MUSTANG:利用跨样本转录相似性指导进行多样本空间转录组学数据分析

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Seyednami Niyakan, Jianting Sheng, Yuliang Cao, Xiang Zhang, Zhan Xu, Ling Wu, Stephen T.C. Wong, Xiaoning Qian
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引用次数: 0

摘要

空间解析转录组学通过提供高分辨率的转录模式表征,彻底改变了基因组规模的转录组学分析。在这里,我们介绍了我们的空间转录组学分析框架 MUSTANG(MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance),它能够通过基于表达的跨样本相似性信息共享以及样本内基因表达模式的空间相关性来执行多样本空间转录组学定点细胞解卷积。在一个半合成空间转录组学数据集和三个真实世界空间转录组学数据集上的实验证明了 MUSTANG 在揭示所研究组织样本细胞特征内在的生物学见解方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
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