Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.

Abstract

Advances in sequencing technologies and the growing volume of single-cell data have created unprecedented opportunities for uncovering gene expression patterns causally induced by experimental perturbations or statistically associated, but not necessarily causal, with disease conditions. However, current analytical methods inadequately account for batch effects and data sparsity or fail to capture the inherent non-linearity in single-cell data, leading to biased estimation. To address these limitations, we developed NDreamer that combines neural discrete representation learning and matching to remove batch effects and estimate perturbation-induced or condition-associated signals at single-cell resolution. NDreamer outperformed existing methods by using mutual information loss on discrete latent variables to disentangle cells' intrinsic features from conditions or batch effects, while preserving both global and local variance within batches and conditions via triplet and local neighborhood loss. We applied NDreamer to multiple datasets across platforms, organs, and species and validated and benchmarked its performance in removing batch effects and estimating perturbation-induced or condition-associated signals. In particular, we applied NDreamer to an Alzheimer's disease cohort, revealing biologically relevant gene expression patterns that distinguish dementia patients from controls.