Main worfklow
The following code snippet give an example of the main commands to
use in order to process your dataset with SingleCellSignalR
package.
Code
data(example_dataset,package='SingleCellSignalR')
mat <- log1p(data.matrix(example_dataset[,-1]))/log(2)
rownames(mat) <- example_dataset[[1]]
rme <- rowMeans(mat)
mmat <- mat[rme>0.05,]
d <- dist(t(mmat))
h <- hclust(d, method="ward.D")
clusters <- paste0("pop_", cutree(h, 5))
# SCSRNoNet -> LRscore based approach
scsrnn <- SCSRNoNet(mat,normalize=FALSE,method="log-only",
min.count=1,prop=0.001,
log.transformed=TRUE,populations=clusters)
scsrnn <- performInferences(scsrnn,verbose=TRUE,
min.logFC=1e-10,max.pval=1,min.LR.score=0.5)## Computing diffential expression tables:## pop_1## pop_2## pop_3## pop_4## pop_5## Computing autocrine naive (network-free) interactions## Computing paracrine naive (network-free) interactionsCode
# SCSRNet -> DifferentialMode based approach
scsrcn <- SCSRNet(mat,normalize=FALSE,method="log-only",
min.count=1,prop=0.001,
log.transformed=TRUE,populations=clusters)
if(FALSE){
scsrcn <- performInferences(scsrcn,
selected.populations = c("pop_1","pop_2","pop_3"),
verbose=TRUE,rank.p=0.8,
min.logFC=log2(1.01),max.pval=0.05)
print("getAutocrines")
inter1 <- getAutocrines(scsrcn, "pop_1")
head(inter1)
print("getParacrines")
inter2 <- getParacrines(scsrcn, "pop_1","pop_2")
head(inter2)
# Visualisation
cellNetBubblePlot(scsrcn)
}