Switching RNAseq count data to explanatory variable?
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jrager ▴ 10
@jrager-21786
Last seen 2.2 years ago

I am trying to figure out if there is a way to modify the design formula such that countData are used as the "effect" / explanatory variable, and another outcome is used as the dependent variable, while adjusting for covariates. So, I'm trying to switch:

countData ~ covariate1 + covariate2 + outcome

to

outcome ~ covariate1 + covariate2 + countData

Is this an option? Or should this kind of question represent something you could still ask about using the first formula listed?

Thank you for your time.

deseq2 • 221 views
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@mikelove
Last seen 3 days ago
United States

Short answer is, this isn’t something you can do with DESeq().

Both designs are a little bit strange because the outcome is not a fixed effect (like, control, treatment or batch).

How many samples do you have? What is the context here?

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Thank you for the response. This is a more general question for several ongoing projects - we have a large sample count (~500) from human tissue. We are trying to evaluate relationships between gene expression signatures and various epidemiology-based outcomes. For instance, does the expression level of a gene(s) associate with an outcome that occurs later-in-life.

I cannot provide further details through public forum - is that enough?

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I wouldn’t usually put an observation like the counts from an RNA-seq experiment (normalized or not) as an independent variable in a design. You may want to have a model that incorporates a latent factor driving the counts and another outcome.

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Great- this confirms our thoughts. Thank you for the feedback!

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Great- this confirms our thoughts. Thank you for the feedback!

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