Update README.md

README.md

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 # RNAcompete_classifier
-This repository contains the code and data for training the RNAcompete experiment classifier used in Ray et al. 2022
+This repository contains the code and data for training the RNAcompete experiment classifier used in Ray *et al.* 2022. To reproduce the results in the paper, follow the instructions below.
+
+## File Descriptions
+
+`RNAcompete_LR_classifier.py`: Contains the Python script that trains the classifier, tests the classifier, and applies the classifier to all ucRBP experiments.
+
+`training_set.txt`: Tab-delimited file containing the feature matrix for training. Each line is an individual RNAcompete experiment. The first column (class) indicates whether it was a passed (1) or failed (0) experiment. Subsequent columns contain values for each of the features as described in the paper.
+
+`testing_set.txt`: Tab-delimited file with the same format as `training_set.txt`. This file contains the 40 experiments used to test the classifier.
+
+`ucrbp_experiment_features.txt`: Tab-delimited file with the same format as `training_set.txt` and `testing_set.txt`, EXCEPT for the 'class' column. Contains feature values for all ucRBP experiments (full-length ucRBPs and truncated constructs). 
+
+`train_IDs.txt`: Contains the RNAcompete experiment identifiers for all experiments in `training_set.txt`.
+
+`test_IDs.txt`: Contains the RNAcompete experiment identifiers for all experiments in `testing_set.txt`.
+
+`ucrbp_IDs.txt`: Contains the RNAcompete experiment identifiers for all experiments in `ucrbp_experiment_features.txt`. IDs in this file correspond to IDs in Supplementary Table 1.
+
+For all `*IDs.txt` files, the IDs correspond line-by-line to the values in `*_set.txt` and `ucrbp_experiment_features.txt`. For example, to directly view the feature values for ucRBP experiments by ID, the files can be merged as follows:
+
+  `paste ucrbp_IDs.txt ucrbp_experiment_features.txt`
+
+## Train & Apply the Classifier
+
+Clone the repository and ensure the following requirements are installed:
+
+- python3 version 3.8
+- numpy version 1.19.2
+- sklearn version 0.23.2
+- skopt version 0.8.1
+- pickleshare version 0.7.5
+
+Run the following from within the directory:
+
+  `python RNAcompete_LR_classifier.py`
+
+## Output File Descriptions
+
+`LR_model.sav`: Contains a "pickled" version of the logistic regression model trained in `RNAcompete_LR_classifier.py`. This model can be loaded in python by running the following:
+  ```
+  import pickle
+  lr = pickle.load(open('LR_model.sav', 'rb'))
+  ```
+
+`LR_coefficients.txt`: Tab-delimited file containing one line for each feature along with the weight of the feature in the model.
+
+`test_set_AUROC.txt`: Contains the AUROC for the test set.
+
+`test_set_probability_estimates.txt`: Contains one line per test set experiment with the probability estimate that the experiment belongs to class 1 (passed experiments).
+
+`ucrbp_probability_estimates.txt`: Contains one line per ucRBP experiment with the probability estimate that the experiment belongs to class 1 (passed experiments).
+
+For both `*_probability_estimates.txt` files, the probability estimates correspond line-by-line to the associated `*IDs.txt` files (and thus the files containing the features as well). For example, to directly view the probability estimates for ucRBP experiments by ID, the files can be merged as follows:
+
+  `paste ucrbp_IDs.txt ucrbp_probability_estimates.txt`
+
+
+