DAGIP: A bias correction algorithm for cell-free DNA data

Given two groups of matched (preferentially paired) samples sequenced under different protocols, the tool explicitly learns the bias using a neural network. The approach builds on Optimal Transport theory, and exploits sample-to-sample similarities to define how to perform bias correction.

Documentations are available at jorisvermeeschlab.github.io/DAGIP/. The source code has also been archived on Zenodo: https://zenodo.org/records/14503340.

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Install the tool

Install dependencies:

pip install -r requirements.txt

To reproduce the results of our manuscript, rpy2 should be installed, as well the following R packages: dplyr, GenomicRanges and dryclean.

Install the package:

python setup.py install --user

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Basic usage:

from dagip.core import DomainAdapter

# Let X and Y be two numpy arrays, where the rows are (coverage, methylation, fragmentomic) profiles and columns are features (e.g., DMRs, bins). Y and X have been produced under sequencing protocols 1 and 2, respectively.

# Build a model from the matched groups
model = DomainAdapter()
X_adapted = model.fit_transform(X, Y)

# X_adapted is a numpy array of same dimensions as X

# Save the model
model.save('some/location.pt')

...

# Load the model
model.load('some/location.pt')

# Perform bias correction on new independent samples from sequencing protocol 2
X_new_adapted = model.transform(X_new)

For more advanced usage, please check the documentation.

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Reproduce the results from the manuscript

To reproduce the results presented in our paper, first download the datasets from the FigShare repository (DOI: 10.6084/m9.figshare.24459304) and place its content in the data folder. The repository is available at https://figshare.com/s/5f837a93ea2719ffcaf9.

Preprocess the data:

python data/preprocess.py
python data/50kb-to1mb.py
python data/to-numpy.py

Cancer detection

python scripts/validate-cnas.py HL
python scripts/validate-cnas.py DLBCL
python scripts/validate-cnas.py MM
python scripts/validate-cnas.py OV
python scripts/validate-fragmentomics-multimodal.py

Cross-validation with paired samples

python scripts/identify-pairs.py OV-forward
python scripts/identify-pairs.py NIPT-chemistry
python scripts/identify-pairs.py NIPT-lib
python scripts/identify-pairs.py NIPT-adapter
python scripts/identify-pairs.py NIPT-hs2000
python scripts/identify-pairs.py NIPT-hs2500
python scripts/identify-pairs.py NIPT-hs4000