My research question is to find difference of miRNA in Groups1 before and after treatment. Sub-groups "B", "C" are pre while "BF", and "CF" are post samples.
basefup<-factor(group$Groups1, levels = c("B", "BF", "C", "CF", "NC")) treatment<-factor(group$Groups, levels = c("NC", "pre", "post")) #Design matrix design<-model.matrix(~0+basefup+treatment) colnames(design) y <- estimateDisp(y, design, robust=TRUE)
Gives following error:
Error in glmFit.default(sely, design, offset = seloffset, dispersion = 0.05, : Design matrix not of full rank. The following coefficients not estimable: treatmentpre treatmentpost
any suggestions ?
My data structure is as shown below which are paired data and NC as control without paired data.
BF is after treatment sample of B, CF is after treatment sample of C. The B, C are two different clinical conditions.
Groups Patient Groups1 pre a B pre b B pre c B post a BF post b BF post c BF pre d B pre e B pre f B post d BF post e BF post f BF pre g C pre h C pre i C post g CF post h CF post i CF NC x NC NC y NC NC z NC
what I want to do is to compare DE miRNAs between
- B-NC , C-NC, BF-NC, CF-NC which means DE mirnas in each groups taking NC as reference
- combined (B+C)/2- NC and (BF+CF)/2- NC
- (BF+CF)/2- (B+C)/2
- BF-B, CF-C which are DE miRNAs after and before treatment