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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,136 @@ | ||
| import logging | ||
| import sys | ||
| from pathlib import Path | ||
|
|
||
| import pandas as pd | ||
| from deeplc import DeepLC | ||
| from psm_utils.io import read_file | ||
| from psm_utils.io.exceptions import PSMUtilsIOException | ||
| from psm_utils.io.peptide_record import peprec_to_proforma | ||
| from psm_utils.psm import PSM | ||
| from psm_utils.psm_list import PSMList | ||
| from rich.logging import RichHandler | ||
|
|
||
| from im2deep.calibrate import linear_calibration | ||
|
|
||
| LOGGER = logging.getLogger(__name__) | ||
| REFERENCE_DATASET_PATH = Path(__file__).parent / "reference_data" / "reference_ccs.zip" | ||
|
|
||
| def predict_ccs( | ||
| file_pred, | ||
| file_cal=None, | ||
| file_reference=REFERENCE_DATASET_PATH, | ||
| file_pred_out=None, | ||
| model_name="tims", | ||
| calibrate_per_charge=True, | ||
| use_charge_state=2, | ||
| n_jobs=None, | ||
| ): | ||
| """Run IM2Deep.""" | ||
| LOGGER.info("IM2Deep started.") | ||
| reference_dataset = pd.read_csv(file_reference) | ||
|
|
||
| with open(file_pred) as f: | ||
| first_line_pred = f.readline().strip() | ||
| if file_cal: | ||
| with open(file_cal) as fc: | ||
| first_line_cal = fc.readline().strip() | ||
|
|
||
| if "modifications" in first_line_pred.split(",") and "seq" in first_line_pred.split(","): | ||
| # Read input file | ||
| df_pred = pd.read_csv(file_pred) | ||
| df_pred.fillna("", inplace=True) | ||
|
|
||
| list_of_psms = [] | ||
| for seq, mod, charge, ident in zip( | ||
| df_pred["seq"], df_pred["modifications"], df_pred["charge"], df_pred.index | ||
| ): | ||
| list_of_psms.append( | ||
| PSM(peptidoform=peprec_to_proforma(seq, mod, charge), spectrum_id=ident) | ||
| ) | ||
| psm_list_pred = PSMList(psm_list=list_of_psms) | ||
|
|
||
| else: | ||
| # psm_list_pred = read_file(file_pred) | ||
| try: | ||
| psm_list_pred = read_file(file_pred) | ||
| except PSMUtilsIOException: | ||
| LOGGER.error("Invalid input file. Please check the format of the input file.") | ||
| sys.exit(1) | ||
|
|
||
| psm_list_cal = [] | ||
| if ( | ||
| file_cal | ||
| and "modifications" in first_line_cal.split(",") | ||
| and "seq" in first_line_cal.split(",") | ||
| ): | ||
| df_cal = pd.read_csv(file_cal) | ||
| df_cal.fillna("", inplace=True) | ||
| del file_cal | ||
|
|
||
| list_of_cal_psms = [] | ||
| for seq, mod, charge, ident, CCS in zip( | ||
| df_cal["seq"], df_cal["modifications"], df_cal["charge"], df_cal.index, df_cal["CCS"] | ||
| ): | ||
| list_of_cal_psms.append( | ||
| PSM(peptidoform=peprec_to_proforma(seq, mod, charge), spectrum_id=ident) | ||
| ) | ||
| psm_list_cal = PSMList(psm_list=list_of_cal_psms) | ||
| psm_list_cal_df = psm_list_cal.to_dataframe() | ||
| psm_list_cal_df["observed_ccs"] = df_cal["CCS"] | ||
| del df_cal | ||
|
|
||
| else: | ||
| LOGGER.error("Invalid calibration file. Please check the format of the calibration file.") | ||
| sys.exit(1) | ||
|
|
||
| if model_name == "tims": | ||
| path_model = Path(__file__).parent / "models" / "TIMS" | ||
|
|
||
| path_model_list = list(path_model.glob("*.hdf5")) | ||
|
|
||
| dlc = DeepLC(path_model=path_model_list, n_jobs=n_jobs, predict_ccs=True) | ||
| LOGGER.info("Predicting CCS values...") | ||
| preds = dlc.make_preds(psm_list=psm_list_pred, calibrate=False) | ||
| LOGGER.info("CCS values predicted.") | ||
| psm_list_pred_df = psm_list_pred.to_dataframe() | ||
| psm_list_pred_df["predicted_ccs"] = preds | ||
|
|
||
| calibrated_psm_list_pred_df = linear_calibration( | ||
| psm_list_pred_df, | ||
| calibration_dataset=psm_list_cal_df, | ||
| reference_dataset=reference_dataset, | ||
| per_charge=calibrate_per_charge, | ||
| use_charge_state=use_charge_state, | ||
| ) | ||
|
|
||
| LOGGER.info("Writing output file...") | ||
| if file_pred_out: | ||
| file_pred_out = open(file_pred_out, "w") | ||
| file_pred_out.write("seq,modifications,charge,predicted CCS\n") | ||
| for seq, mod, charge, ident, CCS in zip( | ||
| df_pred["seq"], | ||
| df_pred["modifications"], | ||
| df_pred["charge"], | ||
| df_pred.index, | ||
| calibrated_psm_list_pred_df["predicted_ccs_calibrated"], | ||
| ): | ||
| file_pred_out.write(f"{seq},{mod},{charge},{CCS}\n") | ||
| file_pred_out.close() | ||
| else: | ||
| #Get path of psm file | ||
| output_file = Path(file_pred).parent / (Path(file_pred).stem + "_IM2Deep-predictions.csv") | ||
| LOGGER.info("Writing output file to %s", output_file) | ||
| output_file = open(output_file, "w") | ||
| output_file.write("seq,modifications,charge,predicted CCS\n") | ||
| for seq, mod, charge, ident, CCS in zip( | ||
| df_pred["seq"], | ||
| df_pred["modifications"], | ||
| df_pred["charge"], | ||
| df_pred.index, | ||
| calibrated_psm_list_pred_df["predicted_ccs_calibrated"], | ||
| ): | ||
| output_file.write(f"{seq},{mod},{charge},{CCS}\n") | ||
| output_file.close() | ||
|
|
||
| LOGGER.info("IM2Deep finished!") |