Antony Rose

Abstract Single-cell gene expression is influenced by diverse covariates such as genomics protocol, tissue origin, donor attributes, and microenvironment, which are challenging to disentangle. We present CellDISECT, a novel method combining disentangled representations and causal inference for multi-batch, multi-covariate single-cell data analysis.

Abstract Addressing challenges in domain invariance within single-cell genomics necessitates innovative strategies to manage the heterogeneity of multi-source datasets while maintaining the integrity of biological signals. We introduce TarDis, a novel deep generative model designed to disentangle intricate covariate structures across diverse biological datasets, distinguishing technical artifacts from true biological variations.