Deep learning cancer classification using methylation profiles
This repository contains the code to construct deep learning neural networks that can take methylation data as inputs and predict cancer type.
01_Data_Processing: Contains the initital scripts to clean data
03_Analysis: Scripts to carry out different analyses on the Full Cohort (7 different disease statuses) or the GI cohort (5 disease statuses).
scripts: .py and sh batch scripts used to run code on server
PCA scripts:
- We performed Principal Component Analysis and identified the top PCs that explained 90% of the variance (only performed on Full Cohort)
Logistic Regression Scripts:
- Scripts to run logistic regression analysis to provide benchmark classification performance to compare Neural Network performance
Random Forest scripts:
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Featurization scripts indicate scripts run on the training set where features from the Variable Importance table were abstracted and included in neural network analyses
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Classifier/Classification scripts indicate scripts trained on the training set and then testing set to provide benchmark classification performance to compare Neural Network performance
Neural Network scripts:
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Clinical in the script name indicates that (gender, age, mean fragment length) were inlcuded
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RF in the script name indicates that features from random forest (abstracted from featurization ipynb scripts) were included