MBIC plot visualization

Intuitive way of visualizing how modified BIC values changes across different number of clusters

mbicPlot(seu, criteria="MBIC")

Arguments

seu

an object of class Seurat revised by DR.SC with argument K=NULL.

criteria

a string specifying the information criteria such as AIC, BIC and MBIC to be plotted, default as MBIC.

Details

Nothing

Value

return a ggplot2 object.

References

None

Author

Wei Liu

Note

nothing

See also

None

Examples

  ## we generate the spatial transcriptomics data with lattice neighborhood, i.e. ST platform.
  seu <- gendata_RNAExp(height=20, width=20,p=100, K=4)
  library(Seurat)
  seu <- NormalizeData(seu)
  # choose spatially variable features
  seu <- FindSVGs(seu)
#> Find the spatially variables genes by SPARK-X...
#> ## ===== SPARK-X INPUT INFORMATION ====
#> ## number of total samples: 400
#> ## number of total genes: 100
#> ## 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
  ## Just for illustrating the usage of mbicPlot
  seu[["RNA"]]@misc[['icMat']] <- data.frame(K=2:5, MBIC=c(105, 101, 99, 108))
  mbicPlot(seu)