acore.differential_regulation.tests module

acore.differential_regulation.tests module#

All the tests for differential regulation. Functions used in the user facing function starting with run_.

calc_means_between_groups(df: DataFrame, boolean_array: Series, event_names: tuple[str, str] = ('1', '0')) → DataFrame[source]#

Mean comparison between groups

calc_ttest(df: DataFrame, boolean_array: Series, variables: list[str]) → DataFrame[source]#

Calculate t-test for each variable in variables between two groups defined by boolean array.

calculate_ttest(df, condition1, condition2, paired=False, is_logged=True, non_par=False, tail='two-sided', correction='auto', r=0.707)[source]#

Calculates the t-test for the means of independent samples belonging to two different groups using scipy.stats.ttest_ind.

Parameters:

  • df – pandas dataframe with groups and subjects as rows and protein identifier as column.

  • condition1 (str) – identifier of first group.

  • condition2 (str) – ientifier of second group.

  • is_logged (bool) – data is logged transformed

  • non_par (bool) – if True, normality and variance equality assumptions are checked and non-parametric test Mann Whitney U test if not passed

Returns:

Tuple with t-statistics, two-tailed p-value, mean of first group, mean of second group and logfc.

Example:

result = calculate_ttest(df, 'group1', 'group2')
calculate_thsd(df, column, group='group', alpha=0.05, is_logged=True)[source]#

Pairwise Tukey-HSD posthoc test using pingouin.pairwise_tukey.

Parameters:

  • df – pandas dataframe with group and protein identifier as columns

  • column (str) – column containing the protein identifier

  • group (str) – column label containing the between factor

  • alpha (float) – significance level

Returns:

Pandas dataframe.

Example:

result = calculate_thsd(df, column='HBG2~P69892', group='group', alpha=0.05)
calculate_pairwise_ttest(df, column, subject='subject', group='group', correction='none', is_logged=True)[source]#

Performs pairwise t-test using pingouin, as a posthoc test, and calculates fold-changes using pingouin.pairwise_ttests.

Parameters:

  • df – pandas dataframe with subject and group as rows and protein identifier as column.

  • column (str) – column label containing the dependant variable

  • subject (str) – column label containing subject identifiers

  • group (str) – column label containing the between factor

  • correction (str) – method used for testing and adjustment of p-values.

Returns:

Pandas dataframe with means, standard deviations, test-statistics, degrees of freedom and effect size columns.

Example:

result = calculate_pairwise_ttest(df,
                                  'protein a',
                                  subject='subject',
                                  group='group',
                                  correction='none'
        )
complement_posthoc(posthoc, identifier, is_logged)[source]#

Calculates fold-changes after posthoc test.

Parameters:

  • posthoc – pandas dataframe from posthoc test. Should have at least columns ‘mean(group1)’ and ‘mean(group2)’.

  • identifier (str) – feature identifier.

Returns:

Pandas dataframe with additional columns ‘identifier’, ‘log2FC’ and ‘FC’.

calculate_anova(df, column, group='group')[source]#

Calculates one-way ANOVA using pingouin.

Parameters:

  • df – pandas dataframe with group as rows and protein identifier as column

  • column (str) – name of the column in df to run ANOVA on

  • group (str) – column with group identifiers

Returns:

Tuple with t-statistics and p-value.

calculate_ancova(data, column, group='group', covariates=[])[source]#

Calculates one-way ANCOVA using pingouin.

Parameters:

  • df – pandas dataframe with group as rows and protein identifier as column

  • column (str) – name of the column in df to run ANOVA on

  • group (str) – column with group identifiers

  • covariates (list) – list of covariates (columns in df)

Returns:

Tuple with column, F-statistics and p-value.

calculate_repeated_measures_anova(df, column, subject='subject', within='group')[source]#

One-way and two-way repeated measures ANOVA using pingouin stats.

Parameters:

  • df – pandas dataframe with samples as rows and protein identifier as column. Data must be in long-format for two-way repeated measures.

  • column (str) – column label containing the dependant variable

  • subject (str) – column label containing subject identifiers

  • within (str) – column label containing the within factor

Returns:

Tuple with protein identifier, t-statistics and p-value.

Example:

result = calculate_repeated_measures_anova(df,
                                          'protein a',
                                          subject='subject',
                                          within='group'
        )
calculate_mixed_anova(df, column, subject='subject', within='group', between='group2')[source]#

One-way and two-way repeated measures ANOVA using pingouin stats.

Parameters:

  • df – pandas dataframe with samples as rows and protein identifier as column. Data must be in long-format for two-way repeated measures.

  • column (str) – column label containing the dependant variable

  • subject (str) – column label containing subject identifiers

  • within (str) – column label containing the within factor

  • within – column label containing the between factor

Returns:

Tuple with protein identifier, t-statistics and p-value.

Example:

result = calculate_mixed_anova(df,
                               'protein a',
                               subject='subject',
                               within='group',
                               between='group2'
        )
pairwise_ttest_with_covariates(df, column, group, covariates, is_logged)[source]#

Pairwise t-test with covariates using statsmodels.

format_anova_table(df, aov_results, pairwise_results, pairwise_cols, group, permutations, alpha, correction)[source]#

Performs p-value correction (permutation-based and FDR) and converts pandas dataframe into final format.

Parameters:

  • df – pandas dataframe with samples as rows and protein identifiers as columns (with additional columns ‘group’, ‘sample’ and ‘subject’).

  • aov_results (list[tuple]) – list of tuples with anova results (one tuple per feature).

  • pairwise_results (list[dataframes]) – list of pandas dataframes with posthoc tests results

  • group (str) – column with group identifiers

  • alpha (float) – error rate for multiple hypothesis correction

  • permutations (int) – number of permutations used to estimate false discovery rates

Returns:

Pandas dataframe

calculate_pvalue_from_tstats(tstat, dfn)[source]#

Calculate two-tailed p-values from T- or F-statistics.

tstat: T/F distribution dfn: degrees of freedrom n (values) per protein (keys),

i.e. number of obervations - number of groups (dict)

eta_squared(aov)[source]#

Calculates the effect size using Eta-squared.

Parameters:

aov – pandas dataframe with anova results from statsmodels.

Returns:

Pandas dataframe with additional Eta-squared column.

omega_squared(aov)[source]#

Calculates the effect size using Omega-squared.

Parameters:

aov – pandas dataframe with anova results from statsmodels.

Returns:

Pandas dataframe with additional Omega-squared column.