Skip to content

Latest commit

 

History

History
106 lines (88 loc) · 3.86 KB

README.md

File metadata and controls

106 lines (88 loc) · 3.86 KB
Raw

AIRRTM

This repository contains the code of the AIRRTM model from the article Weakly supervised identification and generation of adaptive immune receptor sequences associated with immune disease status.

Installation

Pipenv

A locked Pipenv environment is provided with the repository. To install the package, you need to run

pipenv shell
pipenv install

The only prerequisites are havinng Pipenv and python3.9 installed on your machine.

requirements.txt

Alternatively, you can use the requirements.txt file to install the dependencies in a clean python3.9 environemnt:

python -m pip install -r requirements.txt

Data

Two synthetic datasets used in the article can be found in the corresponding repository. Note that you would need to unzip the data file for given dataset and witness rate (for example, dataset S1, wr=0.0001) before running training/prediction on them.

The model can be trained on a dataset with the following structure:

INPUT_DATA_DIR
│
└───samples
    │
    └───WITNESS_RATE
    │   │   1.csv
    │   │   2.csv
    │   │   ...
    │   │   99.csv
    │   │   99.csv
    │   │   metadata.csv

Each of the data csv files must contain a column cdr3_aa with amino acid sequences.

$ head S1/samples/0.005/1.csv
cdr3_aa
CARDGRNTGIVGALTDPGMLLIS
CARGFGQPSSSW*SGWFDPW
CARDSSSWTT
CARDLRKGDYYDSSGYYYAFMMLLIS
CARERGRTVTVDYW
CARGCFFSMVRGVIITFRMLLIS
CARKFRWGRTGSAT
CARVVLLWFGELFDYGMDVW
CARDLIRGTLL*LL

Additionally, optional columns v_gene, j_gene (to use with the --use_vj option), and weight (is used automatically when present) may be provided.

cdr3_aa,v_gene,j_gene,weight
CATSRDVNTGELFF,TCRBV15-01,TCRBJ02-02,1
CASSPPGANVLTF,TCRBV11-02,TCRBJ02-06,1
CASSEYEQYF,TCRBV06-01,TCRBJ02-07,1
CASSLHEQYF,TCRBV11-02,TCRBJ02-07,9
CASSAATGATEAFF,TCRBV05-04,TCRBJ01-01,2
CASSPTGGHTEAFF,TCRBV05-04,TCRBJ01-01,2
CASSPQGAYNEQFF,TCRBV05-04,TCRBJ02-01,2
CASWGVNRGDAGYTF,TCRBV25-01,TCRBJ01-02,5042
CASSAQQGYSGNTIYF,TCRBV28-01,TCRBJ01-03,2247

The metadata file must contain columns label (i.e., repertoire label), filename and split (train/test).

$ head S1/samples/0.005/metadata.csv
label,filename,split
1,0.csv,train
0,1.csv,train
1,2.csv,train
1,3.csv,train

Usage

To train the model, one must first run preprocess_data.py on your dataset folder (structured as described above)

python preprocess_data.py --input_data_dir INPUT_DATA_DIR -w WITNESS_RATE [--use_vj]

Then you can train the model by running

python train_model.py --input_data_dir INPUT_DATA_DIR -w WITNESS_RATE [-t THREADS] [--use_vj] --checkpoint_dir CHECKPOINT_DIR

And, with a trained model, make signal intensity predictions on sequences from an unseen repertoire, for example:

python predict.py -l <MAX_LEN_USED_FOR_TRAINING> -i INPUT_DATA_FILE -o OUTPUT_DIR -m CHECKPOINT_DIR/model_0.005_epoch_9.keras [--use_vj]

The input csv file must be in the same format as the training files (i.e., it must have a column cdr3_aa with amino acid sequences). Note that the --use_vj option must be used consistently, i.e., either by all three commands (preprocess_data, train_model, predict), or by none of them.

Alternatively, one can make signal intensity predictions for all files listed in the metadata file (in the same format and as described above, the paths in the metadatafile are assumed to be relative of the metadata file itself):

python predict.py -l <MAX_LEN_USED_FOR_TRAINING> --from_metadata -i INPUT_METADATA_FILE -o OUTPUT_DIR -m CHECKPOINT_DIR/model_0.005_epoch_9.keras [--use_vj]

Note that AIRRTM is quite CPU-heavy, so it may not be optimized for running on a 'normal' consumer computers.