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Can the CNAmtx.RData (I have these from previous analysis outputs) be used to regenerate the CNV heatmap and if so, is it possible to merge hundreds of samples together, something that is not possible when running the package due to memory limitations. My concern is that the CNVs are probably normalized for each sample, therefore it is not possible to compare the results between samples?
Preferably I would like to generate subclones for each patient and compare these between hundreds of patients. How would one go about trying to compare subclones/CNV profiles between hundreds of patients?
The text was updated successfully, but these errors were encountered:
Hi, I have a question along similar lines. I see that one can provide confident normal cells as reference which I would like to do to make results more comparable between different donors. It would be great if your detection of confident normal cells could run separately from the rest of the pipeline (I tried to take your functions apart to make that work but am running into issues). Could you add to your pipeline that barcodes of confident normal cells are provided as an output t? It would then be possible to run their detection on a merged-donor datasets, filter out the normal cells, add them to each donor-specific dataset and run the pipeline.
Can the CNAmtx.RData (I have these from previous analysis outputs) be used to regenerate the CNV heatmap and if so, is it possible to merge hundreds of samples together, something that is not possible when running the package due to memory limitations. My concern is that the CNVs are probably normalized for each sample, therefore it is not possible to compare the results between samples?
Preferably I would like to generate subclones for each patient and compare these between hundreds of patients. How would one go about trying to compare subclones/CNV profiles between hundreds of patients?
The text was updated successfully, but these errors were encountered: