Hi all, when I run to this step, it showed an error like this.
ltr <- comppvalue(ltr, pthr = 0.01, sensitive = FALSE)
Error in if (quantile(x, 1 - pthr) < cm[i, j]) 0 else 0.5 :
missing value where TRUE/FALSE needed
The original matrix was extracted from a Seurat object like this:
pro='S1'
sce <- CreateSeuratObject(Read10X('./scdata/allraw/day0/'),
pro)
sce <- NormalizeData(sce, normalization.method = "LogNormalize",
scale.factor = 10000)
GetAssay(sce,assay = "RNA")
sc <- as(as.matrix(sce@assays$RNA@counts), 'sparseMatrix')
sc <- SCseq(sc)
sc <- filterdata(sc,FGenes = hbb)
hbb <- rownames(sce)[grep("^Hbb",rownames(sce))]
sc <- filterdata(sc,FGenes = hbb)
sc_p <- compdist(sc,metric="pearson")
sc_p_n20_k <- clustexp(sc_p,sat = T,clustnr = 20,FUNcluster = 'kmedoids')
plotsaturation(sc_p_n20_k,disp=FALSE)
plotjaccard(sc_p_n20_k)
sc_p_n20_k <- clustexp(sc_p_n20_k,cln=10,sat=FALSE)
sc_p_n20_k <- findoutliers(sc_p_n20_k)
plotbackground(sc_p_n20_k)
plotsensitivity(sc_p_n20_k)
plotoutlierprobs(sc_p_n20_k)
clustheatmap(sc_p_n20_k)
sc_p_n20_k <- comptsne(sc_p_n20_k)
sc_p_n20_k <- compfr(sc_p_n20_k,knn=10)
sc_p_n20_k <- compumap(sc_p_n20_k)
plotmap(sc_p_n20_k, final = T, tp = 1, fr = T, um = F, cex = 0.5)
plotexpmap(sc_p_n20_k,"Procr",logsc=TRUE,fr=F)
ltr <- Ltree(sc_p_n20_k)
ltr <- compentropy(ltr)
ltr <- projcells(ltr,cthr=5,nmode=FALSE,fr=TRUE)
ltr <- projback(ltr,pdishuf=100)
ltr <- lineagegraph(ltr)
everything is OK until comppvalue