Error in checkFullRank(modelMatrix) :
1
0
Entering edit mode
6 months ago

Hi

I'm comparing the gene expression between two different cell types and my coldata file looks like this:

    > coldata
    sample condition
1  F1-NC12         1
2  F1-NC24         1
3  F1-NC48         1
4  F1-NC72         1
5   F1-C12         1
6   F1-C24         1
7   F1-C48         1
8   F1-C72         1
9  F2-NC12         2
10  F2NC24         2
11 F2-NC48         2
12 F2-NC72         2
13  F2-C12         2
14  F2-C24         2
15  F2-C48         2
16  F2-C72         2

when I try to make a dds object based on the DESeq2 workflow, I get the following error and I'm not sure why.

> dds <- DESeqDataSetFromMatrix(countData = cts,
+                               colData = coldata,
+                               design= ~ sample + condition)
converting counts to integer mode
  the design formula contains one or more numeric variables with integer values,
  specifying a model with increasing fold change for higher values.
  did you mean for this to be a factor? if so, first convert
  this variable to a factor using the factor() function
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')
In addition: Warning message:
In DESeqDataSet(se, design = design, ignoreRank) :
  some variables in design formula are characters, converting to factors

How should I work around this problem? thank you!

DESeq2 • 429 views
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Entering edit mode

Apart from the response below, your design is strange. Why did you want to adjust for sample if you want to compare the expression between the two cell lines (I assume condition 1 or 2) ?

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Entering edit mode
6 months ago
ATpoint 82k

Sample is nested with condition and with itself, meaning that each sample is either condition1 or 2, but for a paired design each sample would need to appear in both conditions. See the vignette on paired designs. You only can do ~condition.

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