logfoldStandard Error and wald-statistics in DESeq2 results
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deena ▴ 10
@deena-7415
Last seen 4.0 years ago
Germany

Hi,

I am new to statitics and RNAseq analysis, I am using DESeq2 for the anaylzing the RNAseq data. I would like to know what lfcSE(logfoldchangeStandard Error) and stat (wald ) columns tries to convey. For an example. how can I interpret the following:

log2FC:-1.4; lfcSE: 0.144; stat:-10.13

Kindly guide me.

deseq2 wald • 14k views
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@mikelove
Last seen 1 hour ago
United States

hi,

For information about the columns, read over the vignette:

vignette("DESeq2")

and the results man page:

?results

These both describe the columns.

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Dear Michael,

Thanks for your quick response. By going throught he vignette, I undersatnd that smaller  the lfcSe more significant the effect of fold change? Am I right? What does that a value in wald statistic columns conveys.

Kindly guide me
 

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The Wald statistic is the LFC divided by its standard error. This Wald statistic is used to calculate p-values (it is compared to a standard normal distribution) . So it's the ratio of LFC and SE which determines significance.
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Thank you so much. Now I understood clearly. Also is the lesser the lfcSE, more singificant the log2fold change?

If basemean is the mean of normalized rowcounts, how log2foldchange is calculated.?

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hi Deena,

You should look over our paper, if you want details about the method:

http://genomebiology.com/2014/15/12/550

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Hi Michael,

Sorry to bring up an old post. I was reading your paper and noticed it says that the Wald statistic is a Z-score. Is this also true for the LRT statistic? If not, is it possible to convert it to a Z-score using scale() or are there considerations I need to consider? I am asking because I plan to meta-analyze different datasets.

Thanks a lot!

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Only the Wald statistics are asymptotically Normal, not the LRT statistic. No, you should not need to adjust the Wald statistics.

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Thanks so much for the advice. Can I substitute 0 for NA for Wald test statistic? Appreciate the help!

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You can do what you want with these, thats up to you. They are 0 vs 0 genes.

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