
-    :param floar alpha: error rate. Values velow alpha are considered significant.
-    :param string correction: type of correction see apply_pvalue_correction for methods
+    :param float alpha: error rate. Values velow alpha are considered significant.
+    :param str correction: type of correction see apply_pvalue_correction for methods
     :return: Pandas dataframe with columns: 'node1', 'node2', 'weight', 'padj' and 'rejected'.
 
     Example::
@@ -83,7 +83,7 @@ def run_multi_correlation(df_dict, alpha=0.05, subject='subject', on=['subject',
     :param list on: column names to join dataframes on (must be found in all dataframes).
     :param str method: method to use for correlation calculation ('pearson', 'spearman').
     :param float alpha: error rate. Values velow alpha are considered significant.
-    :param string correction: type of correction see apply_pvalue_correction for methods
+    :param str correction: type of correction see apply_pvalue_correction for methods
     :return: Pandas dataframe with columns: 'node1', 'node2', 'weight', 'padj' and 'rejected'.
 
     Example::
@@ -131,7 +131,7 @@ def run_rm_correlation(df, alpha=0.05, subject='subject', correction='fdr_bh'):
     :param df: pandas dataframe with samples as rows and features as columns.
     :param str subject: name of column containing subject identifiers.
     :param float alpha: error rate. Values velow alpha are considered significant.
-    :param string correction: type of correction type see apply_pvalue_correction for methods
+    :param str correction: type of correction type see apply_pvalue_correction for methods
     :return: Pandas dataframe with columns: 'node1', 'node2', 'weight', 'pvalue', 'dof', 'padj' and 'rejected'.
 
     Example::

acore/differential_regulation.py

@@ -438,7 +438,7 @@ def run_repeated_measurements_anova(
 
     :param df: pandas dataframe with samples as rows and protein identifiers as columns (with additional columns 'group', 'sample' and 'subject').
     :param str subject: column with subject identifiers
-    :param srt within: column with within factor identifiers
+    :param str within: column with within factor identifiers
     :param list drop_cols: column labels to be dropped from the dataframe
     :param float alpha: error rate for multiple hypothesis correction
     :param int permutations: number of permutations used to estimate false discovery rates
@@ -488,8 +488,8 @@ def run_mixed_anova(
 
     :param df: pandas dataframe with samples as rows and protein identifiers as columns (with additional columns 'group', 'sample' and 'subject').
     :param str subject: column with subject identifiers
-    :param srt within: column with within factor identifiers
-    :param srt between: column with between factor identifiers
+    :param str within: column with within factor identifiers
+    :param str between: column with between factor identifiers
     :param list drop_cols: column labels to be dropped from the dataframe
     :param float alpha: error rate for multiple hypothesis correction
     :param int permutations: number of permutations used to estimate false discovery rates

acore/exploratory_analysis.py

@@ -12,9 +12,9 @@ def calculate_coefficient_variation(values):
     deviation to the mean, in percentage. For more information
     visit https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.variation.html.
 
-    :param ndarray values: numpy array
+    :param numpy.ndarray values: numpy array
     :return: The calculated variation along rows.
-    :rtype: ndarray
+    :rtype: numpy.ndarray
 
     Example::
 

acore/imputation_analysis.py

@@ -12,7 +12,7 @@ def imputation_KNN(data, drop_cols=['group', 'sample', 'subject'], group='group'
     :param str group: column label containing group identifiers.
     :param list drop_cols: column labels to be dropped. Final dataframe should only have gene/protein/etc identifiers as columns.
     :param float cutoff: minimum ratio of missing/valid values required to impute in each column.
-    :param boolean alone: if True removes all columns with any missing values.
+    :param bool alone: if True removes all columns with any missing values.
     :return: Pandas dataframe with samples as rows and protein identifiers as columns.
 
     Example::

acore/multiple_testing.py

@@ -10,7 +10,7 @@ def apply_pvalue_correction(pvalues, alpha=0.05, method='bonferroni'):
     """
     Performs p-value correction using the specified method. For more information visit https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html.
 
-    :param ndarray pvalues: et of p-values of the individual tests.
+    :param numpy.ndarray pvalues: et of p-values of the individual tests.
     :param float alpha: error rate.
     :param str method: method of p-value correction:
         - bonferroni : one-step correction
@@ -42,7 +42,7 @@ def apply_pvalue_fdrcorrection(pvalues, alpha=0.05, method='indep'):
     """
     Performs p-value correction for false discovery rate. For more information visit https://www.statsmodels.org/devel/generated/statsmodels.stats.multitest.fdrcorrection.html.
 
-    :param ndarray pvalues: et of p-values of the individual tests.
+    :param numpy.ndarray pvalues: et of p-values of the individual tests.
     :param float alpha: error rate.
     :param str method: method of p-value correction ('indep', 'negcorr').
     :return: Tuple with two arrays, boolen for rejecting H0 hypothesis and float for adjusted p-value.
@@ -60,7 +60,7 @@ def apply_pvalue_twostage_fdrcorrection(pvalues, alpha=0.05, method='bh'):
     """
     Iterated two stage linear step-up procedure with estimation of number of true hypotheses. For more information visit https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.fdrcorrection_twostage.html.
 
-    :param ndarray pvalues: et of p-values of the individual tests.
+    :param numpy.ndarray pvalues: et of p-values of the individual tests.
     :param float alpha: error rate.
     :param str method: method of p-value correction ('bky', 'bh').
     :return: Tuple with two arrays, boolen for rejecting H0 hypothesis and float for adjusted p-value.
@@ -139,7 +139,7 @@ def get_counts_permutation_fdr(value, random, observed, n, alpha):
     Calculates local FDR values (q-values) by computing the fraction of accepted hits from the permuted data over accepted hits from the measured data normalized by the total number of permutations.
 
     :param float value: computed p-value on measured data for a feature.
-    :param ndarray random: p-values computed on the permuted data.
+    :param numpy.ndarray random: p-values computed on the permuted data.
     :param observed: pandas Series with p-values calculated on the originally measured data.
     :param int n: number of permutations to be applied.
     :param float alpha: error rate. Values velow alpha are considered significant.

acore/normalization_analysis.py

@@ -56,7 +56,7 @@ def normalize_data(data, method='median', normalize=None):
     This function normalizes the data using the selected method
 
     :param data: DataFrame with the data to be normalized (samples x features)
-    :param string method: normalization method to choose among: median_polish, median,
+    :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.