Error in Error in checkFullRank(modelMatrix) Deseq2
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@michaelsteffen-12555
Last seen 6.2 years ago

 

Hello, 

I'm trying to understand how to fix this area with my design. It seems like others have had similar issues, but finding a solution for myself has proved difficult. 

I know the issue is in the + design = ~ condition + status line, but I am not entirely sure how to fix it so that both condition and status are taken into effect 

Below is the code I have bee trying to run. 

> total_counts<-read.table(file="aust_raw_counts.txt",head=TRUE,row.names=1)
> expt_design <- data.frame(rows = colnames(total_counts),
+ condition = c("SocA", "SocA", "SocA", "SocB", "SocB", "SocB", "Sol", "Sol", "Sol", "MB", "MB", "MB"),
+ status = c("Repro", "Repro", "Repro", "UnRepro", "UnRepro", "UnRepro", "Repro", "Repro", "Repro", "UnRepro", "UnRepro", "UnRepro"))

> expt_design
   rows condition  status
1  Ca01      SocA   Repro
2  Ca02      SocA   Repro
3  Ca03      SocA   Repro
4  Ca04      SocB UnRepro
5  Ca05      SocB UnRepro
6  Ca06      SocB UnRepro
7  Ca07       Sol   Repro
8  Ca08       Sol   Repro
9  Ca09       Sol   Repro
10 Ca10        MB UnRepro
11 Ca11        MB UnRepro
12 Ca12        MB UnRepro


> dds <- DESeqDataSetFromMatrix( countData = total_counts, colData = expt_design,
+ design = ~ condition + status)
Error in checkFullRank(modelMatrix) : 
  the model matrix is not full rank, so the model cannot be fit as specified.
  One or more variables or interaction terms in the design formula are linear
  combinations of the others and must be removed.

  Please read the vignette section 'Model matrix not full rank':

  vignette('DESeq2')
 

>dds <- estimateSizeFactors(dds)
>dds <- estimateDispersions(dds)

>dds <- nbinomLRT(dds, reduced = ~ 1)

If I can get to this last part here, I think I will have more questions, but until then, thanks for the help.

Mike 

 

 

 

 

 

deseq2 • 1.6k views
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@mikelove
Last seen 1 day ago
United States
Condition and status are confounded (for more details on what this means, obviously first check the vignette section referenced in the message). You can just remove status from the design and make comparisons among the four groups using 'contrast' argument of results()
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So if there no way to control for status? 

Mike 

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I'd recommend you speak with a local statistician to help explain confounding. Status doesn't add information beyond what is given by condition so it is not possible to fit both at the same time.

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