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.

中文翻译：

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识别单细胞基因表达数据中的空间表达趋势

随着以单细胞分辨率测量空间基因表达的方法变得可用，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，可以识别具有统计显着空间表达趋势的基因。Trendsceek 在空间转录组和顺序荧光原位杂交数据中发现了这些基因，并在解离的单细胞 RNA-seq 数据的低维投影中揭示了显着的基因表达梯度和热点。