Restore notebook plotting functions removed by dead code cleanup

alphaquant/plotting/multicond.py

@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+
+def plot_proteoform_intensity_profiles(proteoform_df: pd.DataFrame):
+    """Creates a panel of proteoform intensity profiles for each protein in the dataframe.
+
+    Args:
+        proteoform_df: proteoform dataframe loaded from the AlphaQuant output file `medianref_proteoforms.tsv`
+
+    Returns:
+        fig, axes: matplotlib figure and axes objects
+    """
+    fixed_columns = ['proteoform_id', 'peptides', 'number_of_peptides', 'protein', 'corr_to_ref', 'is_reference']
+    conditions = proteoform_df.columns.difference(fixed_columns)
+
+    grouped = proteoform_df.groupby('protein')
+    n_plots = len(grouped)
+
+    n_cols = int(np.ceil(np.sqrt(n_plots)))
+    n_rows = int(np.ceil(n_plots / n_cols))
+
+    fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))
+    axes = axes.flatten()
+
+    for ax, (protein, sub_df) in zip(axes, grouped):
+        for idx, row in sub_df.iterrows():
+            style = '-' if row['is_reference'] else '--'
+            ax.plot(conditions, row[conditions], style, label=row['proteoform_id'])
+        ax.set_title(f'{protein}')
+        ax.set_xlabel('Tissue')
+        ax.set_ylabel('Expression Level')
+        ax.legend(title='Proteoform ID')
+
+    for i in range(n_plots, len(axes)):
+        axes[i].axis('off')
+
+    fig.tight_layout()
+    plt.show()
+    return fig, axes

alphaquant/plotting/pairwise.py

@@ -11,6 +11,32 @@
 LOGGER = logging.getLogger(__name__)
 
 
+def plot_normalization_overview(normed_df, samplemap_df):
+    normed_df, sample2cond = aq_diff_utils.prepare_loaded_tables(normed_df, samplemap_df)
+    sample2cond = dict(zip(samplemap_df["sample"], samplemap_df["condition"]))
+    conditions = list(set([sample2cond.get(x) for x in normed_df.columns]))
+    conditions = [x for x in conditions if x is not None]
+    df_c1 = normed_df[[x for x in normed_df.columns if sample2cond.get(x) == conditions[0]]]
+    df_c2 = normed_df[[x for x in normed_df.columns if sample2cond.get(x) == conditions[1]]]
+
+    plot_betweencond_fcs(df_c1, df_c2, merge_samples=True)
+    plot_sample_vs_median_fcs(df_c1, df_c2)
+
+
+def plot_sample_vs_median_fcs(df_c1_normed, df_c2_normed):
+    combined_median = pd.concat([df_c1_normed, df_c2_normed], axis=1).median(axis=1, skipna=True)
+    fig, axes = plt.subplots()
+    for df in [df_c1_normed, df_c2_normed]:
+        for col in df.columns:
+            diff_fcs = df[col].subtract(combined_median)
+            axes.axvline(0, color='red', linestyle="dashed")
+            cutoff = max(abs(np.nanquantile(diff_fcs, 0.025)), abs(np.nanquantile(diff_fcs, 0.975)))
+            axes.hist(diff_fcs, 80, density=True, histtype='step', range=(-cutoff, cutoff), label=col)
+    axes.set_xlabel("log2(fc)")
+    axes.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
+    return fig, axes
+
+
 def plot_withincond_normalization(df_c1, df_c2):
     LOGGER.info("without missingvals (if applicable)")
     plot_betweencond_fcs(aqnorm.drop_nas_if_possible(df_c1), aqnorm.drop_nas_if_possible(df_c2), True)