Python implementation of the RobNorm R package for robust normalization of quantitative omics data.
- Identical normalization results to the original R implementation.
- Fast and efficient implementation using NumPy (57 ms vs 113 ms in R for 5000x200 data on an M1 MacBook Air)
Install via pip:
pip install rob-normimport pandas as pd
from rob_norm import rob_norm
# load your data into a DataFrame (rows = features, columns = samples)
data = pd.read_csv('./data/simulated_measurements.txt', index_col=0, sep='\t')
# alternatively, simulate data using the provided function
# sim_dat_fn(row_frac, col_frac, mu_up, mu_down, n, m, nu_fix=True, seed=None):
# perform a normalization operation provided by the package
results = rob_norm(data, gamma_0=0.5, tol=1e-4, step=200)
normalized_data = results['norm_data']
# verify against reference results
df_results = pd.read_csv('./data/simulated_measurements_normalized.txt', index_col=0, sep='\t')
assert np.allclose(df_results, normalized_data) # == TrueClone the repository and use uv for development and testing:
git clone https://github.com/Tom-Julux/rob_norm
cd rob_norm
uv run pytest
uv build
uv publishThe project is licensed under the GNU Lesser General Public License v3.0.
See the LICENSE file for full licensing information.
The file data/robnorm.r contains R code used to generate reference results for testing and was copied and adapted from the original R package RobNorm.
