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EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning

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成果类型:
期刊论文
作者:
Tu, Jia-Juan;Li, Hui-Sheng;Yan, Hong;Zhang, Xiao-Fei
通讯作者:
Zhang, XF
作者机构:
[Li, Hui-Sheng; Tu, Jia-Juan; Yan, Hong] Ctr Intelligent Multidimens Data Anal, Hong Kong Sci Pk, Hong Kong 999077, Peoples R China.
[Li, Hui-Sheng; Zhang, Xiao-Fei] Cent China Normal Univ, Sch Math & Stat, Dept Stat, Wuhan 430079, Peoples R China.
[Li, Hui-Sheng; Zhang, Xiao-Fei] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.
[Yan, Hong] City Univ Hong Kong, Dept Elect Engn, Hong Kong 999077, Peoples R China.
通讯机构:
[Zhang, XF ] C
Cent China Normal Univ, Sch Math & Stat, Dept Stat, Wuhan 430079, Peoples R China.
Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.
语种:
英文
期刊:
BIOINFORMATICS
ISSN:
1367-4803
年:
2023
卷:
39
期:
1
基金类别:
This work was supported by the National Natural Science Foundation of China [11871026 and 12271198]; Hong Kong Research Grants Council [Projects 11204821]; and Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), and City University of Hong Kong [Project 9610034].
机构署名:
本校为通讯机构
院系归属:
数学与统计学学院
摘要:
Motivation: Spatially resolved gene expression profiles are the key to exploring the cell type spatial distributions and understanding the architecture of tissues. Many spatially resolved transcriptomics (SRT) techniques do not provide single-cell resolutions, but they measure gene expression profiles on captured locations (spots) instead, which are mixtures of potentially heterogeneous cell types. Currently, several cell-type deconvolution methods have been proposed to deconvolute SRT data. Due to the different model strategies of these methods, their deconvolution results also vary. Results:...

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