Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
Ludwig Institute for Cancer Research, Stockholm, Sweden.
Nat Methods. 2018 May;15(5):339-342. doi: 10.1038/nmeth.4634. Epub 2018 Mar 19.
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.
随着单细胞分辨率下空间基因表达测量方法的出现，需要计算分析策略。我们提出了 trendsceek，这是一种基于标记点过程的方法，可识别具有统计学显著空间表达趋势的基因。trendsceek 可在空间转录组学和顺序荧光原位杂交数据中找到这些基因，还可以在分离单细胞 RNA-seq 数据的低维投影中揭示显著的基因表达梯度和热点。
