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

By D. Edsgärd, P. Johnsson, Rickard Sandberg. Nature Methods, 2018

299 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ec18db3d9c6aaee23eadc8fe4ae28eb754b367f1

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In Molecular Biology

1. Xiang Zhou
2. Shiquan Sun
3. Jiaqiang Zhu
4. Ying Ma

Citations in the corpus

• Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies (2019, methodology): “For example, when SE strength is high and the cell number is large ( n = 500), consistent with 13 , Trendsceek detected 9, 7 and 12 SE genes for the Hotspot, Streak and Gradient patterns (Figs.”