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scLAMBDA

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

We present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations.

By leveraging the embedding capabilities of large language models, scLAMBDA effectively predicts genetic perturbation outcomes for unobserved target genes or gene combinations. Its disentangled representation learning framework enables the modeling of single-cell level perturbations by separating basal cell representations from salient representations associated with perturbation states, allowing for single-cell level generation and in silico prediction of perturbation responses.

Installation

scLAMBDA can be installed from from GitHub:

git clone https://github.com/gefeiwang/scLAMBDA.git
cd scLAMBDA
conda env update --f environment.yml
conda activate sclambda

Normally the installation time is less than 5 minutes.

Quick Start

Preprocessing

scLAMBDA accepts logarithmized data stored in an AnnData object as input. The target genes should be specified in adata.obs['condition']. For single-gene perturbation datasets, this field should contain entries such as "gene_a" for perturbed cells and "ctrl" for control cells. For two-gene perturbation datasets, entries should follow the format "gene_a+gene_b" for cells with two target genes, "gene_a+ctrl" for cells with one target gene, or "ctrl" for control cells.

To run scLAMBDA, a dictionary of gene embeddings in the form gene_embeddings = {"gene_name": np.array} must also be provided as input.

The function can be used for simulating a dataset splitting:

n_split = 0
# for single-gene perturbation
adata, split = sclambda.utils.data_split(adata, split_type='single', seed=n_split)
# for two-gene perturbation
adata, split = sclambda.utils.data_split(adata, seed=n_split)
# use all available perturbations in training:
adata, split = sclambda.utils.data_split(adata, split_type='all_train', seed=n_split)

Training

To train the scLAMBDA model, use:

model = sclambda.model.Model(adata, 
                             gene_embeddings,
                             model_path = 'path_to_save_dir/models')

model.train()

Note that for single-gene perturbations, multi_gene = False should be included in the sclambda.model.Model() parameters.

Prediction

Once trained, scLAMBDA can be used for in silico predictions. With a list of test perturbations pert_test, you can predict control cells using:

res = model.predict(pert_test, return_type = 'cells')

Alternatively, you can generate new perturbed cells using:

res = model.generate(pert_test, return_type = 'cells')

Setting return_type='mean' returns the prediction results as the mean gene expression.

Vignettes

User can follow the example for training and evaluating scLAMBDA on the Perturb-seq dataset from Norman et al. (https://www.science.org/doi/10.1126/science.aax4438).

Citation

Gefei Wang, Tianyu Liu, Jia Zhao, Youshu Cheng, Hongyu Zhao. Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA. bioRxiv 2024; doi: https://doi.org/10.1101/2024.12.04.626878.