FindSVGs: Find spatially variable genes

In feiyoung/DR.SC: Joint Dimension Reduction and Spatial Clustering

Find spatially variable genes

Description

Identifies features that have spatially variation along spots using SPARK-X.

Usage

  FindSVGs(seu, nfeatures=2000, covariates=NULL,
             preHVGs=5000,num_core=1, verbose=TRUE)

Arguments

seu	an object of class "Seurat".
nfeatures	a positive integer, means how many spatially variable genes to be chosen. If there are less than 2000 features in seu, then all features are identified.
covariates	a covariate matrix named control variable matrix whose number of rows is equal to the number of columns of seu.
preHVGs	a positive integer, the number of highly variable genes selected for speeding up computation of SPARK-X in selecting spatially variable features.
num_core	an optional positive integer, specify the cores used for identifying the SVGs in parallel.
verbose	an optional logical value, whether output the related information.

Details

Nothing

Value

return a revised Seurat object by adding three columns named "is.SVGs", "order.SVGs" and "adjusted.pval.SVGs" in the meta.features of default Assay.

Note

nothing

References

Zhu, J., Sun, S., Zhou, X.: Spark-x: non-parametric modeling enables scalable and robust detection of spatialexpression patterns for large spatial transcriptomic studies. Genome Biology 22(1), 1-25 (2021)

See Also

topSVGs

Examples

  seu<-gendata_RNAExp(height=20, width=20,p=200, K=4)
  seu<-FindSVGs(seu, nfeatures=100)
  topSVGs(seu)

feiyoung/DR.SC documentation built on March 23, 2024, 6:31 p.m.