2019
DOI: 10.1101/810903
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Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies

Abstract: Recent development of various spatially resolved transcriptomic techniques has enabled gene expression profiling on complex tissues with spatial localization information. Identifying genes that display spatial expression pattern in these studies is an important first step towards characterizing the spatial transcriptomic landscape. Detecting spatially expressed genes requires the development of statistical methods that can properly model spatial count data, provide effective type I error control, have sufficie… Show more

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Cited by 5 publications
(4 citation statements)
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“…Previous computational methods search for spatially variable genes without incorporating cell type information [6][7][8]. However, because cell types are not evenly distributed in space, and different cell types have different expression profiles, this approach will likely lead to confusing cell type marker genes with spatially variable genes.…”
Section: Rctd Enables Detection Of Spatially Variable Genes Within Cementioning
confidence: 99%
See 1 more Smart Citation
“…Previous computational methods search for spatially variable genes without incorporating cell type information [6][7][8]. However, because cell types are not evenly distributed in space, and different cell types have different expression profiles, this approach will likely lead to confusing cell type marker genes with spatially variable genes.…”
Section: Rctd Enables Detection Of Spatially Variable Genes Within Cementioning
confidence: 99%
“…A common task of interest is identifying genes with expression varying across space. Current computational methods search for spatial patterns in gene expression without stratifying by cell type [6][7][8]. However, much of the variation detected by these methods may be driven by varying cell type composition across the spatial landscape, since single-cell RNA sequencing (scRNA-seq) studies have revealed that cell type can explain a majority of the variation within a population of cells [9,10].…”
Section: Introductionmentioning
confidence: 99%
“…Our approach can provide an insight into the close relationship between molecular function and structure by identifying important genes responsible for the morphological landscape. A few methods have been proposed to find spatially variable genes employing location information of spots and focusing on the representative patterns of spatial gene expression [28][29][30] . However, they did not employ image features, thus, these methods identify spatially variable markers instead of markers associated with morphologic features.…”
Section: Discussionmentioning
confidence: 99%
“…Currently, there is a limited number of methods available for the analysis of high-resolution spatially-resolved data. One group of methods focuses on the analysis of the significant patterns and the variability of expression of individual markers [19][20][21] to describe the landscape of expression within a tissue. Another group of methods considers, more broadly, the analysis of the interactions between the markers within different spatial contexts, that is the expression in the directly neighboring cells or the effect of the expression of a marker in the 3 broader tissue structure.…”
Section: Introductionmentioning
confidence: 99%