scRNA-seq_analysis

pipelines/18_clustering_comparison/clustering.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct 03 10:49:22 2018
+
+@author: doru
+"""
+
+import sys
+args = sys.argv
+output_folder = args[1]
+n_clusters = int(args[2])
+
+import os
+
+os.chdir(output_folder)
+
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.neighbors import kneighbors_graph
+from sklearn import mixture
+from sklearn import metrics
+import pandas as pd
+import numpy as np
+
+print("Loading data ...")
+pca_df = pd.read_csv("material/pca.csv", sep = ",", index_col = 0)
+pca = pca_df.values[:, :20]
+ori_labels = pca_df.values[:, 20]
+
+print("computing agglomerative clustering ... ")
+connectivity_graph = kneighbors_graph(X = pca, n_neighbors = 30)
+clustering = AgglomerativeClustering(n_clusters = n_clusters, affinity = "euclidean", 
+                                     connectivity = connectivity_graph, 
+                                     linkage = "ward")
+agg_labels = clustering.fit(pca)
+agg_labels = agg_labels.labels_
+
+print("computing gaussian mixture clustering ... ")
+gmm = mixture.GaussianMixture(n_components = n_clusters, covariance_type = "full",
+                              random_state = 52).fit(pca)
+gmm_labels = gmm.predict(pca)
+
+# metrics
+
+# voted labels
+def voted_labels(true, pred):
+    pred_unique = np.unique(pred)
+    pred_voted = true.copy()
+    for p in pred_unique:
+        p_indx = pred == p
+        ori_types = true[p_indx]
+        ori_unique = np.unique(ori_types, return_counts = True)
+        voted = ori_unique[0][np.argmax(ori_unique[1])]
+        pred_voted[p_indx] = voted
+    return pred_voted
+
+agg_labels = voted_labels(ori_labels, agg_labels)
+gmm_labels = voted_labels(ori_labels, gmm_labels)
+
+# compute adjusted Rand Index
+# reference: Comparing partitions - https://link.springer.com/article/10.1007%2FBF01908075
+RandIndex_Louvain_Agg = metrics.adjusted_rand_score(ori_labels, agg_labels)
+RandIndex_Louvain_GMM = metrics.adjusted_rand_score(ori_labels, gmm_labels)
+
+# ref for mutual information metrics - "Information theoretic measures for clusterings comparison"
+
+# compute adjusted mutual information
+AdjMutInf_Louvain_Agg = metrics.adjusted_mutual_info_score(ori_labels, agg_labels)
+AdjMutInf_Louvain_GMM = metrics.adjusted_mutual_info_score(ori_labels, gmm_labels)
+
+RandIndex_Louvain_Agg = str(RandIndex_Louvain_Agg)
+RandIndex_Louvain_GMM = str(RandIndex_Louvain_GMM)
+AdjMutInf_Louvain_Agg = str(AdjMutInf_Louvain_Agg)
+AdjMutInf_Louvain_GMM = str(AdjMutInf_Louvain_GMM)
+
+# save measures to disk
+result = '\n'.join([RandIndex_Louvain_Agg, RandIndex_Louvain_GMM, 
+                    AdjMutInf_Louvain_Agg, AdjMutInf_Louvain_GMM])
+
+with open('material/agreement_measures.txt', 'w') as agg_file:
+  agg_file.write(result)
+  
+print("saving gaussian mixture clustering labels to disk ...")
+df = pd.DataFrame(gmm_labels)
+df.index = pca_df.index
+df.to_csv("./material/gaussian_mixture.csv")
+
+print("saving agglomerative clustering labels to disk ...")
+df = pd.DataFrame(agg_labels)
+df.index = pca_df.index
+df.to_csv("./material/agglomerative_clustering.csv")