deepmd-cancer / bigmhc

BigMHC predicts MHC-I (neo)epitope presentation and immunogenicity

BigMHC

BigMHC is a deep learning tool for predicting MHC-I (neo)epitope presentation and immunogenicity.

See the article for more information:

• Nature Machine Intelligence article
• free NMI link

All data used in this research can be freely downloaded here.

Major Update (January 19, 2023)

Major updates have been made to the models, code, data, and preprint. Please pull the latest versions for using BigMHC and note the revised training procedure.

Installation

Get the BigMHC Source

git clone https://github.com/karchinlab/bigmhc.git

The repository is about 5GB, so installation generally takes about 3 minutes depending on internet speed.

Environment and Dependencies

Execution is OS agnostic and does not require GPUs.

Training models with large batch sizes (e.g. 32768) requires significant GPU memory (about 94 GB total). Transfer learning requires minimal GPU memory and can be reasonably conducted on a CPU.

All methods were tested on Debian 11 using Linux 5.10.0-19-amd64, AMD EPYC 7443P, and four RTX 3090 GPUs.

Software depenencies are listed below (the versions used in the paper are parenthesized).

Required Dependencies

• python (3.9.13)
• numpy (1.21.5)
• pytorch (1.13.0)
• pandas (1.4.4)
• psutil (5.9.4)

Optional Dependencies

• cuda (11.7)
  • Required for GPU usage
• magma (magma-cuda117 version 2.6.1)
  • Recommended for GPU usage

Jupyter Notebook Dependencies

• scipy (1.7.3)
• scikit-learn (1.0.2)
• matplotlib (3.5.3)
• seaborn (0.12.1)
• py3dmol (2.0.0.post2)
• logomaker (0.8)
• openpyxl (3.1.1)

Usage

There are two executable Python scripts in src: predict.py and train.py.

• predict.py is used for making predictions using BigMHC EL and BigMHC IM
• train.py allows you to train or retrain (transfer learning) BigMHC on new data

Both scripts, which can be run from any directory, offer help text.

• python predict.py --help
• python train.py --help

Examples

From within the src dir, you can execute the below examples:

python predict.py -i=../data/example1.csv -m=el -t=2 -d="cpu"
python predict.py -i=../data/example2.csv -m=el -a=HLA-A*02:02 -p=0 -c=0 -d="cpu"

Predictions will be written to example1.csv.prd and example2.csv.prd in the data folder. Execution takes a few seconds. Compare your output with example1.csv.cmp and example2.csv.cmp respectively.

Required Arguments

• -i or --input input CSV file
  • Columns are zero-indexed
  • Must have a column of peptides
  • Can also have a column of of MHC-I allele names
• -m or --model BigMHC model to load
  • el or bigmhc_el to load BigMHC EL
  • im or bigmhc_im to load BigMHC IM
  • Can be a path to a BigMHC model directory
  • Optional for train.py (if a model dir is specified, then transfer learn)

Required Arguments for Training

• -t or --tgtcol column index of target values
  • Elements in this column are considered ground truth values.
• -o or --out output directory
  • Directory to save model parameters for each epoch
  • Optional for transfer learning (defaults to model arg)

Input Formatting Arguments

• -a or --allele allele name or allele column
  • If allele is a column index, then a single MHC-I allele name must be present in each row
• -p or --pepcol peptide column
  • Is the column index of a CSV file containing one peptide sequence per row.
• -c or --hdrcnt header count
  • Skip the first hdrcnt rows before consuming input

Output Arguments

• -o or --out output file or directory
  • If using predict.py, save CSV data to this file
    • Defaults to input.prd
  • If using train.py, save the retrained BigMHC model to this directory
    • If transfer learning, defaults to the base model dir
• -z or --saveatt boolean indicating whether to save attention values
  • Only available for predict.py
  • Use 1 for true and 0 for false

Other Optional Arguments

• -d or --devices devices on which to run BigMHC
  • Set to all to utilize all GPUs
  • To use a subset of available GPUs, provide a comma-separated list of GPU device indices
  • Set to cpu to run on CPU (not recommended for large datasets)
• -v or --verbose toggle verbose printing
  • Use 1 for true and 0 for false
• -j or --jobs Number of workers for parallel data loading
  • These workers are persistent throughout the script execution
• -f or --prefetch Number of batches to prefetch per data loader worker
  • Increasing this number can help prevent GPUs waiting on the CPU, but increases memory usage
• -b or --maxbat Maximum batch size
  • Turn this down if running out of memory
  • If using predict.py, defaults to a value that is estimated to fully occupy the device with the least memory
  • If using train.py, defaults to 32
• -s or --pseudoseqs CSV file mapping MHC to one-hot encoding
• -l or --lr AdamW optimizer learning rate
  • Only available for train.py
• -e or --epochs number of epochs for transfer learning
  • Only available for train.py

Contact (Gmail)

benjialbert2

Citation

@Article{Albert2023,
	author={Albert, Benjamin Alexander and Yang, Yunxiao and Shao, Xiaoshan M. and Singh, Dipika and Smith, Kellie N. and Anagnostou, Valsamo and Karchin, Rachel},
	title={Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity},
	journal={Nature Machine Intelligence},
	year={2023},
	month={Jul},
	day={20},
	issn={2522-5839},
	doi={10.1038/s42256-023-00694-6},
	url={https://doi.org/10.1038/s42256-023-00694-6}
}

License

See the LICENSE file