Update README.md

README.md

@@ -7,13 +7,13 @@ Maintained by Bo Yuan, Judy Shen, and Augustin Luna.
 
 If you want to discuss the usage or to report a bug, please use the 'Issues' function here on GitHub.
 
-If you find CellBox useful for your research, please consider citing the corresponding publication.
+If you find `CellBox` useful for your research, please consider citing the corresponding publication.
 bioRxiv: [link](https://www.biorxiv.org/content/10.1101/746842v3)
 
 For more information, please find our contact information [here](https://www.sanderlab.org/#/).
 # Quick Start
 
-Easily try PertBio online with Binder 
+Easily try `CellBox` online with Binder 
 
 1. Go to: https://mybinder.org/v2/gh/dfci/CellBox/version_for_revision
 2. From the New dropdown, click Terminal 
@@ -28,14 +28,14 @@ Alternatively, in project folder, do the same command
 # Installation
 
 ## Install using pip 
-The following command will install pertbio from a particular branch using the '@' notation:
+The following command will install cellbox from a particular branch using the '@' notation:
 
 ```
-pip install git+https://github.com/dfci/CellBox.git@version_for_revision#egg=pertbio\&subdirectory=pertbio
+pip install git+https://github.com/dfci/CellBox.git@version_for_revision#egg=cellbox\&subdirectory=cellbox
 ```
 
 ## Install using setup.py
-Clone repository and in the pertbio folder run:
+Clone repository and in the `cellbox` folder run:
 
 ```
 python3.6 setup.py install
@@ -46,34 +46,34 @@ Only python3.6 supported. Anaconda or pipenv is recommended to create python env
 Now you can test if the installation is successful
 
 ```
-import pertbio
-pertbio.VERSION
+import cellbox
+cellbox.VERSION
 ```
 
 # Project Structure
 
 ## Data files: in ./data/ folder
-* _node_index.txt_: names of each protein/phenotypic node.
-* _expr_index.txt_: information each perturbation condition (also see loo_label.csv).
-* _expr.csv_: Protein expression data from RPPA for the protein nodes and phenotypic node values. Each row is a condition while each column is a node.
-* _pert.csv_: Perturbation strength and target of all perturbation conditions. Used as input for differential equations.
+* `node_index.txt`: names of each protein/phenotypic node.
+* `expr_index.txt`: information each perturbation condition (also see loo_label.csv).
+* `expr.csv`: Protein expression data from RPPA for the protein nodes and phenotypic node values. Each row is a condition while each column is a node.
+* `pert.csv`: Perturbation strength and target of all perturbation conditions. Used as input for differential equations.
 
-## pertbio package:
-* _CellBox_ is defined in model.py
-* dataset factory for random parition and leave one out tasks
-* some training util functions in tensorflow
+## cellbox package:
+* `CellBox` is defined in model.py
+* A dataset factory function for random parition and leave one out tasks
+* Some training util functions in tensorflow
 
 ## One click model construction
 
 ### __Step 1: Create experiment json files (some examples can be found under ./configs/)__
 * Make sure to specify the experiment_id and experiment_type
-	* experiment_id: name of the experiments, would be used to generate results folders
-	* experiment_type: currently available tasks are {"random partition", "leave one out (w/o single)", "leave one out (w/ single)", "full data", "single to combo"]}
-* Different training stages can be specified using stages and sub_stages
+	* `experiment_id`: name of the experiments, would be used to generate results folders
+	* `experiment_type`: currently available tasks are {"random partition", "leave one out (w/o single)", "leave one out (w/ single)", "full data", "single to combo"]}
+* Different training stages can be specified using `stages` and `sub_stages` in config file
 
 ### __Step 2: Use main.py to construct models using random partition of dataset__
 
-The experiment type configuration file is specified by --experiment_config_path OR -config
+The experiment type configuration file is specified by `--experiment_config_path` or `-config`
 
 ```
 python scripts/main.py -config=configs/Example.random_partition.cfg.json
@@ -82,23 +82,22 @@ python scripts/main.py -config=configs/Example.random_partition.cfg.json
 Note: always run the script in the root folder.
 
 
-A random seed can also be assigned by using argument --working_index OR -i
+A random seed can also be assigned by using argument `--working_index` or `-i`
 
 ```
 python scripts/main.py -config=configs/Example.random_partition.cfg.json -i=1234
 ```
 
 
-When training with leave-one-out validation, make sure to specify the drug index --drug_index OR -drug to leave out from training.
+When training with leave-one-out validation, make sure to specify the drug index `--drug_index` or `-drug` to leave out from training.
 
 
 ### __Step 3: Analyze result files__
-* You should see a experiment folder generated under results using the date and experiment_id.
+* You should see a experiment folder generated under results using the date and `experiment_id`.
 * Under experiment folder, you would see different models run with different random seeds
 * Under each model folder, you would have:
-	* _record_eval.csv_: log file with loss changes and time used.
-	* _random_pos.csv_: how the data was split (only for random partitions)
-	* _best.W_, _best.alpha_, _best.eps_: model parameters snapshot for each training stage
-	* predicted training set .csv, predicted nodes of training set (average prediction over last 20% of ODE simulation, time derivative at end point, max-min over last 20% ODE simulation)
-	* _best.test_hat_: Prediction on test set, using the best model for each stage
-	* _.ckpt_ files are the final models in tensorflow compatible format.
+	* `record_eval.csv`: log file with loss changes and time used.
+	* `random_pos.csv`: how the data was split (only for random partitions)
+	* `best.W`, `best.alpha`, `best.eps`: model parameters snapshot for each training stage
+	* `best.test_hat`: Prediction on test set, using the best model for each stage
+	* `.ckpt` files are the final models in tensorflow compatible format.