PertSpectra: Guided triplet factor analysis of perturb-seq data with a prior

Factor analysis model for perturb-seq data, guided by perturbation labels and prior graph regularization

Code accompanying TODO: paper link here

Abstract

Computational modeling of interventional data is a rapidly expanding area of machine learning. In drug discovery, measuring the effects of genetic interventions on cells is important for characterizing unknown disease mechanisms, but interpreting the resulting measurements remains a challenging task. Reliable latent space interpretability and modeling interactions between interventions are key areas of improvement for current models in literature. Therefore, we propose PertSpectra, an extension of previously described factor analysis method Spectra (Kunes et al., 2024) to explicitly support intervensional data. PertSpectra leverages Spectra’s differentiable graph regularization to incorporate prior biological knowledge to generate sparse, biologically relevant latent factors that capture perturbational effects. We assess PertSpectra on three single cell sequencing datasets with genetic perturbations, measuring latent space interpretability, predictive ability on unseen combinations of perturbations, and identification of perturbations of similar biological function. We show that PertSpectra provides an integrated modeling approach to understanding combinatorial interventional data in the context of drug discovery.

Directory Overview

Outline of the organization of the codebase.

• .: Contains notebooks and helper functions for downstream analyses
• ./src: Contains the PertSpectra code, edited from the Spectra codebase
• ./data: Contains instructions for downloading datasets used in analysis
• ./data_preprocessing: Contains notebooks for preprocessing the input data
• ./PertSpectra_training_scripts: Contains training scripts for PertSpectra
• ./PertSpectra_load_checkpoints: Contains notebooks for loading trained PertSpectra models from checkpoints
• ./scETM: Contains notebooks for training scETM
• ./GSFA: Contains notebooks for preprocessing and training GSFA
• ./figures: Contains notebooks for figures

Environment Setup

PertSpectra has been tested on Linux systems.

Please execute:

make install

This will generate a conda environment called pertspectra and an associated jupyter kernel that can be used to execute the notebooks in this code repository.

Data / Setup

Data preprocessing and setup.

Gene expression normalization

The model expects log-normalized count data. Ensure that the log-normalized expression is either in the .X field or .layers field of the anndata.

Stringdb graph pruning

The model accepts an adjacency matrix as a prior to regularize against during training. Currently, the model regularizes against a stringdb prior graph - the notebooks to subset the stringdb graph to the input genes measured in the perturb-seq experiment are located in the prior_graph_preprocessing/ subdirectory. Otherwise, create any prior graph as desired as a sparse adjacency matrix and store under .uns["sparse_gene_network"] in the anndata.

Training

Overview of training and saving the model.

Launching training runs

Training scripts for PertSpectra are located in ./PertSpectra_training_scripts. Run python3 [training script] [args] to launch a training run.

Model loading

The model can be loaded with a helper function in utils.py (refer to utils.py for details):

wrapper, adata = load_model(
                   adata=adata,
                   s3_dir='s3_directory_where_training_runs_are_stored',
                   experiment_name='folder_in_s3_directory_where_training_run_is_located',
                   model_name='name_of_saved_model_pickle',
                   markers_top_n=50,
                   use_cell_types=False,
                )

From the loaded model, we also need to reconstruct the binarized perturbation matrix (cell x perturbation), as the binarization may differ across different models. For details on reconstructing the binarized perturbation matrix, refer to the analysis notebooks.

The returned anndata from load_model also saves the following outputs from the model:

• adata.uns['SPECTRA_pert_scores'] stores the learned perturbation-level factors
• adata.uns['SPECTRA_factors'] stores the learned gene-level factors

For full details on loading saved PertSpectra models, see ./PertSpectra_load_checkpoints.

Downstream analysis

Please reference the Jupyter notebooks in the main directory for all code relating to downstream analysis.

Figure Generation

Code to generate figures based on the downstream analyses located in ./figures.