Year: 2023
Type: article
Abstract: Single-cell RNA (scRNA) sequencing permits the characterization of distinct cellular conditions through the analysis of transcriptomes. Contrasted with bulk sequencing, single-cell methods reveal detailed gene expression, exposing cellular heterogeneity and enabling deep exploration of development and disease. Recently, spatial transcriptomics (ST) emerges and focuses on tissue-level gene expression patterns to capture spatial gene expression profiles. The paper compares and contrasts the approaches currently used to find genes that are differently expressed in ST. This article discusses three noteworthy analysis methods, SpatialDE, BOOST-GP, and CTSV, considers their drawbacks, and suggests new routes for improvement. The purpose of this paper is to provide useful guidelines for performing differential analysis in future ST research by a thorough overview of various techniques. Future research should aim for flexible models, accurate analyses, and richer biological insights. These efforts will enhance comprehension of spatiotemporal gene regulation, illuminate tissue-cell interactions, and advance health and disease studies. This paper is devoted to expanding ST's contribution to life sciences.
DOI: 10.54097/kt4d4e47
Author: Tianyi Wang
Institution: Shanghai University of Finance and Economics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4391277013