Migrating from DeepLC 3.x

DeepLC 4.0 is not backward compatible with v3. Update your code using the guide below.

Functional API replaces the DeepLC class

The DeepLC class is removed. Use the top-level functions from deeplc instead:

v3	v4
DeepLC().calibrate_preds(psm_list)	deeplc.calibrate()
DeepLC().make_preds(psm_list)	deeplc.predict()
(no equivalent)	deeplc.predict_and_calibrate() (predict + calibrate in one call)
(no equivalent)	deeplc.finetune_and_predict() (fine-tune + predict in one call)

Example:

# v3
from deeplc import DeepLC
dlc = DeepLC(path_model="model.hdf5")
dlc.calibrate_preds(psm_list_cal)
preds = dlc.make_preds(psm_list)

# v4
from deeplc import predict_and_calibrate
preds = predict_and_calibrate(psm_list, psm_list_reference=psm_list_cal)

Input format changes

The legacy tab-separated format with seq and modifications columns is no longer supported. Use any format supported by psm_utils instead, or a tab-separated file with peptidoform and spectrum_id columns.

Peptide sequences now use ProForma 2.0 notation. Modifications are encoded as bracketed labels (e.g. PEPTM[Oxidation]IDE or PEPTM[Formula:C1H2O]IDE), not as a separate column.

See Input file format in the Usage guide for details.

Model checkpoints

Legacy .hdf5 checkpoints from v3 are not compatible with v4. The bundled model has been retrained as a PyTorch multitask model (multitask_model.pt). Custom .hdf5 checkpoints cannot be loaded; retrain using the v4 API. The new model and should serve as an ideal starting point for fine-tuning to any custom setup.

Backend: TensorFlow → PyTorch

The deep learning backend changed from TensorFlow to PyTorch. TensorFlow is no longer a dependency. GPU support uses PyTorch CUDA; install the appropriate PyTorch build from pytorch.org if GPU acceleration is needed.