Analysis

padua.analysis.anova_1way(df, *args, fdr=0.05)

Perform Analysis of Variation (ANOVA) on provided dataframe and for specified groups. Groups for analysis can be specified as individual arguments, e.g.

anova(df, “Group A”, “Group B”) anova(df, (“Group A”, 5), (“Group B”, 5))

At least 2 groups must be provided.

Returns:Dataframe containing selected groups and P/T/sig value for the comparisons.

padua.analysis.correlation(df, rowvar=False)

Calculate column-wise Pearson correlations using numpy.ma.corrcoef

Input data is masked to ignore NaNs when calculating correlations. Data is returned as a Pandas DataFrame of column_n x column_n dimensions, with column index copied to both axes.

Parameters:df – Pandas DataFrame Returns:Pandas DataFrame (n_columns x n_columns) of column-wise correlations

padua.analysis.enrichment_from_evidence(dfe, modification='Phospho (STY)')

Calculate relative enrichment of peptide modifications from evidence.txt.

Taking a modifiedsitepeptides DataFrame returns the relative enrichment of the specified modification in the table.

The returned data columns are generated from the input data columns.

Parameters:df – Pandas DataFrame of evidence Returns:Pandas DataFrame of percentage modifications in the supplied data.

padua.analysis.enrichment_from_msp(dfmsp, modification='Phospho (STY)')

Calculate relative enrichment of peptide modifications from modificationSpecificPeptides.txt.

Taking a modifiedsitepeptides DataFrame returns the relative enrichment of the specified modification in the table.

The returned data columns are generated from the input data columns.

Parameters:df – Pandas DataFrame of modificationSpecificPeptides Returns:Pandas DataFrame of percentage modifications in the supplied data.

padua.analysis.go_enrichment(df, enrichment='function', organism='Homo sapiens', summary=True, fdr=0.05, ids_from=['Proteins', 'Protein IDs'])

Calculate gene ontology (GO) enrichment for a specified set of indices, using the PantherDB GO enrichment service.

Provided with a processed data DataFrame will calculate the GO ontology enrichment specified by enrichment, for the specified organism. The IDs to use for genes are taken from the field ids_from, which by default is compatible with both proteinGroups and modified peptide tables. Setting the fdr parameter (default=0.05) sets the cut-off to use for filtering the results. If summary is True (default) the returned DataFrame contains just the ontology summary and FDR.

Parameters:

• df – Pandas DataFrame to
• enrichment – str GO enrichment method to use (one of ‘function’, ‘process’, ‘cellular_location’, ‘protein_class’, ‘pathway’)
• organism – str organism name (e.g. “Homo sapiens”)
• summary – bool return full, or summarised dataset
• fdr – float FDR cut-off to use for returned GO enrichments
• ids_from – list of str containing the index levels to select IDs from (genes, protein IDs, etc.) default=[‘Proteins’,’Protein IDs’]

Returns:

Pandas DataFrame containing enrichments, sorted by P value.

padua.analysis.modifiedaminoacids(df)

Calculate the number of modified amino acids in supplied DataFrame.

Returns the total of all modifications and the total for each amino acid individually, as an int and a dict of int, keyed by amino acid, respectively.

Parameters:df – Pandas DataFrame containing processed data. Returns:total_aas int the total number of all modified amino acids quants dict of int keyed by amino acid, giving individual counts for each aa.

padua.analysis.pca(df, n_components=2, mean_center=False, **kwargs)

Principal Component Analysis, based on sklearn.decomposition.PCA

Performs a principal component analysis (PCA) on the supplied dataframe, selecting the first n_components components in the resulting model. The model scores and weights are returned.

For more information on PCA and the algorithm used, see the scikit-learn documentation.

Parameters:

• df – Pandas DataFrame to perform the analysis on
• n_components – int number of components to select
• mean_center – bool mean center the data before performing PCA
• kwargs – additional keyword arguments to sklearn.decomposition.PCA

Returns:

scores DataFrame of PCA scores n_components x n_samples weights DataFrame of PCA weights n_variables x n_components

padua.analysis.plsda(df, a, b, n_components=2, mean_center=False, scale=True, **kwargs)

Partial Least Squares Discriminant Analysis, based on sklearn.cross_decomposition.PLSRegression

Performs a binary group partial least squares discriminant analysis (PLS-DA) on the supplied dataframe, selecting the first n_components.

Sample groups are defined by the selectors a and b which are used to select columns from the supplied dataframe. The result model is applied to the entire dataset, projecting non-selected samples into the same space.

For more information on PLS regression and the algorithm used, see the scikit-learn documentation.

Parameters:

• df – Pandas DataFrame to perform the analysis on
• a – Column selector for group a
• b – Column selector for group b
• n_components – int number of components to select
• mean_center – bool mean center the data before performing PLS regression
• kwargs – additional keyword arguments to sklearn.cross_decomposition.PLSRegression

Returns:

scores DataFrame of PLSDA scores n_components x n_samples weights DataFrame of PLSDA weights n_variables x n_components

padua.analysis.plsr(df, v, n_components=2, mean_center=False, scale=True, **kwargs)

Partial Least Squares Regression Analysis, based on sklearn.cross_decomposition.PLSRegression

Performs a partial least squares regression (PLS-R) on the supplied dataframe df against the provided continuous variable v, selecting the first n_components.

For more information on PLS regression and the algorithm used, see the scikit-learn documentation.

Parameters:

• df – Pandas DataFrame to perform the analysis on
• v – Continuous variable to perform regression against
• n_components – int number of components to select
• mean_center – bool mean center the data before performing PLS regression
• kwargs – additional keyword arguments to sklearn.cross_decomposition.PLSRegression

Returns:

scores DataFrame of PLS-R scores n_components x n_samples weights DataFrame of PLS-R weights n_variables x n_components

padua.analysis.sitespeptidesproteins(df, site_localization_probability=0.75)

Generate summary count of modified sites, peptides and proteins in a processed dataset DataFrame.

Returns the number of sites, peptides and proteins as calculated as follows:

• sites (>0.75; or specified site localization probability) count of all sites > threshold
• peptides the set of Sequence windows in the dataset (unique peptides)
• proteins the set of unique leading proteins in the dataset

Parameters:

• df – Pandas DataFrame of processed data
• site_localization_probability – float site localization probability threshold (for sites calculation)

Returns:

tuple of int, containing sites, peptides, proteins