USF-LVHN SELECT

Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.

Publication/Presentation Date

4-29-2022

Abstract

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

Volume

13

Issue

1

First Page

2339

Last Page

2339

ISSN

2041-1723

Disciplines

Medical Education | Medicine and Health Sciences

PubMedID

35487922

Department(s)

USF-LVHN SELECT Program, USF-LVHN SELECT Program Students

Document Type

Article

Link to Full Text

Share

COinS