DESeq2 DESeqDataSetFromTximport design formula
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@stephensmith-13036
Last seen 7.2 years ago

Hi,

I am doing some RNA-seq analysis and am running into the Matrix is not full rank error.

It has 16 samples, 8 different treatments so 2 samples per treatment

If i run this

ddsTxi= DESeqDataSetFromTximport( txi, colData= samples, design= ~condition)

it works but anything other than this, e.g.,

ddsTxi= DESeqDataSetFromTximport( txi, colData= samples, design= ~condition + eya1six1)

give the error.

 

Would it be best to just go with one column (condition) then paste together whichever factors I want to compare further down the line?

Thanks,

Stephen

 

deseq2 • 7.0k views
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sina.nassiri ▴ 130
@sinanassiri-10062
Last seen 3.0 years ago
Switzerland/Lausanne

Not sure what you mean by "paste together whichever factors I want to compare further down the line", but the problem with matrix not being full rank is that the columns for condition and eya1six1 are linearly dependent in the design matrix, i.e. condition and eya1six1 are confounding. What's your end goal here?

PS: You can check whether your design matrix is full rank or not using is.fullrank() from the limma package.

 

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I guess what I'd like to do is to be able to compare all samples which are expressing six1, eya1, or six1eya1 with the rest in order to increase my statistical significance.

So when I run contast I can call "six1eya1", "y", "n" or contrast= c("six1", "y", "n") etc.

I'm starting to think it might be easier to just run multiple DESeq2 tests with different design matrices for this.

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You can load the data at first with design=~1, and then change the design as you like. After changing the design, you need to re-run DESeq() to have the estimates updated.

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Ah very nice. That means I won't have to rerun tximport every time.

Thank you very much Michael.

Finding salmon, tximport, deseq2 a very nice pipeline!

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Thanks!
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Very new user. Thank you for the great work! I am wondering how can you change the design after you already import date using tximport? Could you give an example? Thank you!

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You can use design(dds) <- ~ ...

The important thing is only that you have to re-run DESeq() after you change the design.

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