1 group?
Closed this issue · 3 comments
I just read your wonderful paper and would like to ask a question.
What's the meaning of the results if I assign 1 single group for all samples? Is qsmooth
equivalent to standard quantile nomalization under this situation?
The problem I want to solve is that simulated bulk RNA-seq data by single cell data (dataset1) separated far away from real bulk RNA-seq data (dataset2, such as data from TCGA) if we check the first two principal components. Similar to the third column of Fig. 5.
, dataset1 and dataset2 are two separated groups in PC1/PC2 figure. We want to eliminate the difference between two groups. Can we say this difference is caused by batch effect and can be removed by quantile normalization?
Thanks!
Hi @OnlyBelter, yes in the case that there is only 1 group, I believe it is the same as standard quantile normalization.
Just to follow up on the question, currently if there's only 1 group a standard qsmooth()
call fails with
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
Is there a way to run standard quantile normalization from within qsmooth
in these cases or should I use external resources?
Hi,
If there is only one group, you are essentially doing quantile normalization. qsmooth
was designed for at minimum two or more groups. Thanks!