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文章目录

    • A comprehensive comparison on cell-type composition inference for spatial transcriptomics data
      • Abstract
      • Introduction
      • Computational methods developed for cell-type deconvolution of ST data
      • Benchmarking ST deconvolution methods performance
      • 文中重要图表


A comprehensive comparison on cell-type composition inference for spatial transcriptomics data

Author:Jiawen Chen, Weifang Liu, Tianyou Luo, et al.

Journal:Briefings in Bioinformatics

Year:2022

DOI:10.1093/bib/bbac245

Keywords:spatial transcriptomics, single-cell, cell-type deconvolution, deep learning, probabilistic modeling

Code:9 in the paper

Abstract

空间转录组学(ST)技术使研究人员能够在保持位置信息的同时检查转录情况.

学习笔记,仅供参考,有错必纠

博客阅读索引:博客阅读及知识获取指南


文章目录

    • A comprehensive comparison on cell-type composition inference for spatial transcriptomics data
      • Abstract
      • Introduction
      • Computational methods developed for cell-type deconvolution of ST data
      • Benchmarking ST deconvolution methods performance
      • 文中重要图表


A comprehensive comparison on cell-type composition inference for spatial transcriptomics data

Author:Jiawen Chen, Weifang Liu, Tianyou Luo, et al.

Journal:Briefings in Bioinformatics

Year:2022

DOI:10.1093/bib/bbac245

Keywords:spatial transcriptomics, single-cell, cell-type deconvolution, deep learning, probabilistic modeling

Code:9 in the paper

Abstract

空间转录组学(ST)技术使研究人员能够在保持位置信息的同时检查转录情况.

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