BioTranslator API¶
News¶
BioTranslator API has been published on PyPI 2022-08-26¶
Latest additions¶
Release Notes¶
0.1.0 2022-08-26¶
Released basic functions of BioTranslator.
0.1.1 2022-09-04¶
BioTranslator allows Protein Sequence Prediction, Cell Type Classification, and Pathway Analysis.
API¶
Import methods from BioTranslator¶
from biotranslator import .
Setup a config:¶
setup_config(config, data_type=’seq’)
Train text encoder:¶
train_text_encoder(data_dir: str, save_path: str)
Train a BioTranslator Model:¶
train_biotranslator(cfgs)
Test a BioTranslator Model:¶
test_biotranslator(data_dir, anno_data, cfg, translator, task)
News¶
BioTranslator documentation has been released 2022-09-04¶
BioTranslator API has been published on PyPI 2022-08-26¶
Tutorial¶
Import BioTranslator as¶
import biotranslator as bt
Import methods from API¶
from biotranslator.biotranslator_function import setup_config, train_text_encoder, train_biotranslator, \
test_biotranslator
Train Text Encoder¶
model_path = './TextEncoder/model/encoder.pth'
graphine_repo = './TextEncoder/data/Graphine/dataset/'
train_text_encoder(graphine_repo, model_path)
Build Configs¶
Build config for protein sequence dataset:
seq_repo = f'./Protein'
seq_config = {
'task': 'few_shot',
'max_length': 2000,
'data_repo': f'{seq_repo}/data/',
'dataset': 'GOA_Human',
'encoder_path': './Codebase/TextEncoder/Encoder/text_encoder.pth',
'rst_dir': f'{seq_repo}/results/',
'emb_dir': f'{seq_repo}/embeddings/',
'ws_dir': f'{seq_repo}/working_space/',
'hidden_dim': 1500,
'features': 'seqs, description, network',
'lr': 0.0003,
'epoch': 30,
'batch_size': 32,
'gpu_ids': '0',
}
seq_config = setup_config(seq_config, 'seq')
Build config for cell description vector dataset:
vec_repo = f'./SingleCell'
vec_config = {
'task': 'cross_dataset',
'eval_dataset': 'muris_facs',
'vec_ontology_repo': f'{vec_repo}/data/Ontology_data/',
'data_repo': f'{vec_repo}/data/sc_data/',
'dataset': 'muris_droplet',
'encoder_path': './Codebase/TextEncoder/Encoder/text_encoder.pth',
'rst_dir': f'{vec_repo}/results/',
'emb_dir': f'{vec_repo}/embeddings/',
'ws_dir': f'{vec_repo}/working_space/',
'hidden_dim': 30,
'lr': 0.0001,
'epoch': 15,
'batch_size': 128,
'gpu_ids': '0',
}
vec_config = setup_config(vec_config, 'vec')
Build config for pathway graph dataset:
graph_repo = f'./Pathway'
graph_config = {
'max_length': 2000,
'eval_dataset': 'KEGG',
'graph_excludes': ['Reactome', 'KEGG', 'PharmGKB'],
'data_repo': f'{graph_repo}/data/',
'dataset': 'GOA_Human',
'encoder_path': './Codebase/TextEncoder/Encoder/text_encoder.pth',
'rst_dir': f'{graph_repo}/results/',
'emb_dir': f'{graph_repo}/embeddings/',
'ws_dir': f'{graph_repo}/working_space/',
'hidden_dim': 1500,
'features': 'seqs, description, network',
'lr': 0.0003,
'epoch': 30,
'batch_size': 32,
'gpu_ids': '0',
}
graph_config = setup_config(graph_config, 'graph')
Train BioTranlators¶
cfgs = [seq_config, vec_config, graph_config]
translators = train_biotranslator(cfgs)
Test BioTranslators¶
tasks = dict(
seq=['prot_func_pred'],
vec=['cell_type_cls'],
graph=['node_cls', 'edge_pred'])
vec_files = BioLoader(vec_config)
anno_data = dict(
seq=[pd.read_pickle(f'{seq_config.data_repo}{seq_config.dataset}/validation_data_fold_0.pkl')],
vec=[vec_files.test_data],
graph=[pd.read_pickle(f'{graph_config.data_repo}{graph_config.eval_dataset}/pathway_dataset.pkl'),
pd.read_pickle(f'{graph_config.data_repo}{graph_config.eval_dataset}/pathway_dataset.pkl')],
)
for tp_idx, tp in enumerate(list(tasks.keys())):
for task_idx in range(len(tasks[tp])):
cfg = cfgs[tp_idx]
encoder = translators[tp_idx]
annos = test_biotranslator(cfg.data_repo, anno_data[tp][task_idx], cfg, encoder, tasks[tp][task_idx])
print(annos)
Installation¶
PyPI Version¶
Install latest BioTranslator from PyPI (consider using pip3 to access Python 3):
pip install biotranslator
Development Version¶
To work with the latest version on GitHub: clone the repository and cd into its root directory.
Install with HTTPS:¶
https://github.com/ywzhao2002/biotranslator.git
cd biotranslator
Install with Github CLI:¶
gh repo clone ywzhao2002/biotranslator
cd biotranslator
Setup Dataset¶
Processed datasets including CAFA3, GOA_Human, GOA_Mouse, GOA_Yeast, KEGG, PharmGKB, Reactome, and Swissprot are available at https://figshare.com/articles/dataset/Protein_Pathway_data_tar/20120447
Processed datasets including Tabula_Microcebus and Tabula_Sapiens can be found at: https://figshare.com/ndownloader/files/31777475 and https://figshare.com/ndownloader/files/28846647 . Remaining datasets can be found from OnClass package.
Graphine dataset used for training text encoder can be downloaded from https://zenodo.org/record/5320310/files/Graphine.zip?download=1.
Example dataset structure for protein sequence prediction and pathway analysis tasks¶
├── data
│ ├── CAFA3
│ ├── GOA_Human
│ ├── GOA_Mouse
│ ├── GOA_Yeast
│ ├── KEGG
│ ├── PharmGKB
│ ├── Reactome
│ └── SwissProt
Example dataset structure for single cell classification task¶
├── data
│ ├── ont_data
│ │ ├── allen.ontology
│ │ ├── cl.obo
│ │ ├── cl.ontology
│ │ └── cl.ontology.nlp.emb
│ ├── sc_data
│ │ ├── 26-datasets
│ │ │ ├── 293t_jurkat
│ │ │ ├── brain
│ │ │ ├── hsc
│ │ │ ├── macrophage
│ │ │ ├── pancreas
│ │ │ └── pbmc
│ │ ├── Allen_Brain
│ │ │ ├── features.pkl
│ │ │ ├── genes.pkl
│ │ │ └── labels.pkl
│ │ ├── gene_marker_expert_curated.txt
│ │ ├── HLCA
│ │ │ ├── 10x_features.pkl
│ │ │ ├── 10x_genes.pkl
│ │ │ └── 10x_labels.pkl
│ │ ├── Lemur
│ │ │ ├── microcebusAntoine.h5ad
│ │ │ ├── microcebusBernard.h5ad
│ │ │ ├── microcebusMartine.h5ad
│ │ │ └── microcebusStumpy.h5ad
│ │ ├── Tabula_Microcebus
│ │ │ └── LCA_complete_wRaw_toPublish.h5ad
│ │ ├── Tabula_Muris_Senis
│ │ │ ├── tabula-muris-senis-droplet-official-raw-obj.h5ad
│ │ │ └── tabula-muris-senis-facs-official-raw-obj.h5ad
│ │ └── Tabula_Sapiens
│ │ └── TabulaSapiens.h5ad
Example dataset structure for text encoder¶
├── data
│ ├── Graphine
│ │ └── dataset
Release Notes¶
0.1.0 2022-08-26¶
Released basic functions of BioTranslator.
0.1.1 2022-09-04¶
BioTranslator allows Protein Sequence Prediction, Cell Type Classification, and Pathway Analysis.