FindSVGs.Rd
Identifies features that have spatially variation along spots using SPARK-X.
FindSVGs(seu, nfeatures=2000, covariates=NULL,
preHVGs=5000,num_core=1, verbose=TRUE)
an object of class "Seurat".
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.
a covariate matrix named control variable matrix whose number of rows is equal to the number of columns of seu.
a positive integer, the number of highly variable genes selected for speeding up computation of SPARK-X in selecting spatially variable features.
an optional positive integer, specify the cores used for identifying the SVGs in parallel.
an optional logical value, whether output the related information.
Nothing
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.
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)
nothing
data(seu)
seu<-FindSVGs(seu, nfeatures=100)
#> Find the spatially variables genes by SPARK-X...
#> ## ===== SPARK-X INPUT INFORMATION ====
#> ## number of total samples: 100
#> ## number of total genes: 50
#> ## Running with single core, may take some time
#> ## Testing With Projection Kernel
#> ## Testing With Gaussian Kernel 1
#> ## Testing With Gaussian Kernel 2
#> ## Testing With Gaussian Kernel 3
#> ## Testing With Gaussian Kernel 4
#> ## Testing With Gaussian Kernel 5
#> ## Testing With Cosine Kernel 1
#> ## Testing With Cosine Kernel 2
#> ## Testing With Cosine Kernel 3
#> ## Testing With Cosine Kernel 4
#> ## Testing With Cosine Kernel 5
topSVGs(seu)
#> [1] "gene49" "gene26" "gene45" "gene11" "gene1"