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

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.

• Publication year

  2018

• Venue

  Nature Methods

• Publication date

  2018-03-19

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1038/nmeth.4634PMID 29553578PMCID 6314435
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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