figure MS5

ddmc/figures/figureMS5.py

@@ -0,0 +1,58 @@
+import pandas as pd
+import seaborn as sns
+from sklearn.linear_model import LogisticRegressionCV
+from sklearn.cluster import KMeans
+from ddmc.clustering import DDMC
+from ddmc.figures.common import getSetup
+from ddmc.logistic_regression import plot_roc
+from ddmc.datasets import CPTAC, filter_incomplete_peptides
+
+
+def makeFigure():
+    axes, f = getSetup((9, 6), (2, 3), multz={3: 2})
+
+    # Import data
+    cptac = CPTAC()
+    p_signal = filter_incomplete_peptides(cptac.get_p_signal(), sample_presence_ratio=1)
+    is_tumor = cptac.get_tumor_or_nat(p_signal.columns)
+
+    n_components = 30
+    model_gmm = DDMC(
+        n_components=n_components,
+        seq_weight=0,
+        distance_method="Binomial",
+        random_state=5,
+    ).fit(p_signal)
+
+    model_kmeans = KMeans(n_clusters=n_components).fit(p_signal.values)
+
+    lr = LogisticRegressionCV(
+        cv=3,
+        solver="saga",
+        max_iter=10000,
+        n_jobs=-1,
+        penalty="elasticnet",
+        l1_ratios=[0.85],
+        class_weight="balanced",
+    )
+
+    plot_roc(lr, p_signal.T.values, is_tumor, ax=axes[0])
+    axes[0].set_title("unclustered ROC")
+
+    # plot regression coefficients
+    coefs = pd.Series(lr.coef_.squeeze(), index=p_signal.index)
+    coefs.sort_values(ascending=False, inplace=True)
+    coefs.dropna(inplace=True)
+    coefs = pd.concat([coefs.iloc[:10], coefs.iloc[-10:]])
+    coefs = coefs.to_frame("Coefficient")
+    coefs.reset_index(names=["p-site"], inplace=True)
+    sns.barplot(data=coefs, x="p-site", y="Coefficient", color="darkblue", ax=axes[3])
+    axes[3].set_xticklabels(axes[3].get_xticklabels(), rotation=45)
+
+    plot_roc(lr, model_gmm.transform(), is_tumor, ax=axes[1])
+    axes[1].set_title("GMM ROC")
+
+    plot_roc(lr, model_kmeans.cluster_centers_.T, is_tumor, ax=axes[2])
+    axes[2].set_title("kmeans roc")
+
+    return f