Source code for tka.external_models.moshkov

import chemprop
import numpy as np
from tka.utils import (
    transform_moshkov_outputs,
    prepare_df_for_mobc_predictions,
    load_mobc_ordered_feature_columns,
    load_l1000_ordered_feature_columns
)
import importlib
from typing import List, Union
import pandas as pd
import os

[docs]def load_assay_metadata() -> pd.DataFrame: with importlib.resources.path('tka.data', 'assay_metadata.csv') as file_path: return pd.read_csv(file_path)
[docs]def predict_from_smiles(smiles_list: List[str], checkpoint_dir: str) -> pd.DataFrame: """ Make predictions from a list of SMILES strings using a trained checkpoint. Args: smiles_list (List[str]): List of SMILES strings for which to make predictions. checkpoint_dir (str): Directory containing the trained checkpoint. Returns: pd.DataFrame: Predictions with SMILES as indices and assays as columns. """ arguments = [ '--test_path', '/dev/null', '--preds_path', '/dev/null', '--checkpoint_dir', checkpoint_dir, '--no_features_scaling' ] args = chemprop.args.PredictArgs().parse_args(arguments) preds = chemprop.train.make_predictions(args=args, smiles=smiles_list) return transform_moshkov_outputs( identifier_col_vals=smiles_list, output=preds, use_full_assay_names=True )
[docs]def predict_from_mobc(df_real: pd.DataFrame, checkpoint_dir: str) -> pd.DataFrame: """ Make predictions from a dataframe of batch effect corrected morphology profiles from CellProfiler and a trained model checkpoint. Args: df_real (pd.DataFrame): a pd.DataFrame with the columns being CellProfiler features (1746 features) and the index column being the identification column checkpoint_dir (str): Directory containing the trained checkpoint. Returns: pd.DataFrame: Predictions with df_real's first column as indices and assays as columns. """ # Generate and save a dummy smiles CSV file to comply with chemprop_predict # Serves no real purpose and does not affect the final predictions in any way dummy_smiles = ["CCCC" for _ in range(len(df_real))] with open("tmp_smiles.csv", "w") as file: for item in ["smiles"] + dummy_smiles: file.write(item + "\n") # Load the MOBC ordered features to generate .npz file mobc_features = load_mobc_ordered_feature_columns() # Save the pd.DataFrame so that you can load it from a path np.savez("out.npz", features=df_real[mobc_features].to_numpy()) arguments = [ "--test_path", "tmp_smiles.csv", "--preds_path", "/dev/null", "--checkpoint_dir", checkpoint_dir, "--features_path", "out.npz", "--no_features_scaling", ] args = chemprop.args.PredictArgs().parse_args(arguments) preds = chemprop.train.make_predictions(args=args) # Remove temporary files os.remove("out.npz") os.remove("tmp_smiles.csv") return transform_moshkov_outputs( identifier_col_vals=list(df_real.index), output=preds, use_full_assay_names=True )
[docs]def predict_from_ge(df: List[str], gene_id: str, checkpoint_dir: str) -> pd.DataFrame: """ Make predictions from a df of standard scaled gene expressions and a trained model checkpoint. Args: df (pd.DataFrame): a pd.DataFrame with the columns being L1000 features (977 features) and the index column being the identification column checkpoint_dir (str): Directory containing the trained checkpoint. gene_id (str): type of identifier present in the header row - one of "affyID", "entrezID" or "ensemblID" Returns: pd.DataFrame: Predictions with df's first column as indices and assays as columns. """ # Generate and save a dummy smiles CSV file to comply with chemprop_predict # Serves no real purpose and does not affect the final predictions in any way dummy_smiles = ["CCCC" for _ in range(len(df))] with open("tmp_smiles.csv", "w") as file: for item in ["smiles"] + dummy_smiles: file.write(item + "\n") valid_gene_ids = ["affyID", "entrezID", "ensemblID"] if gene_id not in valid_gene_ids: raise ValueError(f"Invalid gene_id argument -> ({gene_id}). Should be one of {valid_gene_ids}.") # Load the MOBC ordered features to generate .npz file l1000_features = load_l1000_ordered_feature_columns(gene_id) # Save the pd.DataFrame so that you can load it from a path np.savez("out.npz", features=df[l1000_features].to_numpy()) arguments = [ "--test_path", "tmp_smiles.csv", "--preds_path", "/dev/null", "--checkpoint_dir", checkpoint_dir, "--features_path", "out.npz", "--no_features_scaling", ] args = chemprop.args.PredictArgs().parse_args(arguments) preds = chemprop.train.make_predictions(args=args) # Remove temporary files os.remove("out.npz") os.remove("tmp_smiles.csv") return transform_moshkov_outputs( identifier_col_vals=list(df.index), output=preds, use_full_assay_names=True )
if __name__ == "__main__": # predict_from_smiles( # smiles_list=["CCC"], # checkpoint_dir="/home/filip/Downloads/Moshkov(etal)-single-models/2021-02-cp-es-op" # ) common_path = "/home/filip/Documents/TKA/2023_Moshkov_NatComm/analysis/" df_real = pd.read_csv(common_path + "real.csv") df_real = df_real.iloc[:10, :] df_dmso = pd.read_csv(common_path + "dmso.csv") out_df = prepare_df_for_mobc_predictions( df_dmso=df_dmso, df_real=df_real, identifier_col="Metadata_pert_id" ) out = predict_from_mobc( df_real=out_df, checkpoint_dir="/home/filip/Downloads/Moshkov(etal)-single-models/2021-02-mobc-es-op", ) print(out)