Package: methylbert
MethylBERT: A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolutionThe figure was generated usingbiorenderBERT model to classify read-level DNA methylation data into tumour/normal and perform tumour deconvolution.MethylBERTis implemented usingpytorchandtransformers🤗.PaperMethylBERTpaper is now online onbioRxiv!!MethylBERT: A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution,
Yunhee Jeong, Karl Rohr, Pavlo Lutsik,
bioRxiv 2023.10.29.564590; doi:https://doi.org/10.1101/2023.10.29.564590InstallationMethylBERTruns most stably withPython=3.7Pip InstallationMethylBERTis available as apython package.conda create -n methylbert -c conda-forge python=3.7 cudatoolkit==11.1.1 pip
pip install methylbertManual InstallationYou can set up your conda environment with thesetup.pyfile.conda create -n methylbert -c conda-forge python=3.7 cudatoolkit==11.1.1 pip
git clone https://github.com/hanyangii/methylbert.git
cd methylbert
pip3 install .Quick startPython libraryIf you want to useMethylBERTas a python library, please follow thetutorials.Command lineMethylBERT supports a command line tool. Before using the command line tool, please checkthe input file requirements> methylbert 
MethylBERT v0.0.1
One option must be given from ['preprocess_finetune', 'finetune', 'deconvolute']1. Data Preprocessing to fine-tune MethylBERT> methylbert preprocess_finetune --help
MethylBERT v0.0.1
usage: methylbert preprocess_finetune [-h] [-s SC_DATASET] [-f INPUT_FILE] -d
                                      F_DMR -o OUTPUT_DIR -r F_REF
                                      [-nm N_MERS] [-p SPLIT_RATIO]
                                      [-nd N_DMRS] [-c N_CORES] [--seed SEED]
                                      [--ignore_sex_chromo IGNORE_SEX_CHROMO]

optional arguments:
  -h, --help            show this help message and exit
  -s SC_DATASET, --sc_dataset SC_DATASET
                        a file all single-cell bam files are listed up. The
                        first and second columns must indicate file names and
                        cell types if cell types are given. Otherwise, each
                        line must have one file path.
  -f INPUT_FILE, --input_file INPUT_FILE
                        .bam file to be processed
  -d F_DMR, --f_dmr F_DMR
                        .bed or .csv file DMRs information is contained
  -o OUTPUT_DIR, --output_dir OUTPUT_DIR
                        a directory where all generated results will be saved
  -r F_REF, --f_ref F_REF
                        .fasta file containing reference genome
  -nm N_MERS, --n_mers N_MERS
                        K for K-mer sequences (default: 3)
  -p SPLIT_RATIO, --split_ratio SPLIT_RATIO
                        the ratio between train and test dataset (default:
                        0.8)
  -nd N_DMRS, --n_dmrs N_DMRS
                        Number of DMRs to take from the dmr file. If the value
                        is not given, all DMRs will be used
  -c N_CORES, --n_cores N_CORES
                        number of cores for the multiprocessing (default: 1)
  --seed SEED           random seed number (default: 950410)
  --ignore_sex_chromo IGNORE_SEX_CHROMO
                        Whether DMRs at sex chromosomes (chrX and chrY) will
                        be ignored (default: True)2. MethylBERT fine-tuning> methylbert finetune --help
MethylBERT v0.0.1
usage: methylbert finetune [-h] -c TRAIN_DATASET [-t TEST_DATASET] -o
                           OUTPUT_PATH [-p PRETRAIN] [-nm N_MERS] [-s SEQ_LEN]
                           [--max_grad_norm MAX_GRAD_NORM]
                           [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]
                           [-b BATCH_SIZE] [--valid_batch VALID_BATCH]
                           [-e STEPS] [--save_freq SAVE_FREQ] [-w NUM_WORKERS]
                           [--with_cuda WITH_CUDA] [--log_freq LOG_FREQ]
                           [--eval_freq EVAL_FREQ]
                           [--corpus_lines CORPUS_LINES] [--lr LR]
                           [--adam_weight_decay ADAM_WEIGHT_DECAY]
                           [--adam_beta1 ADAM_BETA1] [--adam_beta2 ADAM_BETA2]
                           [--warm_up WARM_UP] [--seed SEED]
                           [--decrease_steps DECREASE_STEPS]

optional arguments:
  -h, --help            show this help message and exit
  -c TRAIN_DATASET, --train_dataset TRAIN_DATASET
                        train dataset for train bert
  -t TEST_DATASET, --test_dataset TEST_DATASET
                        test set for evaluate train set
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        ex)output/bert.model
  -p PRETRAIN, --pretrain PRETRAIN
                        a saved pretrained model to restore
  -nm N_MERS, --n_mers N_MERS
                        n-mers (default: 3)
  -s SEQ_LEN, --seq_len SEQ_LEN
                        maximum sequence len (default: 150)
  --max_grad_norm MAX_GRAD_NORM
                        Max gradient norm (default: 1.0)
  --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS
                        Number of updates steps to accumulate before
                        performing a backward/update pass. (default: 1)
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        number of batch_size (default: 50)
  --valid_batch VALID_BATCH
                        number of batch_size in valid set. If it's not given,
                        valid_set batch size is set same as the train_set
                        batch size
  -e STEPS, --steps STEPS
                        number of steps (default: 10)
  --save_freq SAVE_FREQ
                        Steps to save the interim model
  -w NUM_WORKERS, --num_workers NUM_WORKERS
                        dataloader worker size (default: 20)
  --with_cuda WITH_CUDA
                        training with CUDA: true, or false (default: True)
  --log_freq LOG_FREQ   Frequency (steps) to print the loss values (default:
                        1000)
  --eval_freq EVAL_FREQ
                        Evaluate the model every n iter (default: 100)
  --corpus_lines CORPUS_LINES
                        total number of lines in corpus
  --lr LR               learning rate of adamW (default: 4e-4)
  --adam_weight_decay ADAM_WEIGHT_DECAY
                        weight_decay of adamW (default: 0.01)
  --adam_beta1 ADAM_BETA1
                        adamW first beta value (default: 0.9)
  --adam_beta2 ADAM_BETA2
                        adamW second beta value (default: 0.98)
  --warm_up WARM_UP     steps for warm-up (default: 10000)
  --seed SEED           seed number (default: 950410)
  --decrease_steps DECREASE_STEPS
                        step to decrease the learning rate (default: 1500)3. MethylBERT tumour deconvolution> methylbert deconvolute --help
MethylBERT v0.0.1
usage: methylbert deconvolute [-h] -i INPUT_DATA -m MODEL_DIR [-o OUTPUT_PATH]
                              [-b BATCH_SIZE] [--save_logit] [--adjustment]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT_DATA, --input_data INPUT_DATA
                        Bulk data to deconvolve
  -m MODEL_DIR, --model_dir MODEL_DIR
                        Trained methylbert model
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        Directory to save deconvolution results. (default: ./)
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        Batch size. Please decrease the number if you do not
                        have enough memory to run the software (default: 64)
  --save_logit          Save logits from the model (default: False)
  --adjustment          Adjust the estimated tumour purity (default: False)Citation@article{jeong2023methylbert,
  title={MethylBERT: A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution},
  author={Jeong, Yunhee and Rohr, Karl and Lutsik, Pavlo},
  journal={bioRxiv},
  pages={2023--10},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}