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SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies.

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第一作者： Jiaqiang,Zhu

第一单位： Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.;Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.

作者： Jiaqiang,Zhu ^[1] ; Shiquan,Sun ^[2] ; Xiang,Zhou ^[3]

作者单位： Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.;Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA. ^[1] Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.;Department of Epidemiology and Biostatistics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, P.R. China. ^[2] Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA. xzhousph@umich.edu.;Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA. xzhousph@umich.edu. ^[3]

关键词 Covariance test HDST Non-parametric modeling SE analysis SPARK SPARK-X Slide-seq Spatial expression pattern Spatial transcriptomics