SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

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 transcriptom...

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Bibliographic Details
Published inGenome Biology Vol. 22; no. 1; p. 184
Main Authors Zhu, Jiaqiang, Sun, Shiquan, Zhou, Xiang
Format Journal Article
LanguageEnglish
Published England BioMed Central 21.06.2021
BMC
Subjects
Algorithms
Animals
Approximation
Cerebellum - anatomy & histology
Cerebellum - metabolism
Computer applications
Computer Simulation
Covariance test
data collection
Datasets as Topic
Female
Gene Expression Regulation
Generalized linear models
genome
HDST
Humans
Method
Mice
Models, Spatial Interaction
Non-parametric modeling
Nonparametric statistics
Normal distribution
Olfactory Bulb - anatomy & histology
Olfactory Bulb - metabolism
Ovarian Neoplasms - genetics
Ovarian Neoplasms - metabolism
Ovarian Neoplasms - pathology
Performance evaluation
Power
RNA, Messenger - genetics
RNA, Messenger - metabolism
SE analysis
Simulation
Single-Cell Analysis
Slide-seq
Sparsity
Spatial transcriptomics
Transcriptome
transcriptomics
Slide-seq
SE analysis
Spatial transcriptomics
Spatial expression pattern
Covariance test
Non-parametric modeling
SPARK
SPARK-X
HDST
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Summary: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.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-021-02404-0