SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
Abstract
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.
Journal
Genome BiologyInstitutions
Authors
Funding
Develop new bioinformatics infrastructures and computational tools for epitranscriptomics dataEarly adversity and DNA methylation in a primate model of stress and development.Molecular interactions of mammalian circadian clock proteinsQuantitative definition of cell identity from single-cell multi-omic, spatial, and perturbation dataDeveloping new computational tools for spatial transcriptomics dataDMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics StudiesNew Computational Tools for Advanced Analytics in Genome-wide Association StudiesNew directions in single cell genomics method developmentNew Computational Tools for Advanced Analytics in Genome-wide Association StudiesDeveloping new computational tools for spatial transcriptomics dataNew directions in single cell genomics method developmentDMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics StudiesNational Science Foundation Grant DMS1712933Early adversity and DNA methylation in a primate model of stress and development.