1. SVM_ROC.py

1.1. Description

Plot Receiver operating characteristic (ROC) curves using K-fold cross-validation.

Options:

--version	show program’s version number and exit
-h, --help	show this help message and exit
-i INPUT_FILE, --input_file=INPUT_FILE
	Tab or space separated file. The first column contains sample IDs; the second column contains sample labels in integer (must be 0 or 1); the third column contains sample label names (string, must be consistent with column-2). The remaining columns contain featuers used to build SVM model.
-o OUT_FILE, --output=OUT_FILE
	The prefix of the output file.
-n N_FOLD, --nfold=N_FOLD
	The original sample is randomly partitioned into n equal sized subsamples (2 =< n <= 10). Of the n subsamples, a single subsample is retained as the validation data for testing the model, and the remaining n − 1 subsamples are used as training data. default=5.
-C C_VALUE, --cvalue=C_VALUE
	C value. default=1.0
-s RAND_SEED, --seed=RAND_SEED
	random_state seed used by the random number generator. default=0
-k S_KERNEL, --kernel=S_KERNEL
	Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. default=linear
--xl=X_LOW	The lower limit of X-axis (false positive rate). default=-0.05
--xu=X_UPPER	The upper limit of X-axis (false positive rate). default=0.5
--yl=Y_LOW	The lower limit of Y-axis (true positive rate). default=0.5
--yu=Y_UPPER	The upper limit of Y-axis (true positive rate). default=1.05

1.2. Input files format

ID	Label	Label_name	feature_1	feature_2	feature_3	…	feature_n
sample_1	1	WT	1560	795	0.9716	…	feature_n
sample_2	1	WT	784	219	0.4087	…	feature_n
sample_3	1	WT	2661	2268	1.1691	…	feature_n
sample_4	0	Mut	643	198	0.5458	…	feature_n
sample_5	0	Mut	534	87	1.0545	…	feature_n
sample_6	0	Mut	332	75	0.5115	…	feature_n

1.3. Command

$ python3  SVM_ROC.py -i lung_CES_5features.tsv -o output_ROC -C 10