The p1h from levinas

p1h's Introduction

Pilot1 Hackathon

Python functions and command line utilities for working with standard pilot1 data sets.

git clone https://github.com/levinas/p1h
pip install -U pandas scikit-learn xgboost

Command-line Examples

Scripts exist for dataframe export and prediciton tasks.

Export by-drug data

Save molecular features and dose reponse data for given drugs to CSV files:

$ python dataframe.py --by drug --drugs 100071 --feature_subsample 10

NSC 100071: saved 52 rows and 11 columns to NSC_100071.csv

Export by-cell data

Save drug features and dose response data for given cell lines to CSV files:

$ python dataframe.py --by cell --cells BR:MCF7 CNS:SF_268

BR:MCF7: saved 15628 rows and 3811 columns to BR:MCF7.csv
CNS:SF_268: saved 28151 rows and 3811 columns to CNS:SF_268.csv

By-drug regression runs

Run three regression models on drug set A (defined by Jason to include 306 drugs) using all types of cell line features (expression, miRNA and proteome), and save feature importance and model performance evaluated on various metrics to files.

$ python by_drug.py --drugs A --models randomforest lasso elasticnet --cell_features all

Code Examples

Run standard regressions on a drug

from datasets import NCI60
from skwrapper import regress

df = NCI60.load_by_drug_data(drug='100071')
regress('XGBoost', df)
regress('Lasso', df)

Sweep customized RandomForest regression on all cell lines

from datasets import NCI60
from skwrapper import regress
from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(n_estimators=20)

cells = NCI60.all_cells()
for cell in cells:
    df = NCI60.load_by_cell_data(cell)
    regress(model, df, cv=3)

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