Question

DESeq2 LRT Error: less than one degree of freedom, perhaps full and reduced models are not in the correct order

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musa ▴ 10

@musa-4898

Last seen 8.4 years ago

Hi Michael,

I am trying to use the DESseq package to estimate ribosome loading efficiency as described in "systemPipeR Workflow for Ribo-Seq and polyRibo-Seq Experiments".

But I have run into some problems that I can't understand the genesis i.e.

"Error in nbinomLRT(object, full = full, reduced = reduced, betaPrior = betaPrior, :

less than one degree of freedom, perhaps full and reduced models are not in the correct order"

I have tried to look around the source of this error in the manpages but can't find it.

My colData looks like this:

DataFrame with 8 rows and 2 columns

assay condition

M1A Ribo M1

M1B Ribo M1

A1A Ribo A1

A1B Ribo A1

V1A mRNA M1

V1B mRNA M1

M6A mRNA A1

M6B mRNA A1

with a datafile matching the colData row.names and as you can see every sample has a df>1

But when I run:

dds=DESeq(dds,test="LRT",reduced= ~assay+condition)

I get the above error message.

Is there something am missing?

Thanks,

Musa

deseq2 systempiper Tutorial • 4.2k views

ADD COMMENT • link 8.5 years ago musa ▴ 10

score 0 · Answer 1 · 2016-06-28

hi Musa,

What is the design you used when creating the dds? You should read over the "Likelihood ratio test section" of the vignette

vignette("DESeq2")

and this help page which explains how the LRT works in DESeq2:

?nbinomLRT

You don't have to use the nbinomLRT() function directly, it is used within DESeq(), but this is where we have the help information.

score 0 · Answer 2 · 2016-06-28

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musa ▴ 10

@musa-4898

Last seen 8.4 years ago

Hi Michael,

Thanks for your response

This is how I created the dds

dds=DESeqDataSetFromMatrix(countData=as.matrix(y[,rownames(coldata)]),colData=coldata,design=~assay+condition:condition)

with coldata as is in the email above.

I have looked at the vignette but can't figure out the source or what likely causes this type of error.

Musa

ADD COMMENT • link 8.5 years ago musa ▴ 10

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note: I'm adding a comment to your post (you are posting an answer to your own original question)

The condition:condition term doesn't make any sense. What is your biological question you want to answer?

ADD REPLY • link 8.5 years ago Michael Love 43k

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I want to test if the ribosome release score in M1 is different from A1 ( (Ribo A 1/mRNA A 1)/(Ribo M 1/mRNA M 1). I was just testing the systemPipeR workflow as described in there and that was the design.

ADD REPLY • link 8.5 years ago musa ▴ 10

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You want a design of ~assay + condition + condition:assay and a reduced of ~assay + condition.

See also the section of ?results about interpreting results tables from likelihood ratio tests.

ADD REPLY • link 8.5 years ago Michael Love 43k

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That worked. Thanks.

ADD REPLY • link 8.5 years ago musa ▴ 10

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Note systemPipeR seems to have the correct code in their vignette:

dds <- DESeqDataSetFromMatrix(countData=as.matrix(countDFeByg[,rownames(coldata)]), colData = coldata, design = ~ assay + condition + assay:condition) 
dds <- DESeq(dds, test="LRT", reduced = ~ assay + condition) 
res <- DESeq2::results(dds) head(res[order(res$padj),],4)

ADD REPLY • link 8.5 years ago Michael Love 43k