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SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
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SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
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Journal Article
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
2021
Overview
Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
Publisher
BioMed Central,Springer Nature B.V,BMC
Subject
/ Animal Genetics and Genomics
/ Animals
/ Biomedical and Life Sciences
/ Cerebellum - anatomy & histology
/ Female
/ genome
/ HDST
/ Humans
/ Method
/ Mice
/ Microbial Genetics and Genomics
/ Olfactory Bulb - anatomy & histology
/ Ovarian Neoplasms - genetics
/ Ovarian Neoplasms - metabolism
/ Ovarian Neoplasms - pathology
/ Power
/ Sparsity
