Question: Error in design.matrix in edgeR?
0
gravatar for Björn
18 months ago by
Björn40
Björn40 wrote:

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

  1. B-NC , C-NC, BF-NC, CF-NC which means DE mirnas in each groups taking NC as reference
  2. combined (B+C)/2- NC and (BF+CF)/2- NC
  3. (BF+CF)/2- (B+C)/2
  4. BF-B, CF-C which are DE miRNAs after and before treatment
edger rna-seq mirnaseq R • 886 views
ADD COMMENTlink modified 18 months ago by Devon Ryan91k • written 18 months ago by Björn40

You can edit your posts by clicking on the "edit" button. I lieu of that, I've copied your answer into your post.

ADD REPLYlink written 18 months ago by Devon Ryan91k
0
gravatar for Devon Ryan
18 months ago by
Devon Ryan91k
Freiburg, Germany
Devon Ryan91k wrote:

I assume what you actually want is something like this:

sample   subgroup   treatment
1        B          control
2        B          control
3        C          control
4        C          control
5        B          treatment
...

And so on. The model is then ~subgroup + treatment, since presumably you want the treatment effect while accounting for subgroups.

Given your update:

  1. model ~0 + group1 and use the contrasts you specified.
  2. Define "combined"
  3. model ~0 + group1 and use the contrasts you specified.
  4. model ~0 + group1 and use the contrasts you specified.
ADD COMMENTlink modified 18 months ago • written 18 months ago by Devon Ryan91k

The data set you created is different that the real one. sorry for confusion. The dataset is provided again

ADD REPLYlink written 18 months ago by Björn40

I've updated my reply.

ADD REPLYlink written 18 months ago by Devon Ryan91k
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