How to correct for age, sex, etc. from an RNA-seq data in DESeq2.
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treebig44 ▴ 40
@bffcbc5f
Last seen 2.2 years ago
United States of America

Hi. I am new to RNA-seq data expression analysis and to DESeq2. I have a RNA-seq dataset, and in its annotation data is the column of 'gender', 'age', etc. apart from the column of interest, which is 'condition'.

I wanted to ask that, how can I correct for the columns apart from the 'condition' column. i.e. for 'gender', 'age', etc.

Please excuse me for being a newbie. Thanks.

DESeq2 RNASeq RNASeqData • 9.2k views
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~gender+whatever+you+want+to+correct+for, see the section on design formula in the vignette.

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I see. Yeah, I took a look at the vignette, and it says the same thing you mentioned in addition to 'condition' being the last entry in design.

~gender+whatever+i+want+to+correct+for+condition

So, is it something like that?

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The order does not matter if you specify the contrast. It's smart to always specify the contrast, if only so you know what you are comparing to what.

Also consider carefully the possible ramifications of making the age data into factors.

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Oh okay. I will specify the contrast in that case as well.

possible ramifications of making the age data into factors

So, if there are ramifications, does that mean, that I cannot include 'age' into the design? Or, is there a way to include it in the design without any problems? So far, I was thinking of doing this:

design = ~ gender + age + condition

where gender has values 'M' and 'F', and age varies continuously from 18 to 80.

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@mikelove
Last seen 1 day ago
United States

Check out the vignette first.

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I found the following in the vignette:

Note: If there is unwanted variation present in the data (e.g. batch effects) it is always recommend to correct for this, which can be accommodated in DESeq2 by including in the design any known batch variables or by using functions/packages such as svaseq in sva (Leek 2014) or the RUV functions in RUVSeq (Risso et al. 2014) to estimate variables that capture the unwanted variation. In addition, the ashr developers have a specific method for accounting for unwanted variation in combination with ashr (Gerard and Stephens 2017)

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