{"action":"created","comment":{"url":"https://api.github.com/repos/cognoma/machine-learning/pulls/comments/76476481","id":76476481,"diff_hunk":"@@ -0,0 +1,535 @@\n+\n+# coding: utf-8\n+\n+# # Predicting Aberrations in The Hippo Signaling Pathway\n+\n+# ## To do this, we will use Hetnets to query \"Signaling by Hippo\"\n+# \n+# The query is built in the [Cypher Language](https://neo4j.com/developer/cypher-query-language/) and draws data from [Hetionet](https://neo4j.het.io/browser/)\n+# \n+# ### How Cognoma could help with Hippo signaling\n+# \n+# The Hippo pathway is a highly conserved signaling cascade that controls organ size, cell growth, and cell death ([Zhao et al. 2010](http://doi.org/10.1101/gad.1909210)). It is one of the mechanisms that influences size diversity across eukaryotes; including different sizes across dog breeds ([Dong et al. 2007](http://doi.org/10.1016/j.cell.2007.07.019), [Crickmore and Mann 2008](http://doi.org/10.1002/bies.20806)). Recently, Hippo signaling has also been shown to be important for tumorigenesis, but there are shockingly few recurrent mutations of single genes within the pathway across tissues ([Harvey et al 2013](http://doi.org/10.1038/nrc3458)). Therefore, leveraging cancers from multiple tissues and combining genes associated with the same pathway could aid in the detection of a Hippo signaling specific gene expression signature. Cognoma is situated well to quickly query the list of all pathway associated genes, build a machine learning classifier to detect aberrant pathway activity, and output tissue and gene specific performance.\n+\n+# In[1]:\n+\n+import os\n+import urllib\n+import random\n+import warnings\n+\n+import pandas as pd\n+import numpy as np\n+import matplotlib.pyplot as plt\n+import seaborn as sns\n+from sklearn import preprocessing, grid_search\n+from sklearn.linear_model import SGDClassifier\n+from sklearn.cross_validation import train_test_split\n+from sklearn.metrics import roc_auc_score, roc_curve\n+from sklearn.pipeline import make_pipeline\n+from sklearn.preprocessing import StandardScaler\n+from sklearn.feature_selection import SelectKBest\n+from statsmodels.robust.scale import mad\n+\n+\n+# In[2]:\n+\n+from neo4j.v1 import GraphDatabase\n+\n+\n+# In[3]:\n+\n+get_ipython().magic('matplotlib inline')\n+plt.style.use('seaborn-notebook')\n+\n+\n+# ## Specify model configuration\n+\n+# In[4]:\n+\n+REACTOMEPATHWAY = 'PC7_7459'  # \"Signaling by Hippo\" in Reactome\n+GOPATHWAY_A = 'GO:0035329'    # \"hippo signaling\" in the Gene Ontology\n+GOPATHWAY_B = 'GO:0035330'    # \"regulation of hippo signaling\" in the Gene Ontology\n+\n+\n+# In[5]:\n+\n+def pathway_query(path_id, node_type='BiologicalProcess', particip='GpBP'):\n+    query = '''\n+    MATCH (node:{})-[:PARTICIPATES_{}]-(gene)\n+    WHERE node.identifier = \"{}\"\n+    RETURN\n+      gene.identifier as entrez_gene_id,\n+      gene.name as gene_symbol\n+    ORDER BY gene_symbol\n+    '''.format(node_type, particip, path_id)\n+    return query\n+\n+\n+# In[6]:\n+\n+query_r = pathway_query(REACTOMEPATHWAY, node_type='Pathway', particip='GpPW')\n+query_go_a = pathway_query(GOPATHWAY_A)\n+query_go_b = pathway_query(GOPATHWAY_B)\n+queries = [query_r, query_go_a, query_go_b]\n+\n+\n+# In[7]:\n+\n+driver = GraphDatabase.driver(\"bolt://neo4j.het.io\")\n+full_results_df = pd.DataFrame()\n+with driver.session() as session:\n+    for query in queries:\n+        result = session.run(query)\n+        result_df = pd.DataFrame((x.values() for x in result), columns=result.keys())\n+        full_results_df = full_results_df.append(result_df, ignore_index=True)\n+classifier_genes_df = full_results_df.drop_duplicates().sort_values('gene_symbol').reset_index(drop=True)\n+classifier_genes_df['entrez_gene_id'] = classifier_genes_df['entrez_gene_id'].astype('str')\n+\n+\n+# In[8]:\n+\n+# Here are the genes that participate in the Hippo signaling pathway\n+classifier_genes_df\n+\n+\n+# In[9]:\n+\n+# Parameter Sweep for Hyperparameters\n+n_feature_kept = 8000\n+param_fixed = {\n+    'loss': 'log',\n+    'penalty': 'elasticnet',\n+}\n+param_grid = {\n+    'alpha': [10 ** x for x in range(-6, 1)],\n+    'l1_ratio': [0, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 1],\n+}\n+\n+\n+# *Here is some [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) regarding the classifier and hyperparameters*\n+\n+# ## Load Data\n+\n+# In[10]:\n+\n+if not os.path.exists('data'):\n+    os.makedirs('data')\n+\n+\n+# In[11]:\n+\n+url_to_path = {\n+    # X matrix\n+    'https://ndownloader.figshare.com/files/5864859':\n+        os.path.join('data', 'expression-matrix.tsv.bz2'),\n+    # Y Matrix\n+    'https://ndownloader.figshare.com/files/5864859':\n+        os.path.join('data', 'mutation-matrix.tsv.bz2'),\n+    # Clinical matrix\n+    'https://ndownloader.figshare.com/files/5864859':\n+        os.path.join('data', 'samples.tsv')\n+}\n+\n+for url, path in url_to_path.items():\n+    if not os.path.exists(path):\n+        urllib.request.urlretrieve(url, path)\n+\n+\n+# In[12]:\n+\n+get_ipython().run_cell_magic('time', '', \"path = os.path.join('data', 'expression-matrix.tsv.bz2')\\nX = pd.read_table(path, index_col=0)\")\n+\n+\n+# In[13]:\n+\n+get_ipython().run_cell_magic('time', '', \"path = os.path.join('data', 'mutation-matrix.tsv.bz2')\\nY = pd.read_table(path, index_col=0)\")\n+\n+\n+# In[14]:\n+\n+get_ipython().run_cell_magic('time', '', \"path = os.path.join('data', 'samples.tsv')\\nclinical = pd.read_table(path, index_col=0)\")\n+\n+\n+# In[15]:\n+\n+clinical.tail(5)\n+\n+\n+# In[16]:\n+\n+# Subset the Y matrix to only the genes to be classified\n+y_full = Y[classifier_genes_df['entrez_gene_id']]\n+\n+\n+# In[17]:\n+\n+y_full.columns = classifier_genes_df['gene_symbol']\n+y_full['disease'] = clinical['disease']\n+\n+\n+# In[18]:\n+\n+mut_tis_total = y_full.groupby('disease').sum()\n+mut_tis_total.plot(kind='bar', stacked=True, grid=False, legend=False, sort_columns=True)\n+\n+\n+# In[19]:\n+\n+# How frequent are different mutations across different cancer types?\n+sns.heatmap(mut_tis_total)\n+\n+\n+# Visualizing the input data here is key. The heterogeneity of the mutations across tissues is apparent for this particular pathway. In comparison with `TP53` mutations, it appears that Hippo signaling impacts different tissues with much higher diversity.\n+# \n+# Looking closer at the plots above, it is evident that several tissues do not demonstrate aberrations (at least at the mutation level) in Hippo signaling. Specifically, it appears that cancers with gender specificity like testicular cancer and uterine cancer  are _not_ impacted. Therefore, because of this artificial imbalance, if Cognoma were to include these cancers in the classifier, it **will** key in on gender specific signal (i.e. genes that are only on the Y chromosome, or X inactivation genes).  \n+\n+# In[20]:\n+\n+# Build final y matrix for classifier\n+y = y_full.max(axis=1)\n+\n+# Determine which tissues to filter\n+filter_tissue = pd.DataFrame([y, y_full['disease']], index=['sample', 'disease']).T\n+tissue_decision = filter_tissue.groupby('disease').sum()\n+\n+\n+# In[21]:\n+\n+# How many samples in each tissue that have Hippo signaling aberrations\n+tissue_decision","path":"scripts/2.TCGA-MLexample_Pathway.py","position":200,"original_position":200,"commit_id":"48dc5940108d52fa11f83cd7e5f66a1380d9079d","original_commit_id":"48dc5940108d52fa11f83cd7e5f66a1380d9079d","user":{"login":"dhimmel","id":1117703,"avatar_url":"https://avatars.githubusercontent.com/u/1117703?v=3","gravatar_id":"","url":"https://api.github.com/users/dhimmel","html_url":"https://github.com/dhimmel","followers_url":"https://api.github.com/users/dhimmel/followers","following_url":"https://api.github.com/users/dhimmel/following{/other_user}","gists_url":"https://api.github.com/users/dhimmel/gists{/gist_id}","starred_url":"https://api.github.com/users/dhimmel/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dhimmel/subscriptions","organizations_url":"https://api.github.com/users/dhimmel/orgs","repos_url":"https://api.github.com/users/dhimmel/repos","events_url":"https://api.github.com/users/dhimmel/events{/privacy}","received_events_url":"https://api.github.com/users/dhimmel/received_events","type":"User","site_admin":false},"body":"Would be nice here to have 2 columns (n_positives and n_negatives)","created_at":"2016-08-26T19:41:38Z","updated_at":"2016-08-26T19:41:38Z","html_url":"https://github.com/cognoma/machine-learning/pull/39#discussion_r76476481","pull_request_url":"https://api.github.com/repos/cognoma/machine-learning/pulls/39","_links":{"self":{"href":"https://api.github.com/repos/cognoma/machine-learning/pulls/comments/76476481"},"html":{"href":"https://github.com/cognoma/machine-learning/pull/39#discussion_r76476481"},"pull_request":{"href":"https://api.github.com/repos/cognoma/machine-learning/pulls/39"}}},"pull_request":{"url":"https://api.github.com/repos/cognoma/machine-learning/pulls/39","id":82936718,"html_url":"https://github.com/cognoma/machine-learning/pull/39","diff_url":"https://github.com/cognoma/machine-learning/pull/39.diff","patch_url":"https://github.com/cognoma/machine-learning/pull/39.patch","issue_url":"https://api.github.com/repos/cognoma/machine-learning/issues/39","number":39,"state":"open","locked":false,"title":"Machine Learning Pathway Classifier Example","user":{"login":"gwaygenomics","id":7340421,"avatar_url":"https://avatars.githubusercontent.com/u/7340421?v=3","gravatar_id":"","url":"https://api.github.com/users/gwaygenomics","html_url":"https://github.com/gwaygenomics","followers_url":"https://api.github.com/users/gwaygenomics/followers","following_url":"https://api.github.com/users/gwaygenomics/following{/other_user}","gists_url":"https://api.github.com/users/gwaygenomics/gists{/gist_id}","starred_url":"https://api.github.com/users/gwaygenomics/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gwaygenomics/subscriptions","organizations_url":"https://api.github.com/users/gwaygenomics/orgs","repos_url":"https://api.github.com/users/gwaygenomics/repos","events_url":"https://api.github.com/users/gwaygenomics/events{/privacy}","received_events_url":"https://api.github.com/users/gwaygenomics/received_events","type":"User","site_admin":false},"body":"The pull request adds an example of predicting a Biological Pathway signature using PanCancer data. In the example, I predict Hippo Signaling with variable prediction across tissues. The pull request makes two contributions:\r\n\r\n1. Demonstrates that we _can_ predict whole pathways using the Cognoma approach\r\n2. Drives home the need for tissue specific visualizations and the importance of selecting appropriate tissues for training the classifier.","created_at":"2016-08-26T18:57:01Z","updated_at":"2016-08-26T19:41:38Z","closed_at":null,"merged_at":null,"merge_commit_sha":"e85b30e3ee4bb9c5cfbb5454c5bb3caa31b89a8d","assignee":{"login":"yl565","id":15843826,"avatar_url":"https://avatars.githubusercontent.com/u/15843826?v=3","gravatar_id":"","url":"https://api.github.com/users/yl565","html_url":"https://github.com/yl565","followers_url":"https://api.github.com/users/yl565/followers","following_url":"https://api.github.com/users/yl565/following{/other_user}","gists_url":"https://api.github.com/users/yl565/gists{/gist_id}","starred_url":"https://api.github.com/users/yl565/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yl565/subscriptions","organizations_url":"https://api.github.com/users/yl565/orgs","repos_url":"https://api.github.com/users/yl565/repos","events_url":"https://api.github.com/users/yl565/events{/privacy}","received_events_url":"https://api.github.com/users/yl565/received_events","type":"User","site_admin":false},"assignees":[{"login":"yl565","id":15843826,"avatar_url":"https://avatars.githubusercontent.com/u/15843826?v=3","gravatar_id":"","url":"https://api.github.com/users/yl565","html_url":"https://github.com/yl565","followers_url":"https://api.github.com/users/yl565/followers","following_url":"https://api.github.com/users/yl565/following{/other_user}","gists_url":"https://api.github.com/users/yl565/gists{/gist_id}","starred_url":"https://api.github.com/users/yl565/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yl565/subscriptions","organizations_url":"https://api.github.com/users/yl565/orgs","repos_url":"https://api.github.com/users/yl565/repos","events_url":"https://api.github.com/users/yl565/events{/privacy}","received_events_url":"https://api.github.com/users/yl565/received_events","type":"User","site_admin":false}],"milestone":null,"commits_url":"https://api.github.com/repos/cognoma/machine-learning/pulls/39/commits","review_comments_url":"https://api.github.com/repos/cognoma/machine-learning/pulls/39/comments","review_comment_url":"https://api.github.com/repos/cognoma/machine-learning/pulls/comments{/number}","comments_url":"https://api.github.com/repos/cognoma/machine-learning/issues/39/comments","statuses_url":"https://api.github.com/repos/cognoma/machine-learning/statuses/48dc5940108d52fa11f83cd7e5f66a1380d9079d","head":{"label":"gwaygenomics:pathway","ref":"pathway","sha":"48dc5940108d52fa11f83cd7e5f66a1380d9079d","user":{"login":"gwaygenomics","id":7340421,"avatar_url":"https://avatars.githubusercontent.com/u/7340421?v=3","gravatar_id":"","url":"https://api.github.com/users/gwaygenomics","html_url":"https://github.com/gwaygenomics","followers_url":"https://api.github.com/users/gwaygenomics/followers","following_url":"https://api.github.com/users/gwaygenomics/following{/other_user}","gists_url":"https://api.github.com/users/gwaygenomics/gists{/gist_id}","starred_url":"https://api.github.com/users/gwaygenomics/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gwaygenomics/subscriptions","organizations_url":"https://api.github.com/users/gwaygenomics/orgs","repos_url":"https://api.github.com/users/gwaygenomics/repos","events_url":"https://api.github.com/users/gwaygenomics/events{/privacy}","received_events_url":"https://api.github.com/users/gwaygenomics/received_events","type":"User","site_admin":false},"repo":{"id":64150251,"name":"machine-learning","full_name":"gwaygenomics/machine-learning","owner":{"login":"gwaygenomics","id":7340421,"avatar_url":"https://avatars.githubusercontent.com/u/7340421?v=3","gravatar_id":"","url":"https://api.github.com/users/gwaygenomics","html_url":"https://github.com/gwaygenomics","followers_url":"https://api.github.com/users/gwaygenomics/followers","following_url":"https://api.github.com/users/gwaygenomics/following{/other_user}","gists_url":"https://api.github.com/users/gwaygenomics/gists{/gist_id}","starred_url":"https://api.github.com/users/gwaygenomics/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gwaygenomics/subscriptions","organizations_url":"https://api.github.com/users/gwaygenomics/orgs","repos_url":"https://api.github.com/users/gwaygenomics/repos","events_url":"https://api.github.com/users/gwaygenomics/events{/privacy}","received_events_url":"https://api.github.com/users/gwaygenomics/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/gwaygenomics/machine-learning","description":"Machine 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