ScType Enables Fast and Accurate Cell Type Identification from Spatial Transcriptomics Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 Jun 27

PMID 38936341

Authors

Kristen Nader

Misra Tasci

Aleksandr Ianevski

Andrew Erickson

Emmy W Verschuren

Tero Aittokallio

Mitro Miihkinen

Affiliations

Soon will be listed here.

Abstract

Summary: The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation.

Availability And Implementation: scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.

Citing Articles

SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq.

Li X, Zhu F, Min W Brief Bioinform. 2024; 25(6).

PMID: 39508444 PMC: 11541600. DOI: 10.1093/bib/bbae571.

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