典型文献
TIST:Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics
文献摘要：
Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypi-cal information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcrip-tomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in dif-ferent biological scenarios.TIST is available at .
文献关键词：
中图分类号：
作者姓名：
Yiran Shan;Qian Zhang;Wenbo Guo;Yanhong Wu;Yuxin Miao;Hongyi Xin;Qiuyu Lian;Jin Gu
作者机构：
MOE Key Laboratory of Bioinformatics,BNRIST Bioinformatics Division,Department of Automation,Tsinghua University,Beijing 100084,China;UM-SJTU Joint Institute,Shanghai Jiao Tong University,Shanghai 200240,China;Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China
文献出处：
引用格式：
[1]Yiran Shan;Qian Zhang;Wenbo Guo;Yanhong Wu;Yuxin Miao;Hongyi Xin;Qiuyu Lian;Jin Gu-.TIST:Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics)[J].基因组蛋白质组与生物信息学报（英文版）,2022(05):974-988
A类：
TIST,phenotypi,devises,tomic
B类：
Transcriptome,Histopathological,Image,Integrative,Analysis,Spatial,Transcriptomics,Sequencing,spatial,transcriptomics,emerging,technology,study,situ,expression,patterns,whole,genome,scale,Currently,analysis,still,complicated,by,high,technical,noises,low,resolution,addition,matched,histopathological,images,are,usually,generated,same,tissue,along,experiment,provide,complementary,cellular,information,providing,opportunity,mitigate,We,present,novel,called,transcriptome,integrative,which,enables,identification,clusters,SCs,enhancement,extraction,Markov,random,field,MRF,learn,features,from,integrates,them,location,network,termed,Based,identified,walk,strategy,enhanced,neighborhood,smoothing,benchmark,both,simulated,datasets,real,samples,against,several,state,art,methods,Results,show,that,robust,multiple,tasks,sequencing,can,find,interesting,microstructures,dif,ferent,biological,scenarios,available
AB值：
0.48964
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