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Question: DESeq2 output explanation
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gravatar for francesca.defilippis
3.0 years ago by
European Union
francesca.defilippis40 wrote:

Hello!

I have a question about the results of differential expression analysis in deseq2.

I did:

DEgenes=results(DEcd, contrast=c("clD", "V", "VG"))

and I got the table with the log2 fold change and the p values. But what I didn't understand is to which group the fold change refer to. So if I have a negative log2 fold change, it means that the gene is down-regulated, but in which of the 2 groups of samples? Where can I find this information?

 

Many thanks

Francesca

ADD COMMENTlink modified 3.0 years ago by Michael Love14k • written 3.0 years ago by francesca.defilippis40
1
gravatar for Michael Love
3.0 years ago by
Michael Love14k
United States
Michael Love14k wrote:

We describe the interpretation of results in a few places which you might find useful. Check the section "More information on results columns" in the software vignette:

vignette("DESeq2")

and also the "Building the results table" section of the workflow (this has a slower pace than the vignette and might be helpful to look over):

http://www.bioconductor.org/help/workflows/rnaseqGene/

 

ADD COMMENTlink written 3.0 years ago by Michael Love14k

Hi Michael,

thanks for your reply. I read both the vignette and the tutorial, but I still didn't find the information I'm looking for. 

If I extract the results for a specific contrast, let see A vs B, how can I know if the log2 fold changes are referred to A or B?

 

ADD REPLYlink written 3.0 years ago by francesca.defilippis40

A positive log2 fold change for a comparison of A vs B means that gene expression in A is larger in comparison to B.

Here's the section of the vignette

"For a particular gene, a log2 fold change of −1 for condition treated vs untreated means that the treatment induces a change in observed expression level of 2^−1 = 0.5 compared to the untreated condition."

Here's the section of the workflow

"The column log2FoldChange is the effect size estimate. It tells us how much the gene's expression seems to have changed due to treatment with dexamethasone in comparison to untreated samples. This value is reported on a logarithmic scale to base 2: for example, a log2 fold change of 1.5 means that the gene's expression is increased by a multiplicative factor of 2^1.5≈2.82." 

ADD REPLYlink modified 3.0 years ago • written 3.0 years ago by Michael Love14k

Hi Michal

I have a further question. I read I can use the rlog transformation and use those values for heatplots or pca. Do I need to use raw counts as input for rlog or do I need to normalize for library size before (diving the reads for each gene by the total reads of the sample)

thanks!

ADD REPLYlink written 3.0 years ago by francesca.defilippis40

Always check the documentation first, by typing the function name with a question mark in front: ?rlog

The help file tells you:

"This function transforms the count data to the log2 scale in a way which minimizes differences between samples for rows with small counts, and which normalizes with respect to library size."

The vignette (accessible via vignette("DESeq2")) section on transformations says:

"Both transformations produce transformed data on the log2 scale which has been normalized with respect to library size."

So the rlog function takes care of normalization for library size; you do not provide the rlog with normalized counts or non-integer values.

 

ADD REPLYlink written 3.0 years ago by Michael Love14k

Hi Michael,

I have some doubt in the explanation "A positive log2 fold change for a comparison of A vs B means that gene expression in A is larger in comparison to B.'

Does it have anything to do with alphabetical order of the condition? Like, I have "Reponse" and "Non-response" in my sampledata. How to know if the increased positive  fold change is for Response group or no-response group?

 

Note : I am using Deseq with phyloseq , http://joey711.github.io/phyloseq-extensions/DESeq2.html

Thanks,

Reeba

 

 

ADD REPLYlink modified 5 weeks ago • written 5 weeks ago by lucky.rp0

See this section of the vignette:

https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#note-on-factor-levels

There are three ways to know:

You can specify the reference level as in the above link.

You can specify the contrast explicitly when you call results() by using the 'contrast' argument.

Finally, when you print the DESeqResults table, it has the information printed at the top, see here:

https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis

...
log2 fold change (MLE): condition treated vs untreated 
​...

 

ADD REPLYlink written 5 weeks ago by Michael Love14k

Thanks Michael. That worked.

ADD REPLYlink written 5 weeks ago by lucky.rp0
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