Aleksandrina Goeva

The paper introduces found, a tool implementing HiDDEN for cell-level perturbation analysis, showing performance depends on modeling choices.

This paper develops a platform to model and manipulate human microglial states in vitro using single-cell RNA sequencing and genetic tools, identifying disease-associated transcriptional signatures.

The paper develops a computational framework to study division of labor in tissues by analyzing cell interactions and spatial cues using single-cell RNA-seq and spatial transcriptomics data.

HiDDEN refines case-control labels in single-cell transcriptomics to detect disease-relevant cell populations, improving detection of subtle perturbations in simulated and real datasets.

This paper introduces RCTD, a method to decompose cell type mixtures in spatial transcriptomics, improving cell type assignment and enabling discovery of spatially dependent gene expression.

Slide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

This paper constructs a single-cell atlas of the adult mouse brain, identifying 565 transcriptionally distinct cell groups and revealing new insights into neuronal specialization and brain organization.