Identification of spatial expression trends in single-cell gene expression data

被引：175
|
作者：
Edsgard, Daniel [1 ,2 ]
Johnsson, Per [1 ,2 ]
Sandberg, Rickard [1 ,2 ]
机构：
[1] Karolinska Inst, Dept Cell & Mol Biol, Stockholm, Sweden
[2] Ludwig Inst Canc Res, Stockholm, Sweden
来源：
NATURE METHODS | 2018年 / 15卷 / 05期
基金：
瑞典研究理事会; 欧洲研究理事会;
关键词：
REVEALS;
D O I：
10.1038/nmeth.4634
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
As methods for measuring spatial gene expression at single-cell resolution become available, there is a need for computational analysis strategies. We present trendsceek, a method based on marked point processes that identifies genes with statistically significant spatial expression trends. trendsceek finds these genes in spatial transcriptomic and sequential fluorescence in situ hybridization data, and also reveals significant gene expression gradients and hot spots in low-dimensional projections of dissociated single-cell RNA-seq data.
引用
收藏
页码：339 / +
页数：7
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