--- a/README.md
+++ b/README.md
@@ -59,102 +59,102 @@
 
 f | omic_file
 
-> REQUIRED: File path for omics files (should be matrix)
+REQUIRED: File path for omics files (should be matrix)
 
 **NOTE:The file must be in csv format, such as rna.csv. Of course, it can be compressed with gz, such as rna.csv.gz.**. Example: The first line is the header, patient_id and gene (features) names.
 
->  patient_id,A1BG,A1CF,A2BP1,A2LD1,....
->
->  TCGA.KL.8323,3.3491,0.0,0.0,5.8939,....
->
->  TCGA.KL.8324,2.922,0.5557,0.5557,6.4226,....
+patient_id,A1BG,A1CF,A2BP1,A2LD1,....
+
+TCGA.KL.8323,3.3491,0.0,0.0,5.8939,....
+
+TCGA.KL.8324,2.922,0.5557,0.5557,6.4226,....
 
 n | omic_name
 
-> REQUIRED: Omic names for omics files, should be the same order as the omics file
+#### REQUIRED: Omic names for omics files, should be the same order as the omics file
 
 l | label_file
 
-> REQUIRED: File path for label file
+#### REQUIRED: File path for label file
 
 **NOTE:The file must be in csv format, such as label.csv. Of course, it can be compressed with gz, such as label.csv.gz.**. Example: The first line is the header, patient_id and label represent the sample name and sample classification label respectively. 
 
-> patient_id,label
->
-> TCGA.KL.8328,0
->
-> TCGA.KL.8339,0
->
-> TCGA.KM.8439,1
->
-> TCGA.KM.8441,1
->
-> TCGA.KM.8442,1
+ patient_id,label
+
+TCGA.KL.8328,0
+
+TCGA.KL.8339,0
+
+TCGA.KM.8439,1
+
+TCGA.KM.8441,1
+
+TCGA.KM.8442,1
 
 
 **2. Output**
 
 o | outdir
 
-> OPTIONAL: Setting output file path, default=./output
+OPTIONAL: Setting output file path, default=./output
 
 
 **3. Feature selection**
 
 method
 
-> OPTIONAL: Method of feature selection, choosing from ANOVA, RFE, LASSO, PCA, default is no feature selection
+OPTIONAL: Method of feature selection, choosing from ANOVA, RFE, LASSO, PCA, default is no feature selection
 
 percentile
 
-> OPTIONAL: Percent of features to keep for ANOVA (integer between 1-100), only used when using ANOVA, default=30
+OPTIONAL: Percent of features to keep for ANOVA (integer between 1-100), only used when using ANOVA, default=30
 
 num_pc
 
-> OPTIONAL: Number of PCs to keep for PCA (integer), only used when using PCA, default=50
+OPTIONAL: Number of PCs to keep for PCA (integer), only used when using PCA, default=50
 
 FSD
 
-> OPTIONAL: Whether to use FSD to mitigate noise of omics. Default is not using FSD, and set --FSD to use FSD
+OPTIONAL: Whether to use FSD to mitigate noise of omics. Default is not using FSD, and set --FSD to use FSD
 
 i | iteration
 
-> OPTIONAL: The number of FSD iterations (integer), default=10
+OPTIONAL: The number of FSD iterations (integer), default=10
 
 s | seed
 
-> OPTIONAL: Random seed for FSD (integer), default=0
+OPTIONAL: Random seed for FSD (integer), default=0
 
 threshold
 
-> OPTIONAL: FSD threshold to select features (float), default=0.8 (select features that are selected in 80 percent FSD iterations)
+OPTIONAL: FSD threshold to select features (float), default=0.8 (select features that are selected in 80 percent FSD iterations)
 
 
 **4. Building Model**
 
 m | model 
 
-> OPTIONAL: Model names, choosing from DNN, Net (Net for AttentionMOI), RF, XGboost, svm, mogonet, moanna, default=DNN.
+ OPTIONAL: Model names, choosing from DNN, Net (Net for AttentionMOI), RF, XGboost, svm, mogonet, moanna, default=DNN.
 
 t | test_size
 
-> OPTIONAL: Testing dataset proportion when split train test dataset (float), default=0.3 (30 percent data for testing)
+OPTIONAL: Testing dataset proportion when split train test dataset (float), default=0.3 (30 percent data for testing)
 
 b | batch
 
-> OPTIONAL: Mini-batch number for model training (integer), default=32
+OPTIONAL: Mini-batch number for model training (integer), default=32
 
 e | epoch
 
-> OPTIONAL: Epoch number for model training (integer), default=300
+ OPTIONAL: Epoch number for model training (integer), default=300
 
 r | lr
 
-> OPTIONAL: Learning rate for model training(float), default=0.0001
+ OPTIONAL: Learning rate for model training(float), default=0.0001
 
 w | weight_decay
 
-> OPTIONAL: weight_decay parameter for model training (float), default=0.0001
+OPTIONAL: weight_decay parameter for model training (float), default=0.0001
 
 ---