--- a
+++ b/docs/usage/preprocess-downstream-analysis.md
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+# Preparing data for downstream analyses
+
+## To prepare the data for classification
+
+```python
+X_multiomics, y = luad_data.load_data(omics="all", target=["pathologic_stage"], remove_duplicates=True)
+
+print(X_multiomics['MessengerRNA'].shape,
+      X_multiomics['MicroRNA'].shape,
+      X_multiomics['LncRNA'].shape,
+      y.shape)
+```
+
+> (338, 20472) (338, 1870) (338, 12727) (338, 1)
+
+
+```python
+print(y)
+```
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>pathologic_stage</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>TCGA-05-4390-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4405-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4410-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4417-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4424-01A</th>
+      <td>Stage II</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4427-01A</th>
+      <td>Stage II</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-4433-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-5423-01A</th>
+      <td>Stage II</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-5425-01A</th>
+      <td>Stage II</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-5428-01A</th>
+      <td>Stage II</td>
+    </tr>
+    <tr>
+      <th>TCGA-05-5715-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-38-4631-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-38-7271-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-38-A44F-01A</th>
+      <td>Stage I</td>
+    </tr>
+    <tr>
+      <th>TCGA-44-2655-11A</th>
+      <td>Stage I</td>
+    </tr>
+  </tbody>
+</table>
+<p>336 rows × 1 columns</p>
+</div>
+
+
+
+## Log2 transform the mRNA, microRNA, and lncRNA expression values
+
+
+```python
+def expression_val_transform(x):
+    return np.log2(x+1)
+X_multiomics['MessengerRNA'] = X_multiomics['MessengerRNA'].applymap(expression_val_transform)
+X_multiomics['MicroRNA'] = X_multiomics['MicroRNA'].applymap(expression_val_transform)
+# X_multiomics['LncRNA'] = X_multiomics['LncRNA'].applymap(expression_val_transform)
+```
+
+## Classification of Cancer Stage
+
+
+```python
+from sklearn import preprocessing
+from sklearn import metrics
+from sklearn.svm import SVC, LinearSVC
+import sklearn.linear_model
+from sklearn.model_selection import train_test_split
+
+```
+
+
+```python
+binarizer = preprocessing.LabelEncoder()
+binarizer.fit(y)
+binarizer.transform(y)
+```
+
+
+    array([0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
+           0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0,
+           0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
+           1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,
+           0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,
+           0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,
+           0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0,
+           0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0,
+           1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,
+           1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
+           1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
+           0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
+           0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
+           1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0])
+
+
+
+
+```python
+for omic in ["MessengerRNA", "MicroRNA"]:
+    print(omic)
+    scaler = sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=False)
+    scaler.fit(X_multiomics[omic])
+
+    X_train, X_test, Y_train, Y_test = \
+        train_test_split(X_multiomics[omic], y, test_size=0.3, random_state=np.random.randint(0, 10000), stratify=y)
+    print(X_train.shape, X_test.shape)
+
+
+    X_train = scaler.transform(X_train)
+
+    model = LinearSVC(C=1e-2, penalty='l1', class_weight='balanced', dual=False, multi_class="ovr")
+#     model = sklearn.linear_model.LogisticRegression(C=1e-0, penalty='l1', fit_intercept=False, class_weight="balanced")
+#     model = SVC(C=1e0, kernel="rbf", class_weight="balanced", decision_function_shape="ovo")
+
+    model.fit(X=X_train, y=Y_train)
+    print("NONZERO", len(np.nonzero(model.coef_)[0]))
+    print("Training accuracy", metrics.accuracy_score(model.predict(X_train), Y_train))
+    print(metrics.classification_report(y_pred=model.predict(X_test), y_true=Y_test))
+
+```
+
+    MessengerRNA
+    (254, 20472) (109, 20472)
+    NONZERO 0
+    Training accuracy 0.6929133858267716
+                 precision    recall  f1-score   support
+
+        Stage I       0.69      1.00      0.82        75
+       Stage II       0.00      0.00      0.00        34
+
+    avg / total       0.47      0.69      0.56       109
+
+    MicroRNA
+    (254, 1870) (109, 1870)
+    NONZERO 0
+    Training accuracy 0.6929133858267716
+                 precision    recall  f1-score   support
+
+        Stage I       0.69      1.00      0.82        75
+       Stage II       0.00      0.00      0.00        34
+
+    avg / total       0.47      0.69      0.56       109