Downloads provided by UsageCountsSPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
Copyright policy )NSF| High Dimensional Mediation Analysis with Multi-Omics Data ,
NIH| Developing new computational tools for spatial transcriptomics data ,
NIH| New Computational Tools for Advanced Analytics in Genome-wide Association Studies ,
NIH| DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies ,
NIH| New directions in single cell genomics method development ,
NIH| Early adversity and DNA methylation in a primate model of stress and development.
Zhu, Jiaqiang; Sun, Shiquan; Zhou, Xiang;
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
AbstractSpatial 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.
United States
- University of Michigan–Flint United States
- University of Michigan–Ann Arbor United States
- University of Michigan United States
- UNIVERSITY OF MICHIGAN AT ANN ARBOR
- Xi'an Jiaotong University China (People's Republic of)
QH301-705.5, Method, Datasets as Topic, Non-parametric modeling, QH426-470, Spatial transcriptomics, Mice, Covariance test, Models, Spatial Interaction, HDST, Cerebellum, Genetics, Animals, Humans, Computer Simulation, RNA, Messenger, Biology (General), Slide-seq, Ovarian Neoplasms, SE analysis, Olfactory Bulb, Gene Expression Regulation, Female, Single-Cell Analysis, Transcriptome, Algorithms
QH301-705.5, Method, Datasets as Topic, Non-parametric modeling, QH426-470, Spatial transcriptomics, Mice, Covariance test, Models, Spatial Interaction, HDST, Cerebellum, Genetics, Animals, Humans, Computer Simulation, RNA, Messenger, Biology (General), Slide-seq, Ovarian Neoplasms, SE analysis, Olfactory Bulb, Gene Expression Regulation, Female, Single-Cell Analysis, Transcriptome, Algorithms
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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NSF| High Dimensional Mediation Analysis with Multi-Omics Data, NIH| Developing new computational tools for spatial transcriptomics data, NIH| New Computational Tools for Advanced Analytics in Genome-wide Association Studies, NIH| DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies
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