pyproteome.analysis package

This module provides functionality for data set analysis.

Functions include volcano plots, sorted tables, and plotting sequence levels.

pyproteome.analysis.correlation module

This module provides functionality for data set analysis.

Functions include volcano plots, sorted tables, and plotting sequence levels.

pyproteome.analysis.correlation.correlate_data_sets(data1, data2, adjust=True, label_cutoff=1.5, show_labels=False, show_title=True, ax=None)

Plot the correlation between peptides levels in two different data sets.

Parameters:

data1 : pyproteome.data_sets.DataSet

data2 : pyproteome.data_sets.DataSet

filename : str, optional

pyproteome.analysis.correlation.correlate_signal(data, signal, corr_cutoff=0.8, scatter_cols=4, options=None, title=None, show_duplicates=False, scatter_colors=None, scatter_symbols=None, show_scatter=True, ax=None, xlabel='')

Calculate the correlation between levels of each peptide in a data set and a given signal variable.

Parameters:

data : pyproteome.data_sets.DataSet

signal : pandas.Series

Returns:

f_corr : matplotlib.figure.Figure

f_scatter : matplotlib.figure.Figure

pyproteome.analysis.heatmap module

pyproteome.analysis.heatmap.hierarchical_heatmap(data, cmp_groups=None, minmax=0, zscore=False, show_y=False, title=None, **kwargs)

Plot a hierarhically-clustered heatmap of a data set.

Parameters:

data : pyproteome.data_sets.DataSet

cmp_groups : list of list of str

minmax : float, optional

zscore : bool, optional

show_y : bool, optional

title : str, optional

kwargs : dict

Kwargs passed directly to seaborn.clustermap().

Returns:

map : seaborn.ClusterGrid

pyproteome.analysis.plot module

Plot calculated levels of a given sequence across channels or groups.

pyproteome.analysis.plot.plot(data, title=None, ax=None, log_2=True, box=True)

Plot the levels of a sequence across multiple channels.

Parameters:

data : pyproteome.data_sets.DataSet

title : str, optional

figsize : tuple of (int, int), optional

Returns:

figs : list of matplotlib.figure.Figure

pyproteome.analysis.plot.plot_all(data, cmp_groups=None)

Runs plot() and plot_group() for all peptides in a data set.

Parameters:

data : pyproteome.data_sets.DataSet

cmp_groups : list of tuple, optional

Returns:

figs : list of matplotlib.figure.Figure

pyproteome.analysis.plot.plot_group(data, cmp_groups=None, cmp_groups_star=None, title=None, ax=None, box=True, show_p=True, show_ns=False, log_2=True, offset_frac=20, title_mods=None, size=4, y_max=None, p_ha='center', cmap='cool', linecolor='#000000', swarmcolor='#000000')

Plot the levels of a sequence across each group.

Parameters:

data : pyproteome.data_sets.DataSet

cmp_groups : list of tuple, optional

cmp_groups_star : list of tuple, optional

title : str, optional

ax : matplotlib.axes.Axes, optional

box : bool, optional

show_p : bool, optional

show_ns : bool, optional

log_2 : bool, optional

offset_frac : float, optional

title_mods : list of str, optional

size : float, optional

y_max : float, optional

p_ha : str, optional

cmap : str, optional

Returns:

figs : list of matplotlib.figure.Figure

pyproteome.analysis.plot.plot_together(data, cmp_groups=None, title=None, ax=None, show_p=True, log_2=True, cmap='cool')

Plot the levels of a sequence across each group in one shared plot.

Parameters:

data : pyproteome.data_sets.DataSet

cmp_groups : list of tuple, optional

title : str, optional

ax : matplotlib.axes.Axes, optional

show_p : bool, optional

log_2 : bool, optional

cmap : str, optional

Returns:

figs : list of matplotlib.figure.Figure

pyproteome.analysis.protein module

pyproteome.analysis.protein.draw_protein_seq(ds, genes, max_col=50, p_cutoff=0.01, upper_fc_cutoff=1.05, lower_fc_cutoff=0.95, missed_cleavage=1)

Generate a figure showing all peptides in a data set mapping to the full sequence of their respective proteins.

Peptide differential regulation is indicated by bars for full peptide sequences and circles indicating phosphorylated residues.

Bars and circles are colored red for upregulation and blue for downregulation. Dark grey bars indicate an unmodified peptide with no change. Light grey bars indicate that only the phosphorylated version of that peptide was identified.

Parameters:

ds : pyproteome.data_sets.data_set.DataSet

genes : list of str

max_col : int, optional

p_cutoff : float, optional

upper_fc_cutoff : float, optional

lower_fc_cutoff : float, optional

missed_cleavage : int, optional

Returns:

figs : list of matplotlib.figure.Figure

Examples

>>> figs = analysis.protein.draw_protein_seq(
...     ds, ['Mapt']
... )    

pyproteome.analysis.tables module

pyproteome.analysis.tables.changes_table(data, sort='p-value')

Show a table of fold changes and p-values for each unique peptide in a data set.

Parameters:

data : pyproteome.data_sets.DataSet

sort : str, optional

Returns:

df : pandas.DataFrame

pyproteome.analysis.tables.motif_table(data, f, p=0.05, sort='p-value', **kwargs)

Run a motif enrichment algorithm on a data set and display the significantly enriched motifs.

Parameters:

data : pyproteome.data_sets.DataSet

f : dict or list of dict

p : float, optional

sort : str, optional

Returns:

df : pandas.DataFrame

See also

pyproteome.motifs.motif.run_motif_enrichment()

pyproteome.analysis.tables.ptmsigdb_changes_table(data, sort='p-value', folder_name=None, csv_name=None)

Show a table of fold changes and p-values for PTMSigDB.

Parameters:

data : pyproteome.data_sets.DataSet

sort : str, optional

folder_name : str, optional

csv_name : str, optional

Returns:

df : pandas.DataFrame

pyproteome.analysis.tables.write_csv(data, folder_name=None, out_name='DataSet.csv')

Write information for a single data set to a .csv file.

Sheets are populated with protein, peptide, scan, and quantification values for all peptide-spectrum matches contained within a data set.

Parameters:

data : pyproteome.data_sets.DataSet

folder_name : str, optional

out_name : str, optional

Returns:

path : str

Path to .xlsx file.

pyproteome.analysis.tables.write_full_tables(datas, save_cols=None, sample_values=True, folder_name=None, out_name='Full Data.xlsx')

Write information for a list of data sets to sheets of a .xlsx file.

Sheets are populated with protein, peptide, scan, and quantification values for all peptide-spectrum matches contained within a data set.

Parameters:

datas : list of pyproteome.data_sets.DataSet

save_cols : list of str, optional

Extra column names to save from in each dataset.

sample_values : bool, optional

Save normalized TMT values for each sample to the output.

folder_name : str, optional

out_name : str, optional

Returns:

path : str

Path to .xlsx file.

pyproteome.analysis.volcano module

pyproteome.analysis.volcano.plot_volcano(data, group_a=None, group_b=None, p=0.05, fold=1.25, xminmax=None, yminmax=None, title=None, ax=None, show_xlabel=True, show_ylabel=True, log2_fold=True, log10_p=True, bonferoni=False, **kwargs)

Display a volcano plot of data.

This plot inclues the fold-changes and p-values associated with said changes.

Parameters:

data : pyproteome.data_sets.DataSet

group_a : str or list of str, optional

group_b : str or list of str, optional

p : float, optional

fold : float, optional

xminmax : tuple of (float, float), optional

yminmax : tuple of (float, float), optional

title : str, optional

ax : matplotlib.axes.Axes

show_xlabel : bool, optional

show_ylabel : bool, optional

log2_fold : bool, optional

log10_p : bool, optional

bonferoni : bool, optional

kwargs : dict

Arguments passed to plot_volcano_labels()

Returns:

f : matplotlib.figure.Figure

ax : matplotlib.axes.Axes

pyproteome.analysis.volcano.plot_volcano_filtered(data, f, **kwargs)

Display a volcano plot, showing only peptides that are included by a given filter.

Parameters:

data : pyproteome.data_sets.DataSet

f : dict or list of dict

Filters passed to pyproteome.data_sets.DataSet.filter().

kwargs : dict

Extra arguments that are passed directly to plot_volcano().

Returns:

f : matplotlib.figure.Figure

ax : matplotlib.axes.Axes

pyproteome.analysis.volcano.plot_volcano_labels(data, ax, upper_fold=None, lower_fold=None, p=None, fold_and_p=True, sequence_labels=False, options=None, show_duplicates=False, compress_sym=True, adjust=True, mods=None)

Plot labels on a volcano plot.

Parameters:

data : pyproteome.data_sets.DataSet

ax : matplotlib.axes.Axes

upper_fold : float, optional

lower_fold : float, optional

p : float, optional

fold_and_p : bool, optional

sequence_labels : bool, optional

options : dict, optional

show_duplicates : bool, optional

compress_sym : bool, optional

adjust : bool, optional

mods : str or list of str, optional

Returns:

labels : pandas.DataFrame