DESpace - DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions
Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions. See Cai et al. (2024) <doi:10.1093/bioinformatics/btae027> and Cai et al. (2026) <doi:10.1093/bioinformatics/btag450>.
Last updated
spatialsinglecellrnaseqtranscriptomicsgeneexpressionsequencingdifferentialexpressionstatisticalmethodvisualization
6.51 score 8 stars 68 scripts 523 downloadsmuSpaData - Multi-sample multi-group spatially resolved transcriptomic data
Data package containing a multi-sample multi-group spatial dataset in SpatialExperiment Bioconductor object format.
Last updated
experimenthubexperimentdataexpressiondataspatialdatasinglecelldatadatapkg
4.48 score 1 dependents 8 scripts