Normalized Shannon entropy-based "unclassified" assignment
CID.entropy calculates the normalized Shannon entropy of labels for each cell
among k-nearest neighbors less than four-degrees apart, and then sets cells with statistically
significant large Shannon entropy to be "Unclassified."
CID.entropy(ac, distM)
ac |
a character vector of cell type labels |
distM |
the distance matrix, see ?CID.GetDistMat |
A character vector like 'ac' but with cells type labels set to "Unclassified" if there was high normalized Shannon entropy.
## Not run:
# load data classified previously (see \code{SignacFast})
P <- readRDS("celltypes.rds")
S <- readRDS("pbmcs.rds")
# get edges from default assay from Seurat object
default.assay <- Seurat::DefaultAssay(S)
edges = S@graphs[[which(grepl(paste0(default.assay, "_nn"), names(S@graphs)))]]
# get distance matrix
D = CID.GetDistMat(edges)
# entropy-based unclassified labels labels
entropy = CID.entropy(ac = P$L2, distM = D)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.