-
Notifications
You must be signed in to change notification settings - Fork 10
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Could I use this method to infer cell type from samples which are not tumor samples, likes untreated and treated samples? #24
Comments
Hello, I try my data on the website and I don't know how to solve this error, could you help me check the input data format? % Total % Received % Xferd Average Speed Time Time Time Current 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 |
Thank you for your interest in our work. If we assume there is no sex-specific transcription on genes from somatic chromosomes, you can merge the female and male scRNA-seq and then exclude genes from sex chromosomes (please refer to the vignette of the cleanup.genes function for details ). Otherwise, you may deconvolve using scRNA-seq from the matched sex. |
I will forward your question to our web developer to follow up. |
Hello, very great method! But I have normal and treated mouse hypothalamus scRNA-seq data and I wonder could I use this method on my data? Also, could sex bias affect the inference results? I mean, could I use female scRNA-seq data to inference female bulk RNA-seq data, male scRNA-seq for bulk RNA-seq data? Or I can merge female and male scRNA-seq data together and inferecne all the bulk RNA-seq data? Thanks a lot for your help.
The text was updated successfully, but these errors were encountered: