Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis

Abstract

Dissecting tissue compartments in spatial transcriptomics (ST) remains challenging due to limited spatial resolution and dependence on single-cell reference data. We present Chrysalis, a novel method to rapidly detect tissue compartments through spatially variable gene (SVG) detection and archetypal analysis without external references. We applied Chrysalis on ST datasets originating from various species, tissues and technologies and demonstrated state-of-the-art performance in identifying cellular niches.