RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.

ABSTRACT: Spatial transcriptomics (ST) profiles gene expression in intact tissues. However, ST data measured at each spatial location may represent gene expression of multiple cell types, making it difficult to identify cell-type-specific transcriptional variation across spatial contexts. Existing cell-type deconvolutions of ST data often require single-cell transcriptomic references, which can be limited by availability, completeness and platform effect of such references. We present RETROFIT, a reference-free Bayesian method that produces sparse and interpretable solutions to deconvolve cell types underlying each location independent of single-cell transcriptomic references. Results from synthetic and real ST datasets acquired by Slide-seq and Visium platforms demonstrate that RETROFIT outperforms

SUBMITTER: Singh R 

PROVIDER: S-EPMC10274808 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature