Many thanks for this tool!
I currently working on a snRNA seq dataset derived from a mouse model for breast cancer.
I have an integrated dataset which contains one normal sample and two cancer samples ( different time points/ tumor progression). So I used this integrated dataset for the copykat by giving in the function all the cells from the healthy sample as the normal cells.
Unfortunately the results are not the best, some healthy cells are assigned as aneuploid.
I was wondering if anyone has any suggestions to go round this issue. I am trying tuning some of the arguments but still I have this problem.
I've read the issue #49 where you are saying that maybe the tumor aneuploidy is weak
I will appreciate any help,
Thanks in advance,
Anna
Hello,
Many thanks for this tool! I currently working on a snRNA seq dataset derived from a mouse model for breast cancer. I have an integrated dataset which contains one normal sample and two cancer samples ( different time points/ tumor progression). So I used this integrated dataset for the copykat by giving in the function all the cells from the healthy sample as the normal cells.
Unfortunately the results are not the best, some healthy cells are assigned as aneuploid. I was wondering if anyone has any suggestions to go round this issue. I am trying tuning some of the arguments but still I have this problem.
I've read the issue #49 where you are saying that maybe the tumor aneuploidy is weak
I will appreciate any help, Thanks in advance, Anna
note: here is the function
copykat.test <- copykat(rawmat=exp.rawdata_snRNA, id.type="S", cell.line="no", ngene.chr=5, win.size=5, KS.cut=0.2, sam.name="snRNA",norm.cell.names=normal_cell_names, distance="euclidean",genome="mm10", output.seg=TRUE,plot.genes = TRUE, n.cores=50)