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 This repository contains two branches: MicroVI and MicroVI-retraining. MicroVI contains the base code for implementing MicroVI 5-fold cross-validation 100 times. MicroVI-retraining contains the code for implementing MicroVI with data augmentation, i.e., after each run of the model is trained, a portion of the learned latent space is sampled and used to finetune the trained model.
 
-The run.py file within each project is the main file to run. A sample shell script is provided in myjob.sh. The data for each project is provided within the NEW_DATASETS folder, which is subdivided into a subfolder containing microbiome data with mice on different diet (aka M-DIET, our dataset used for linear regression) and another subfolder containing microbiome data when mice were in different ages (aka M-AGE, our dataset used for classification/logistic regression).
+The run.py file within each project is the main file to run. A sample shell script is provided in myjob.sh. The data for each project is provided within the NEW_DATASETS folder, which is subdivided into a subfolder containing microbiome data with mice on different diets (aka M-DIET, our dataset used for linear regression) and another subfolder containing microbiome data measured when mice were in different ages (aka M-AGE, our dataset used for classification/logistic regression).
 
 To choose which setting to run (i.e., linear regression or classification), modify the first ten lines of the main function within run.py: