diff --git a/ddmc/figures/figureM4.py b/ddmc/figures/figureM4.py
index 52fa98b9..a2575e41 100644
--- a/ddmc/figures/figureM4.py
+++ b/ddmc/figures/figureM4.py
@@ -58,7 +58,7 @@ def do_phenotype_regression(n_runs=3, n_components=35, n_cv_folds=3):
     hot_cold = cptac.get_hot_cold_labels()
 
     lr = LogisticRegressionCV(
-        cv=3,
+        cv=n_cv_folds,
         solver="saga",
         max_iter=10000,
         n_jobs=-1,
diff --git a/ddmc/figures/figureM7.py b/ddmc/figures/figureM7.py
index ca2a7ff9..cb593a13 100644
--- a/ddmc/figures/figureM7.py
+++ b/ddmc/figures/figureM7.py
@@ -15,40 +15,35 @@
     plot_roc,
     plot_cluster_regression_coefficients,
     normalize_cluster_centers,
+    get_highest_weighted_clusters, 
 )
 
 
 def makeFigure():
-    """Get a list of the axis objects and create a figure"""
-    # Get list of axis objects
     axes, f = getSetup((11, 7), (2, 3), multz={0: 1, 4: 1})
     cptac = CPTAC()
     is_hot = cptac.get_hot_cold_labels()
     p_signal = cptac.get_p_signal()
     model = DDMC(n_components=30, seq_weight=100, random_state=5).fit(p_signal)
-    assert (
-        not model.has_empty_clusters()
-    ), "This plot assumes that every cluster will have at least one peptide. Please rerun with fewer components are more peptides."
 
     centers = model.transform(as_df=True).loc[is_hot.index]
+    centers.iloc[:, :] = normalize_cluster_centers(centers.values)
 
     plot_p_signal_across_clusters_and_binary_feature(is_hot, centers, "is_hot", axes[0])
 
-    centers.iloc[:, :] = normalize_cluster_centers(centers.values)
+    n_cv = 15
     lr = LogisticRegressionCV(
-        cv=3, solver="saga", n_jobs=1, penalty="l1", max_iter=10000
+        cv=n_cv, solver="saga", n_jobs=1, penalty="l1", max_iter=10000
     )
     plot_roc(
-        lr, centers.values, is_hot.values, cv_folds=3, title="ROC TI", return_mAUC=True
+        lr, centers.values, is_hot.values, cv_folds=n_cv, title="ROC TI", return_mAUC=True
     )
     plot_cluster_regression_coefficients(axes[1], lr, title="")
 
-    top_clusters = np.argsort(np.abs(lr.coef_.squeeze()))[-3:]
+    top_clusters = get_highest_weighted_clusters(model, lr.coef_)
 
-    #  plot predicted kinases for most weighted clusters
     distances = model.predict_upstream_kinases()[top_clusters]
 
-    # plot upstream Kinases
     plot_cluster_kinase_distances(
         distances, model.get_pssms(clusters=top_clusters), axes[2], num_hits=2
     )