proteometer.stats
=================

.. py:module:: proteometer.stats


Classes
-------

.. autoapisummary::

   proteometer.stats.TTestGroup


Functions
---------

.. autoapisummary::

   proteometer.stats.recalculate_adj_pval
   proteometer.stats.recalculate_adj_pval_proteinwise
   proteometer.stats.log2_transformation
   proteometer.stats.anova
   proteometer.stats.pairwise_ttest
   proteometer.stats.calculate_pairwise_scalars
   proteometer.stats.calculate_all_pairwise_scalars


Module Contents
---------------

.. py:class:: TTestGroup

   .. py:attribute:: treat_group
      :type:  str


   .. py:attribute:: control_group
      :type:  str


   .. py:attribute:: treat_samples
      :type:  collections.abc.Sequence[str]


   .. py:attribute:: control_samples
      :type:  collections.abc.Sequence[str]


   .. py:method:: label()


.. py:function:: recalculate_adj_pval(df: pandas.DataFrame, comparisons: list[str])

   Recalculates adjusted p-values for specified comparisons.

   :param df: DataFrame containing p-values and adjusted p-values.
   :type df: pd.DataFrame
   :param comparisons: List of comparison names. Each comparison
                       should have a p-value and adjusted p-value indicated by a "_pval" and
                       "_adj-p" suffix, respectively.
   :type comparisons: list[str]

   :returns: DataFrame with recalculated adjusted p-values.
   :rtype: pd.DataFrame


.. py:function:: recalculate_adj_pval_proteinwise(df: pandas.DataFrame, comparisons: list[str], protein_col: str = 'Protein')

   Recalculates adjusted p-values for specified comparisons, computed protein-wise.

   See:
   - Schopper et al. Nature Protocols, 12(11):2391-2410, October 2017.
   - Nagel et al. Cellular Proteomics, 24(4):100934, April 2025.

   :param df: DataFrame containing p-values and adjusted p-values.
   :type df: pd.DataFrame
   :param comparisons: List of comparison names. Each comparison
                       should have a p-value and adjusted p-value indicated by a "_pval" and
                       "_adj-p" suffix, respectively.
   :type comparisons: list[str]

   :returns: DataFrame with recalculated adjusted p-values.
   :rtype: pd.DataFrame


.. py:function:: log2_transformation(df2transform: pandas.DataFrame, int_cols: collections.abc.Sequence[str]) -> pandas.DataFrame

   Applies log2 transformation to specified intensity columns in a DataFrame.

   :param df2transform: DataFrame containing the data to transform.
   :type df2transform: pd.DataFrame
   :param int_cols: List of intensity column names to apply the transformation.
   :type int_cols: Sequence[str]

   :returns: DataFrame with log2-transformed intensity columns.
   :rtype: pd.DataFrame


.. py:function:: anova(df: pandas.DataFrame, anova_cols: list[str], metadata: pandas.DataFrame, anova_factors: collections.abc.Sequence[str], sample_col: str) -> pandas.DataFrame

   Performs ANOVA on specified columns of a DataFrame.

   :param df: DataFrame containing the data for analysis.
   :type df: pd.DataFrame
   :param anova_cols: List of column names to analyze.
   :type anova_cols: list[str]
   :param metadata: Metadata containing sample information.
   :type metadata: pd.DataFrame
   :param anova_factors: Factors for ANOVA analysis.
   :type anova_factors: Sequence[str], optional
   :param sample_col: Column name for sample identifiers.
   :type sample_col: str, optional

   :returns: DataFrame with ANOVA p-values and adjusted p-values.
   :rtype: pd.DataFrame


.. py:function:: pairwise_ttest(df: pandas.DataFrame, pairwise_ttest_groups: collections.abc.Iterable[TTestGroup]) -> pandas.DataFrame

   Performs pairwise t-tests for specified treatment and control groups.

   :param df: DataFrame containing the data for analysis.
   :type df: pd.DataFrame
   :param pairwise_ttest_groups: Iterable of TTestGroup objects defining the groups.
   :type pairwise_ttest_groups: Iterable[TTestGroup]

   :returns: DataFrame with t-test results, including p-values and adjusted p-values.
   :rtype: pd.DataFrame


.. py:function:: calculate_pairwise_scalars(prot: pandas.DataFrame, pairwise_ttest_name: str | None = None, sig_type: str = 'pval', sig_thr: float = 0.05) -> pandas.DataFrame

   Calculates pairwise scalars based on significance thresholds.

   :param prot: DataFrame containing pairwise t-test results.
   :type prot: pd.DataFrame
   :param pairwise_ttest_name: Name of the pairwise t-test column. Defaults to None.
   :type pairwise_ttest_name: str | None, optional
   :param sig_type: Type of significance metric (e.g., "pval"). Defaults to "pval".
   :type sig_type: str, optional
   :param sig_thr: Significance threshold. Defaults to 0.05.
   :type sig_thr: float, optional

   :returns: DataFrame with calculated scalars.
   :rtype: pd.DataFrame


.. py:function:: calculate_all_pairwise_scalars(prot: pandas.DataFrame, pairwise_ttest_groups: collections.abc.Iterable[TTestGroup], sig_type: str = 'pval', sig_thr: float = 0.05) -> pandas.DataFrame

   Calculates pairwise scalars for all specified t-test groups.

   :param prot: DataFrame containing pairwise t-test results.
   :type prot: pd.DataFrame
   :param pairwise_ttest_groups: Iterable of TTestGroup objects defining the groups.
   :type pairwise_ttest_groups: Iterable[TTestGroup]
   :param sig_type: Type of significance metric (e.g., "pval"). Defaults to "pval".
   :type sig_type: str, optional
   :param sig_thr: Significance threshold. Defaults to 0.05.
   :type sig_thr: float, optional

   :returns: DataFrame with calculated scalars for all groups.
   :rtype: pd.DataFrame