Journal Article

Robust decomposition of cell type mixtures in spatial transcriptomics

Cable, Dylan M., Chen, Fei, Goeva, Aleksandrina, Macosko, Evan Z., Irizarry, Rafael A., Zou, Luli S., Murray, Evan

2022

Overview

A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD . Cell type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/2415 / 631/61 / 631/61/212 / 631/61/212/2019 / Agriculture / Animals / Bioinformatics / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Biomedicine / Biotechnology / Computational neuroscience / Datasets / Decomposition / Exome Sequencing / Life Sciences / Localization / Mapping / Mice / Mixtures / Sequence Analysis, RNA / Single-Cell Analysis / Software / Tissues / Transcriptome - genetics / Transcriptomics