Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xiya Guo, Jin Ning, Yuanze Chen, Guoliang Liu, Liyan Zhao, Yue Fan, Shiquan Sun
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引用次数: 0

Abstract

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

用于单细胞 RNA-seq 和空间解析转录组研究的差异表达分析的最新进展。
差异表达(DE)分析是分析单细胞 RNA 测序(scRNA-seq)和空间分辨转录组学(SRT)数据的必要步骤。与传统的大容量 RNA-seq 不同,scRNA-seq 或 SRT 数据的差异表达分析具有独特的特点,可能导致难以检测到差异表达基因。然而,由于有大量的 DE 工具可在各种假设条件下工作,因此很难选择合适的工具。此外,关于从多条件、多样本实验设计中检测scRNA-seq数据或SRT数据中的DE基因,目前还缺乏全面的综述。为了弥补这一空白,我们在此首先关注 DE 检测所面临的挑战,然后强调促进 scRNA-seq 或 SRT 分析进一步发展的潜在机遇,最后为选择合适的 DE 工具或开发新的计算 DE 方法提供见解和指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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