Preparing data for downstream analyses#
To prepare the data for classification#
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)
print(y)
| pathologic_stage | |
|---|---|
| TCGA-05-4390-01A | Stage I |
| TCGA-05-4405-01A | Stage I |
| TCGA-05-4410-01A | Stage I |
| TCGA-05-4417-01A | Stage I |
| TCGA-05-4424-01A | Stage II |
| TCGA-05-4427-01A | Stage II |
| TCGA-05-4433-01A | Stage I |
| TCGA-05-5423-01A | Stage II |
| TCGA-05-5425-01A | Stage II |
| TCGA-05-5428-01A | Stage II |
| TCGA-05-5715-01A | Stage I |
| TCGA-38-4631-01A | Stage I |
| TCGA-38-7271-01A | Stage I |
| TCGA-38-A44F-01A | Stage I |
| TCGA-44-2655-11A | Stage I |
336 rows × 1 columns
Log2 transform the mRNA, microRNA, and lncRNA expression values#
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#
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
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])
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