scouter-learn 0.1.10

A transcriptional response predictor for unseen genetic perturbtions with LLM embeddings

Scouter: a transcriptional response predictor for unseen genetic perturbtions with LLM embeddings

Scouter is a deep neural network with simple architecture, for the task of predicting transcriptional response to unseen genetic perturbtions.

Scouter employs the LLM embeddings generated from text description of genes, enabling the perdiction on unseen genes.

For more details read our manuscript

Installation

pip install scouter-learn

Main API

Below is an example that includes main APIs to train Scouter on a perturbation dataset.

from scouter import Scouter, ScouterData, adamson_small, embedding_small

adata = adamson_small()
embd = embedding_small()
scouterdata = ScouterData(adata=adata, embd=embd, key_label='condition', key_var_genename='gene_name')
scouterdata.setup_ad('embd_index')
scouterdata.gene_ranks()
scouterdata.get_dropout_non_zero_genes()
scouterdata.split_Train_Val_Test(seed=1)

# Model Training
scouter_model = Scouter(scouterdata)
scouter_model.model_init()
scouter_model.train()

# Prediction
scouter_model.pred(['ATP5B+ctrl', 'MANF+ctrl'])

# Evaluation
scouter_model.barplot('MANF+ctrl')

Demos

Name	Description
Demo.ipynb	A detailed tutorial on how to apply Scouter to a smaller version of Adamson dataset, including preprocessing, paramter setting, model training, and evaluation
OwnDataTutorial.ipynb	A tutorial to guide users to load their own dataset and embedding matrix.
UnmatchRemedy.ipynb	A tutorial that illustrates the problem of unmatched genes between perturbation dataset and embedding matrix, and provides a remedy.