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bioc
tpSVG:Thin plate models to detect spatially variable genes
The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.
Maintained by Boyi Guo. Last updated 5 months ago.
spatialtranscriptomicsgeneexpressionsoftwarestatisticalmethoddimensionreductionregressionpreprocessingspatially-resolvespatially-variable-genes
2 stars 4.30 score 2 scripts