refactor pca functions

ddmc/figures/common.py

@@ -4,21 +4,18 @@
 
 import sys
 import time
-from pathlib import Path
 from string import ascii_uppercase
 from matplotlib import gridspec, pyplot as plt, axes, rcParams
 import seaborn as sns
 import numpy as np
 import pandas as pd
 import svgutils.transform as st
 import logomaker as lm
-from sklearn.preprocessing import StandardScaler
-from ..pre_processing import filter_NaNpeptides
-from ..clustering import DDMC
 from scipy.stats import mannwhitneyu
 from statsmodels.stats.multitest import multipletests
 from ..motifs import KinToPhosphotypeDict
 from ddmc.binomial import AAlist
+from sklearn.decomposition import PCA
 
 
 rcParams["font.sans-serif"] = "Arial"
@@ -249,6 +246,63 @@ def plot_p_signal_across_clusters_and_binary_feature(
         ax.text(i, annotation_height, annotation, ha="center", va="bottom", fontsize=10)
 
 
+def plot_pca_on_cluster_centers(
+    centers: pd.DataFrame,
+    axes,
+    hue_scores: np.ndarray = None,
+    hue_scores_title: str = None,
+    hue_loadings: np.ndarray = None,
+    hue_loadings_title: str = None,
+):
+    # run PCA on cluster centers
+    pca = PCA(n_components=2)
+    scores = pca.fit_transform(centers)  # sample by PCA component
+    loadings = pca.components_  # PCA component by cluster
+    variance_explained = np.round(pca.explained_variance_ratio_, 2)
+
+    # plot scores
+    sns.scatterplot(
+        x=scores[:, 0],
+        y=scores[:, 1],
+        hue=hue_scores,
+        ax=axes[0],
+        **{"linewidth": 0.5, "edgecolor": "k"},
+    )
+    if hue_scores_title:
+        axes[0].legend(
+            loc="lower left", prop={"size": 9}, title=hue_scores_title, fontsize=9
+        )
+    axes[0].set_title("PCA Scores")
+    axes[0].set_xlabel(
+        "PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10
+    )
+    axes[0].set_ylabel(
+        "PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10
+    )
+
+    # plot loadings
+    sns.scatterplot(
+        x=loadings[0],
+        y=loadings[1],
+        ax=axes[1],
+        hue=hue_loadings,
+        **{"linewidth": 0.5, "edgecolor": "k"},
+    )
+    if hue_loadings_title:
+        axes[1].legend(title="p < 0.01", prop={"size": 8})
+    axes[1].set_title("PCA Loadings")
+    axes[1].set_xlabel(
+        "PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10
+    )
+    axes[1].set_ylabel(
+        "PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10
+    )
+    for j, txt in enumerate(centers.columns):
+        axes[1].annotate(
+            txt, (loadings[0][j] + 0.001, loadings[1][j] + 0.001), fontsize=10
+        )
+
+
 def ExportClusterFile(cluster, cptac=False, mcf7=False):
     """Export cluster SVG file for NetPhorest and GO analysis."""
     if cptac:

ddmc/figures/figureM3.py

@@ -9,17 +9,15 @@
 from ..clustering import DDMC
 from ..binomial import AAlist
 from .common import getSetup
-from ..pca import plotPCA
 from .common import (
-    plot_distance_to_upstream_kinase,
     plot_motifs,
     plot_cluster_kinase_distances,
+    plot_pca_on_cluster_centers,
 )
 from ..clustering import compute_control_pssm, get_pspl_pssm_distances
 from ..binomial import AAlist
 from ..motifs import DictProteomeNameToSeq, get_pspls
 from ..pre_processing import filter_NaNpeptides, separate_sequence_and_abundance
-from sklearn.decomposition import PCA
 
 
 def makeFigure():
@@ -84,37 +82,9 @@ def plot_fig_3abd(ax_a, ax_b, ax_d):
         for drug in inhibitors
     ]
 
-    # run PCA on cluster centers
-    pca = PCA(n_components=2)
-    scores = pca.fit_transform(centers)  # sample by PCA component
-    loadings = pca.components_  # PCA component by cluster
-    variance_explained = np.round(pca.explained_variance_ratio_, 2)
-
-    # plot scores
-    sns.scatterplot(
-        x=scores[:, 0],
-        y=scores[:, 1],
-        hue=is_AKTi,
-        ax=ax_a,
-        **{"linewidth": 0.5, "edgecolor": "k"},
+    plot_pca_on_cluster_centers(
+        centers, [ax_a, ax_b], hue_scores=is_AKTi, hue_scores_title="AKTi?"
     )
-    ax_a.legend(loc="lower left", prop={"size": 9}, title="AKTi", fontsize=9)
-    ax_a.set_title("PCA Scores")
-    ax_a.set_xlabel("PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10)
-    ax_a.set_ylabel("PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10)
-
-    # plot loadings
-    sns.scatterplot(
-        x=loadings[0], y=loadings[1], ax=ax_b, **{"linewidth": 0.5, "edgecolor": "k"}
-    )
-    ax_b.set_title("PCA Loadings")
-    ax_b.set_xlabel("PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10)
-    ax_b.set_ylabel("PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10)
-    ax_b.legend(prop={"size": 8})
-    for j, txt in enumerate(centers.columns):
-        ax_b.annotate(
-            txt, (loadings[0][j] + 0.001, loadings[1][j] + 0.001), fontsize=10
-        )
 
     # Plot kinase predictions for cluster 16
     plot_cluster_kinase_distances(

ddmc/figures/figureM5.py

@@ -1,11 +1,9 @@
 import numpy as np
 import seaborn as sns
-from scipy.stats import mannwhitneyu
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegressionCV
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
-from statsmodels.stats.multitest import multipletests
 
 from ddmc.clustering import DDMC
 from ddmc.datasets import CPTAC, select_peptide_subset
@@ -14,6 +12,7 @@
     plot_cluster_kinase_distances,
     get_pvals_across_clusters,
     plot_p_signal_across_clusters_and_binary_feature,
+    plot_pca_on_cluster_centers,
 )
 from ddmc.logistic_regression import plotClusterCoefficients, plotROC
 
@@ -33,51 +32,18 @@ def makeFigure():
     # sns.clustermap(centers.set_index("Type").T, method="complete", cmap="bwr", vmax=lim, vmin=-lim,  figsize=(15, 9)) Run in notebook and save as svg
     axes[0].axis("off")
 
-    # run PCA on cluster centers
-    pca = PCA(n_components=2)
-    scores = pca.fit_transform(centers)  # sample by PCA component
-    loadings = pca.components_  # PCA component by cluster
-    variance_explained = np.round(pca.explained_variance_ratio_, 2)
-
-    # plot scores
-    sns.scatterplot(
-        x=scores[:, 0],
-        y=scores[:, 1],
-        hue=is_tumor,
-        ax=axes[1],
-        **{"linewidth": 0.5, "edgecolor": "k"},
-    )
-    axes[1].legend(loc="lower left", prop={"size": 9}, title="Tumor", fontsize=9)
-    axes[1].set_title("PCA Scores")
-    axes[1].set_xlabel(
-        "PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10
-    )
-    axes[1].set_ylabel(
-        "PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10
+    plot_pca_on_cluster_centers(
+        centers,
+        axes[1:3],
+        hue_scores=is_tumor,
+        hue_scores_title="Tumor?",
+        hue_loadings=get_pvals_across_clusters(is_tumor, centers) < 0.01,
+        hue_loadings_title="p < 0.01",
     )
 
-    # plot loadings
-    sns.scatterplot(
-        x=loadings[0],
-        y=loadings[1],
-        ax=axes[2],
-        hue=get_pvals_across_clusters(is_tumor, centers) < 0.01,
-        **{"linewidth": 0.5, "edgecolor": "k"},
+    plot_p_signal_across_clusters_and_binary_feature(
+        is_tumor, centers, "is_tumor", axes[4]
     )
-    axes[2].set_title("PCA Loadings")
-    axes[2].set_xlabel(
-        "PC1 (" + str(int(variance_explained[0] * 100)) + "%)", fontsize=10
-    )
-    axes[2].set_ylabel(
-        "PC2 (" + str(int(variance_explained[1] * 100)) + "%)", fontsize=10
-    )
-    axes[2].legend(title="p < 0.01", prop={"size": 8})
-    for j, txt in enumerate(centers.columns):
-        axes[2].annotate(
-            txt, (loadings[0][j] + 0.001, loadings[1][j] + 0.001), fontsize=10
-        )
-
-    plot_p_signal_across_clusters_and_binary_feature(is_tumor, centers, "is_tumor", axes[4])
 
     # Logistic Regression
     lr = LogisticRegressionCV(