This project applies feature selection to highly confident differentailly expressed (DE) transcripts, and employs supervised machine learning algorithms (e.g. Random Forest, Support Vector Machine, Neural Network, Gradient Boosting) to train model and predict patient labels (i.e. cancer, polys, normal).

python get-pip.py

sudo pip install numpy
sudo pip install scipy
sudo pip install matplotlib
sudo pip install scikit-learn

Put normalized expression matrix, quality control file, sample sheet file into

data/<project name>

This pipeline includes assessment of sample quality, split of training/testing sets, analysis of significantly DE genes in training set, training of ML models, and testing of prediction quality. Input arguments in scripts/run_pipeline.sh:

DIR_SCRIPTS=<complete path to script directory>
DIR_DATA=<complete path to data resources>
NUM_SAMPLES=<number of samples>
GROUP=<label comparison>
THLD_PVAL=<threshold of p value>
THLD_FC=<threshold of fold change>
NORMALIZED_CHIPDATA=<filename of normalized expression matrix>
QC_TABLE=<filename of quality control table>
SAMPLE_SHEET=<filename of sample sheet>

bash scripts/run_pipeline.sh