Is it appropriate to apply linear mixed models to Voom transformed data?
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pl23 • 0
@4b83ad99
Last seen 7 months ago
Canada

Hello,

I have some gene expression data for different tissue types with multiple replicates. My species has undergone a historical duplication event and and I am looking to compare the genome copies to identify which one is more highly expressed. Due to the nature of the count data, most parametric models don't seem to be a good fit for my problem. I was looking into the voom paper and am wondering if it is statistically sound to apply linear mixed models to the voom transformed data using external R packages such as lme4. I want to add the tissue types and gene groups as factors and treat the locus (for gene duplicates) as a random effect. I was unsure if limma could accommodate this type of analysis since most examples construct factors for samples, and not groups of genes.

Any feedback is much appreciated. Thanks!

stats r RNASeqData limma voom • 970 views
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@gordon-smyth
Last seen 1 hour ago
WEHI, Melbourne, Australia

limma-voom computes logcpm values with associated precision weights. You could conduct downstream genewise analyses of the logcpm values using any statistical method that is able to accommodate the precision weights.

Comparing groups of genes is another matter. I do not know of any statistical methods that are able to compare groups of genes because genes are not statistically independent and the nature of the dependence is unknown and almost impossible to estimate. Intergene correlation is extremely common and not just for gene duplicates. Perhaps you could build a linear mixed model for the intergene dependence structure, but that would be very challenging and I haven't seen it done.

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Thank very much for your feedback! I didn't realize that genes can be correlated to such an extent, I think I am going to have to rethink my problem.

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Genes will be positive correlated if they are activated by the same molecular pathways. They might by negatively correlated if they activated by mutually exclusive pathways or if they are involved in the same pathway but with one as a suppressor of the other.

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Interesting! This is really helpful to know - I am not a biologist so I was unaware of these dependencies. Thank you.

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