Skip to content

CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data

License

Notifications You must be signed in to change notification settings

CSB5/CaDRReS-Sc

Repository files navigation

Introduction

CaDRReS-Sc is an AI/ML framework for robust cancer drug response prediction based on single-cell RNA-sequencing (scRNA-seq) data. It extends an existing recommender system model (CaDRReS, Suphavilai et al., 2018) with new features calibrated for diverse cell types, and accounting for tumor heterogeneity (Suphavilai et al., 2020). In addition to monotherapy response, CaDRReS-Sc can also predict response to combinatorial therapy targeting tumor sub-clones.

Key features:

Usage

git clone https://github.com/CSB5/CaDRReS-Sc.git

CaDRReS-Sc is based on Python 3.x

Required packages

Optional package

  • Scanpy (for single-cell clustering)

Usage examples

Please refer to our tutorial notebooks. Below are snippets from the notebooks:

Model training (tutorial)

model_dict, output_dict = model.train_model_logistic_weight(
    Y_train, X_train,                 # Y = drug response; X = kernel features
    Y_test, X_test, 
    sample_weights_logistic_x0_df,    # Sample weight w.r.t. maximum drug dosage
    indication_weight_df,             # High weight for specific tissue types
    10, 0.0, 100000, 0.01,            # Hyperparamters
    model_spec_name=model_spec_name,  # Select objective function
    save_interval=5000,
    output_dir=output_dir)

Predicting monotherapy response based on a pre-trained model (tutorial)

cadrres_model = model.load_model(model_file)
pred_df, P_df = model.predict_from_model(cadrres_model, X_test, model_spec_name)
pred_df.head() # Predicted drug response (log2 IC50)
D1 D1001 D1003 D1004 ...
906826 3.86 11.11 -5.71 -5.56 ...
687983 7.00 11.52 -4.12 -4.48 ...
910927 1.74 10.84 -7.32 -6.77 ...
1240138 3.55 11.42 -4.79 -4.82 ...
1240139 2.79 10.70 -7.89 -7.42 ...
P_df.head() # A latent vector of each sample in the 10D pharmacogenomic space
1 2 3 4 5 6 7 8 9 10
906826 0.38 -1.39 -1.26 -0.15 -0.37 -1.35 1.09 0.04 -0.78 0.37
687983 0.26 -0.68 0.47 1.31 0.61 0.93 -0.09 -0.77 -2.20 -0.42
910927 -0.52 0.47 0.12 -0.10 -1.56 -2.99 1.15 -0.06 -0.58 -1.21
1240138 0.72 0.51 -1.16 -0.34 1.56 -1.21 1.06 -0.59 0.08 -0.53
1240139 -0.08 -0.45 0.45 -0.19 -0.75 -2.93 0.50 0.67 -0.11 0.27

Predicting response to drug combinations at specific dosages (tutorial)

Inputs:

freq_df Proportion of each cell cluster in each sample

cluster_gene_exp_df Cluster-specific gene expression profiles

drug_df Drug dosage information

Example outputs:

drug_combi_pred_df.head() # Predicted cell death percentage at specific dosage
patient drug_id_A drug_id_B cell_death_A cell_death_B cell_death_combi improve
HN120 D1007 D133 8.52 75.63 77.19 1.56
HN120 D1007 D201 8.52 60.39 63.04 2.65
HN120 D1007 D1010 8.52 15.31 22.39 7.08
HN120 D1007 D182 8.52 64.66 67.22 2.56
HN120 D1007 D301 8.52 63.39 66.39 3.00

Citation

Suphavilai C, Chia S, Sharma A et al. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures. Genome Medicine 13, Article number: 189 (2021)

Suphavilai C, Bertrand D, Nagarajan N. Predicting cancer drug response using a recommender system. Bioinformatics 2018 Nov 15;34(22):3907-14.

Contact information

For additional information, help and bug reports please email Chayaporn Suphavilai ([email protected])

About

CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published