Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-seq data
- Add trained adata.h5ad objects for 6 data examples.
- Fix the issues of na values filling when loading the drug labels in scmodel.py and bulkmodel.py
- Change the default directory to ./saved to ./save.
- Added the function to use clustering labels to help transfer learning. Details are listed in the usage section.
- Migrate the source of testing data from the FTP to OneDrive.
The following packages are required to run the program:
- conda 4.8.4
- python 3.7.3
- torch 1.3.0
- sklearn 0.23.0
- imblearn 0.7.0
- scanpy 1.6.0
- captum 0.4.1
This software is developed and tested in the following software environment:
Python 3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
This software is developed and tested on the hardware environments of:
- CPU: HexaCore Intel Core i7-9750H, 4100 MHz (41 x 100)
- RAM: 32GB
- GPU: nVIDIA GeForce GTX 1660 Ti with Max-Q Design
No non-standard hardware is required for the software.
The software is a stand-alone python script package. It can be downloaded and installed with this github repository:
git clone https://github.com/OSU-BMBL/scDEAL.git
Or download the .zip file from the repository/ acquire the .zip file then decompress .zip file. The download time depends on the network speed of the user. No extra compile or installation time is required to run our main script. The installation time of dependencies is normally 1 minute per package.
Please create download the zip format dataset from the data.zip link inside:
Click here to download data.zip
The file "data.zip" includes all the datasets we have tested. Please extract the zip file and place it in the root directory of the scDEAL folder.
| Author | Drug | GEO access | Cells | Species | Cancer type | |
|---|---|---|---|---|---|---|
| Data 1&2 | Sharma, et al. | Cisplatin | GSE117872 | 548 | Homo sapiens | Oral squamous cell carcinomas |
| Data 3 | Kong, et al. | Gefitinib | GSE112274 | 507 | Homo sapiens | Lung cancer |
| Data 4 | Schnepp, et al. | Docetaxel | GSE140440 | 324 | Homo sapiens | Prostate Cancer |
| Data 5 | Aissa, et al. | Erlotinib | GSE149383 | 1496 | Homo sapiens | Lung cancer |
| Data 6 | Bell, et al. | I-BET-762 | GSE110894 | 1419 | Mus musculus | Acute myeloid leukemia |
The organization of the directory should be similar as follows:
scDEAL
│ README.md
│ bulkmodel.py
│ scmodel.py
| ...
└───data
│ │ ALL_expression.csv
│ │ ALL_label_binary_wf.csv
│ │ ...
│ └───GSE110894
│ | │ GSE110894.csv
│ | │ GSE110894_CellInfo.xlsx
│ | ...
│ └───GSE112274
│ └───GSE117872
│ └───GSE140440
│ └───GSE149383
│ | ...
└───save
│ │
| └───logs
│ │ ...
| └───figures
│ │ ...
| └───models
│ │ ...
│ └───adata
│ | │
│ | └───data
│ │ ...
└───DaNN
│ │ ...
└───scanpypip
│ │ ...
└───figures
│ │ ...
Folders in our package will store the corresponding contents:
- root: python scripts to run the program and README.md
- data: datasets required for the learning
- save/logs: log and error files that record running status.
- save/figures & figures: figures generated through the run.
- save/models: models trained through the run.
- save/adata: results AnnData outputs.
- DaNN: python scripts describe the model.
- scanpypip: python scripts of utilities.
Two main scripts to run the program are bulkmodel.py and scmodel.py Run bulkmode.py first using the python command line to train the source model.
For example, run bulkmode.py with user-defined parameters:
python bulkmodel.py --drug 'I-BET-762' --dimreduce 'AE' --encoder_h_dims "256,256" --predictor_h_dims "128,64" --bottleneck 256 --data_name 'GSE110894' --sampling 'SMOTE' --dropout 0.3 --printgene 'F' -mod 'new'
This step takes the expression profile of bulk RNA-Seq and the drug response annotations as input. Iw will train a drug sensitivity predictor for the drug 'I-BET-762.' The output model will be stored in the directory "save/models." The prefix of the model's file name will be 'bulk_predictor_ae_' and its full name will be dependent on parameters that users insert. In this case. The file name of the bulk model will be "bulk_predictor_AEI-BET-762.pkl". For all available drug names, please refer to the columns names of files: ALL_label_binary_wf.csv.
For the transfer learning, we provide a built-in testing case of acute myeloid leukemia cells Bell et al. accessed from Gene Expression Omnibus (GEO) accession GSE110894. The training time for the cases are:
python scmodel.py --sc_data 'GSE110894' --dimreduce 'AE' --drug 'I-BET-762' --bulk_h_dims "256,256" --bottleneck 256 --predictor_h_dims "128,64" --dropout 0.3 --printgene 'F' -mod 'new'
This step trains the scDEAL model and predicts the sensitivity of I-BET-762 of the input scRNA-Seq data from GSE110984. Remember that the dimension of the encoder and predictor should be identical (--bulk_h_dims "256,256" --bottleneck 256) in two steps. Other applicable drugs and their resistance can be viewed from the table provided in the file:
The training time of the test case including bulk-level and single-cell-level training on the testing computer was 4 minutes.
The expected output format of scDEAL is the AnnData object (.h5ad) applied by the scanpy package. The file will be stored in the directory "scDEAL//adata/data/". The prediction of sensitivity will be stored in adata.obs["sens_label"] (if you load your AnnDdata object named as adata) where 0 represents resistance and 1 represents sensitivity respectively. Further analysis for the output can be processed by the package Scanpy. The object can be loaded into python through the function: scanpy.read_h5ad.
The expected output format of a successful run show includes:
scDEAL
| ...
└───data
│ │ ...
└───save
│ └───adata
│ | │
│ | └───data
│ │ GSE110894[a timestamp].h5ad
│ │ ...
| └───models
│ │ bulk_endoder_ae_256.pkl
│ │ bulk_predictor_AEIBET-762.pkl
│ │ sc_predictor_DaNN.pkl
│ │ ...
For your input count matrix, you can replace the --sc_data option with your data path as follows:
python bulkmodel.py --drug [*Your selected drug*] --data [*Your own bulk level expression*] --label [*Your own bulk level drug resistance table*] ...
python scmodel.py --sc_data [*Your own data path*] ...
Formats for your own drug resistance table and your bulk level expression should be identical to files in the demo:
For more detailed parameter settings of the two scripts, please refer to the documentation section.
The folder named "casestudy" contains Jupyter notebook templates of selected case studies in the paper. You can follow the introduction within each notebook to create analysis results for scDEAL. For example, run bulkmode.py with user-defined parameters:
python bulkmodel.py --drug [*Your selected drug*] --data [*Your own bulk level expression*] --label [*Your own bulk level drug resistance table*] ... --printgene 'T'
python scmodel.py --sc_data [*Your own data path*] ... --printgene 'T'
- casestudy/analysis_criticalgenes.ipynb: critical gene identification by integrated gradient matrix;
- casestudy/analysis_tarined_anndata.ipynb: umap, gene score, and regression plot of single-cell level prediction.
Trained results as scanpy h5ad objects for the example datasets are provided as follows:
- Command: python bulkmodel.py
usage: bulkmodel.py [-h] [--data DATA] [--label LABEL] [--result RESULT] [--drug DRUG] [--missing_value MISSING_VALUE] [--test_size TEST_SIZE] [--valid_size VALID_SIZE] [--var_genes_disp VAR_GENES_DISP] [--sampling SAMPLING]
[--PCA_dim PCA_DIM] [--bulk_encoder BULK_ENCODER] [--pretrain PRETRAIN] [--lr LR] [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--bottleneck BOTTLENECK] [--dimreduce DIMREDUCE] [--freeze_pretrain FREEZE_PRETRAIN]
[--encoder_h_dims ENCODER_H_DIMS] [--predictor_h_dims PREDICTOR_H_DIMS] [--VAErepram VAEREPRAM] [--data_name DATA_NAME] [--bulk_model BULK_MODEL] [--log LOG] [--load_source_model LOAD_SOURCE_MODEL] [--mod MOD]
[--printgene PRINTGENE] [--dropout DROPOUT] [--bulk BULK]
optional arguments:
-h, --help show this help message and exit
--data DATA Path of the bulk RNA-Seq expression profile
--label LABEL Path of the processed bulk RNA-Seq drug screening annotation
--result RESULT Path of the training result report files
--drug DRUG Name of the selected drug, should be a column name in the input file of --label
--missing_value MISSING_VALUE
The value filled in the missing entry in the drug screening annotation, default: 1
--test_size TEST_SIZE
Size of the test set for the bulk model traning, default: 0.2
--valid_size VALID_SIZE
Size of the validation set for the bulk model traning, default: 0.2
--var_genes_disp VAR_GENES_DISP
Dispersion of highly variable genes selection when pre-processing the data. If None, all genes will be selected .default: None
--sampling SAMPLING Samping method of training data for the bulk model traning. Can be upsampling, downsampling, or SMOTE. default: None
--PCA_dim PCA_DIM Number of components of PCA reduction before training. If 0, no PCA will be performed. Default: 0
--bulk_encoder BULK_ENCODER, -e BULK_ENCODER
Path of the pre-trained encoder in the bulk level
--pretrain PRETRAIN Whether to perform pre-training of the encoder. 0: do not pretraing, 1: pretrain. Default: 0
--lr LR Learning rate of model training. Default: 1e-2
--epochs EPOCHS Number of epoches training. Default: 500
--batch_size BATCH_SIZE
Number of batch size when training. Default: 200
--bottleneck BOTTLENECK
Size of the bottleneck layer of the model. Default: 32
--dimreduce DIMREDUCE
Encoder model type. Can be AE or VAE. Default: AE
--freeze_pretrain FREEZE_PRETRAIN
Fix the prarmeters in the pretrained model. 0: do not freeze, 1: freeze. Default: 0
--encoder_h_dims ENCODER_H_DIMS
Shape of the encoder. Each number represent the number of neuron in a layer. Layers are seperated by a comma. Default: 512,256
--predictor_h_dims PREDICTOR_H_DIMS
Shape of the predictor. Each number represent the number of neuron in a layer. Layers are seperated by a comma. Default: 16,8
--VAErepram VAEREPRAM
--data_name DATA_NAME
Accession id for testing data, only support pre-built data.
--bulk_model BULK_MODEL, -p BULK_MODEL
Path of the trained prediction model in the bulk level
--log LOG, -l LOG Path of training log
--load_source_model LOAD_SOURCE_MODEL
Load a trained bulk level or not. 0: do not load, 1: load. Default: 0
--mod MOD Embed the cell type label to regularized the training: new: add cell type info, ori: do not add cell type info. Default: new
--printgene PRINTGENE
Print the cirtical gene list: T: print. Default: T
--dropout DROPOUT Dropout of neural network. Default: 0.3
--bulk BULK Selection of the bulk database.integrate:both dataset. old: GDSC. new: CCLE. Default: integrate
- Command: python scmodel.py
usage: scmodel.py [-h] [--bulk_data BULK_DATA] [--label LABEL] [--sc_data SC_DATA] [--drug DRUG] [--missing_value MISSING_VALUE] [--test_size TEST_SIZE] [--valid_size VALID_SIZE] [--var_genes_disp VAR_GENES_DISP]
[--min_n_genes MIN_N_GENES] [--max_n_genes MAX_N_GENES] [--min_g MIN_G] [--min_c MIN_C] [--percent_mito PERCENT_MITO] [--cluster_res CLUSTER_RES] [--mmd_weight MMD_WEIGHT] [--mmd_GAMMA MMD_GAMMA]
[--bulk_model_path BULK_MODEL_PATH] [--sc_model_path SC_MODEL_PATH] [--pretrain PRETRAIN] [--lr LR] [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--bottleneck BOTTLENECK] [--dimreduce DIMREDUCE]
[--freeze_pretrain FREEZE_PRETRAIN] [--bulk_h_dims BULK_H_DIMS] [--sc_h_dims SC_H_DIMS] [--predictor_h_dims PREDICTOR_H_DIMS] [--VAErepram VAEREPRAM] [--batch_id BATCH_ID] [--load_sc_model LOAD_SC_MODEL] [--mod MOD]
[--printgene PRINTGENE] [--dropout DROPOUT] [--logging_file LOGGING_FILE] [--sampling SAMPLING] [--fix_source FIX_SOURCE] [--bulk BULK]
optional arguments:
-h, --help show this help message and exit
--bulk_data BULK_DATA
Path of the bulk RNA-Seq expression profile
--label LABEL Path of the processed bulk RNA-Seq drug screening annotation
--sc_data SC_DATA Accession id for testing data, only support pre-built data.
--drug DRUG Name of the selected drug, should be a column name in the input file of --label
--missing_value MISSING_VALUE
The value filled in the missing entry in the drug screening annotation, default: 1
--test_size TEST_SIZE
Size of the test set for the bulk model traning, default: 0.2
--valid_size VALID_SIZE
Size of the validation set for the bulk model traning, default: 0.2
--var_genes_disp VAR_GENES_DISP
Dispersion of highly variable genes selection when pre-processing the data. If None, all genes will be selected .default: None
--min_n_genes MIN_N_GENES
Minimum number of genes for a cell that have UMI counts >1 for filtering propose, default: 0
--max_n_genes MAX_N_GENES
Maximum number of genes for a cell that have UMI counts >1 for filtering propose, default: 20000
--min_g MIN_G Minimum number of genes for a cell >1 for filtering propose, default: 200
--min_c MIN_C Minimum number of cell that each gene express for filtering propose, default: 3
--percent_mito PERCENT_MITO
Percentage of expreesion level of moticondrial genes of a cell for filtering propose, default: 100
--cluster_res CLUSTER_RES
Resolution of Leiden clustering of scRNA-Seq data, default: 0.3
--mmd_weight MMD_WEIGHT
Weight of the MMD loss of the transfer learning, default: 0.25
--mmd_GAMMA MMD_GAMMA
Gamma parameter in the kernel of the MMD loss of the transfer learning, default: 1000
--bulk_model_path BULK_MODEL_PATH, -s BULK_MODEL_PATH
Path of the trained predictor in the bulk level
--sc_model_path SC_MODEL_PATH, -p SC_MODEL_PATH
Path (prefix) of the trained predictor in the single cell level
--pretrain PRETRAIN Path of the pre-trained encoder in the single-cell level
--lr LR Learning rate of model training. Default: 1e-2
--epochs EPOCHS Number of epoches training. Default: 500
--batch_size BATCH_SIZE
Number of batch size when training. Default: 200
--bottleneck BOTTLENECK
Size of the bottleneck layer of the model. Default: 32
--dimreduce DIMREDUCE
Encoder model type. Can be AE or VAE. Default: AE
--freeze_pretrain FREEZE_PRETRAIN
Fix the prarmeters in the pretrained model. 0: do not freeze, 1: freeze. Default: 0
--bulk_h_dims BULK_H_DIMS
Shape of the source encoder. Each number represent the number of neuron in a layer. Layers are seperated by a comma. Default: 512,256
--sc_h_dims SC_H_DIMS
Shape of the encoder. Each number represent the number of neuron in a layer. Layers are seperated by a comma. Default: 512,256
--predictor_h_dims PREDICTOR_H_DIMS
Shape of the predictor. Each number represent the number of neuron in a layer. Layers are seperated by a comma. Default: 16,8
--VAErepram VAEREPRAM
--batch_id BATCH_ID Batch id only for testing
--load_sc_model LOAD_SC_MODEL
Load a trained model or not. 0: do not load, 1: load. Default: 0
--mod MOD Embed the cell type label to regularized the training: new: add cell type info, ori: do not add cell type info. Default: new
--printgene PRINTGENE
Print the cirtical gene list: T: print. Default: T
--dropout DROPOUT Dropout of neural network. Default: 0.3
--logging_file LOGGING_FILE, -l LOGGING_FILE
Path of training log
--sampling SAMPLING Samping method of training data for the bulk model traning. Can be upsampling, downsampling, or SMOTE. default: None
--fix_source FIX_SOURCE
Fix the bulk level model. Default: 0
--bulk BULK Selection of the bulk database.integrate:both dataset. old: GDSC. new: CCLE. Default: integrate