Very grateful for your work with copykat! Excited to try this out.
I wonder how copykat recognizes tumor subclone. The copykat_with_genes_heatmap.pdf only classificate tumor and normal cells.
If I want to identify tumor subclones, do I have to set the number of clones myself according to Step 5: define subpopulations of aneuploid tumor cells?For example, in github REANME:
tumor.cells <- pred.test$cell.names[which(pred.test$copykat.pred=="aneuploid")] tumor.mat <- CNA.test[, which(colnames(CNA.test) %in% tumor.cells)] hcc <- hclust(parallelDist::parDist(t(tumor.mat),threads =4, method = "euclidean"), method = "ward.D2") hc.umap <- cutree(hcc,2)
@gaobio
Hi Gaobio,
Very grateful for your work with copykat! Excited to try this out. I wonder how copykat recognizes tumor subclone. The copykat_with_genes_heatmap.pdf only classificate tumor and normal cells. If I want to identify tumor subclones, do I have to set the number of clones myself according to Step 5: define subpopulations of aneuploid tumor cells?For example, in github REANME:
tumor.cells <- pred.test$cell.names[which(pred.test$copykat.pred=="aneuploid")] tumor.mat <- CNA.test[, which(colnames(CNA.test) %in% tumor.cells)] hcc <- hclust(parallelDist::parDist(t(tumor.mat),threads =4, method = "euclidean"), method = "ward.D2") hc.umap <- cutree(hcc,2)Thanks!