Cells from normal sample were incorrectly classified as tumor cells using copykat

Question

[tujuchuanli]

Hi everyone,

I am trying to identify tumor cells from an scRNA-seq dataset of cancer patients using copykat (https://github.com/navinlabcode/copykat). Following the manual`s recommendations, I performed the analysis sample by sample using the code below:

  for (sample in samples) {  
  sample_obj <- subset(seurat_obj.filter, subset = orig.ident == sample)  
  count_mtx <- sample_obj@assays$RNA@counts  
  copykat_result <- copykat(  
    rawmat = count_mtx,  
    id.type = "S",  
    ngene.chr = 5,  
    win.size = 25,  
    KS.cut = 0.1,  
    sam.name = sample,  
    distance = "euclidean",  
    norm.cell.names = "",  
    output.seg = "FALSE",  
    plot.genes = "TRUE",  
    genome = "hg20",  
    n.cores = 1  
  )  
  save(copykat_result, file = paste("copykat_result.", dataset, "-", sample, ".Rdata", sep = ""))  
}

However, when I checked the results, I noticed that a substantial proportion of cells (around 50% in one particular sample) from a normal sample were incorrectly classified as tumor cells. I suspect this might be due to suboptimal parameter settings, but I’m not sure how to adjust them effectively.

I would greatly appreciate any suggestions or advice on how to optimize the parameters or improve the analysis.

Thanks in advance!

[Arup Ghosh]

Provide a vector of representative normal cells from each normal sample in the norm.cell.names option.

[tujuchuanli]

Since I'm running an automated pipeline to process multiple datasets, can I simply use the immune cells from CD45+ clusters in each dataset as the normal cells? I'll try this approach and share my feedback.

Thanks for your precious suggestion~~.