scRNA-seq

scGPT is a generative pretrained transformer foundation model for single-cell biology, pretrained on over 33 million human cells from 51 organs across 441 studies. Uses a GPT architecture adapted for gene expression data with a specialized attention mask. Outperforms traditional methods on cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction, and gene network inference. Represents a foundational AI model for cellular biology analogous to GPT for natural language.

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications including cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.

Statistical framework for cell-type-resolved transcriptome-wide association using single-cell RNA-seq: models sparsity and technical noise via latent variables and moment-based estimation to improve genetically regulated expression prediction and gene–trait discovery.

TWAS, single-cell, cell-type-specific, latent variable, GReX

Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most have been performed using bulk gene expression data, which aggregate signals across heterogeneous cell types. Population-scale single-cell RNA sequencing data now make it possible to perform TWAS at the cell-type resolution, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identifies substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer's disease also reveals cell-subtype-specific associations, including MS4A6A in the disease-associated microglial subtype and PPP1R37 in the inflammatory microglial subtype.

R framework that links GWAS signals to single-cell-defined cell types via a cell-type gene specificity score (expression magnitude and consistency) and regression on gene-level association statistics, with influential-gene follow-up for interpretability.

GWAS, scRNA-seq, cell type, MAGMA, post-GWAS interpretation

Integrating single-cell RNA sequencing with Genome-Wide Association Studies (GWAS) can uncover cell types involved in complex traits and disease. However, current methods often lack scalability, interpretability, and robustness. We present seismic, a framework that computes a novel specificity score capturing both expression magnitude and consistency across cell types and introduces influential gene analysis, an approach to identify genes driving each cell type-trait association. Across over 1000 cell-type characterizations at different granularities and 28 polygenic traits, seismic corroborates known associations and uncovers trait-relevant cell groups not apparent through other methodologies. In Parkinson's and Alzheimer's, seismic unveils both cell- and brain-region-specific differences in pathology. Analyzing a pathology-based Alzheimer's GWAS with seismic enables the identification of vulnerable neuron populations and molecular pathways implicated in their neurodegeneration. In general, seismic is a computationally efficient, powerful, and interpretable approach for mapping the relationships between polygenic traits and cell-type-specific expression, offering new insights into disease mechanisms.