Perturb OT

Gangguan-PL

Pencocokan lintas modalitas dan prediksi respon gangguan dengan label Gromov-Wasserstein O ptimal T ransport.

Tata letak repositori

 ├── .gitignore
├── README.md
├── perturbot
│   ├── cv      # Cross-validation experiments
│   └── perturbot
│       ├── match
│       │    ├── ot_labels.py   # Label-constrained Entropic Optimal Transport
│       │    ├── ott_egwl.py    # Label-constrained Entropic Gromov-Wasserstein
│       │    ├── cot_labels.py  # Label-constrained Co-OT (Redko et al., 2020)
│       │    └── cot_feature.py # Feature-feature OT based on Co-OT concept
│       ├── predict
│       └── eval
├── scvi-tools              # forked `scvi-tools` with label adaptation
└── ott                     # forked `ott` with label adaptation

Instalasi

perturbot/ menggunakan scvi-tools dan submodul ott yang dimodifikasi yang dapat diinstal dengan pip install .

 cd scvi-tools/
pip install .
cd ../ott/
pip install .
cd ../perturbot
pip install . 

Penggunaan

 import numpy as np
from sklearn . decomposition import PCA
from perturbot . match import (
    get_coupling_cotl , 
    get_coupling_cotl_sinkhorn , 
    get_coupling_egw_labels_ott ,
    get_coupling_egw_all_ott ,
    get_coupling_eot_ott ,
    get_coupling_leot_ott ,
    get_coupling_egw_ott ,
    get_coupling_cot , 
    get_coupling_cot_sinkhorn , 
    get_coupling_gw_labels ,
    get_coupling_fot ,
)
from perturbot . predict import train_mlp

# Generate data
n_samples = 300
labels = [ 0 , 1 , 2 , 3 ]
X_dict = { k : np . random . rand ( n_samples , 1000 ) for k in labels }
Y_dict = { k : np . random . rand ( n_samples , 2000 ) for k in labels }
pca = PCA ( n_components = 50 )
X_reduced = { k : pca . fit_transform ( X_dict [ k ]) for k in labels }
Y_reduced = { k : pca . fit_transform ( Y_dict [ k ]) for k in labels }


# Learn matching in the latent space
T_dict , log = get_coupling_egw_labels_ott (( X_reduced , Y_reduced )) # Other get_coupling_X methods be used

# Train MLP based on matching
model , pred_log = train_mlp (( X_dict , Y_dict ), T_dict )

# Learn feature-feature matching
T_feature , fm_log = get_coupling_fot (( X_dict , Y_dict ), T_dict )

Lihat dokumentasi dan naskah untuk lebih jelasnya.

Mendukung

Silakan kirimkan masalah atau hubungi [email protected].

Penulis dan pengakuan

Jayoung Ryu, Romain Lopez, dan Charlotte Bunne

Kutipan

Jika Anda telah menggunakan Perturb-OT untuk pekerjaan Anda, harap kutip:

 @misc{ryu2024crossmodality,
      title={Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport}, 
      author={Jayoung Ryu and Romain Lopez and Charlotte Bunne and Aviv Regev},
      year={2024},
      eprint={2405.00838},
      archivePrefix={arXiv},
      primaryClass={q-bio.GN}
}