A deep learning framework for early cancer detection using T cell receptor repertoire in peripheral blood. In brief, the framework contains two deep learning classifiers, including a binary classification module and a multi-category classification module, and their corresponding cancer scoring strategies.
From Source:
git clone https://github.com/JiangBioLab/iCanTCR.git
cd iCanTCR
Running the iCanTCR requires python3.6, numpy version 1.19.2, torch version 1.6.0, torchvision version 0.7.0, pandas version 1.1.2 and scikit-learn version 0.24.2 to be installed.
If they are not installed on your environment, please first install numpy, pandas, and scikit-learn by running the following command:
pip install -r requirements.txt
Next, Please select the most suitable command from the following options to install torch and torchvision based on your terminal’s specific situation:
For OSX:
pip install torch==1.6.0 torchvision==0.7.0
For Linux and Windows:
# CUDA 10.2
pip install torch==1.6.0 torchvision==0.7.0
# CUDA 10.1
pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
# CUDA 9.2
pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
# CPU only
pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
Using the examples to perform iCanTCR. The examples folder contains 3 files. In each file, the first column is the amino acid sequence of CDR3, the second column is the cloning fraction,Each sample contains the sequences with the highest cloning abundance. Now, you can choose one of the following commands based on whether you want to use GPU or not, and input it in the terminal.
python -u iCanTCR_run.py --I examples --O output --D cpu # cpu only
python -u iCanTCR_run.py --I examples --O output --D gpu # if gpu is available
If you want to perform binary classification tasks only, please run the following command.
python -u iCanTCR_run.py --I examples --O output --D cpu --T binary # cpu only
python -u iCanTCR_run.py --I examples --O output --D gpu --T binary # if gpu is available
Similarly, for multi-classification tasks only.
python -u iCanTCR_run.py --I examples --O output --D cpu --T multi # cpu only
python -u iCanTCR_run.py --I examples --O output --D gpu --T multi # if gpu is available
After running the command, an output file under the output folder will be created, which contains name of the input folder and the corresponding prediction result.
The iCanTCR program is also provided at the online webserver(http://jianglab.org.cn/iCanTCR).
You can train the model using the provided training data with the following command.
python bina_training.py # if gpu is available you can add "--D gpu" in the end of this command
python multi_training.py # if gpu is available you can add "--D gpu" in the end of this command
The dataset is first randomly divided into training data and testing data. Then, the training data is further divided into a training set and a validation set for model optimization.
Run iCanTCR on benchmark datasets as an example.
cd data
unzip sample_data.rar
cd ..
python -u iCanTCR_run.py --I sample_data/bench_bina --O output --D cpu --T binary # if gpu is available, set the --D parameter to “gpu”.
python -u iCanTCR_run.py --I sample_data/bench_multi --O output --D cpu --T multi # if gpu is available, set the --D parameter to “gpu”. Feel free to submit an issue or contact us at cyd_charrick@163.com for problems about the tool.