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Use limma for correlation analysis of RNA-seqdata and continous trait

Hi, there I want to use voom function in limma package to analyze the correlations between expression and age(continous data). I am not sure whether it is suitable to use this package, which is often used for binary data. And the p values were far small compared to pearson correlation test. Please give me some advice. Thank you~ enter image description here "code"

enter image description here "voom-plot" enter image description here "result of top genes"

limma rna-seq
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updated by Gordon Smyth Gordon Smyth • written by Yibin • Yibin

1 answer

limma and voom are routinely used for continuous covariates like age, but you are not using voom correctly. The voom function is applied to counts rather than to cpm values.

Just use voom as documented, e.g., as in the workflows or in the limma User's Guide, for example:

design <- model.matrix(~age)
keep <- filterByExpr(dge, design)
dge <- dge[keep,,keep.lib.size=FALSE]
dge <- normLibSizes(dge)
v <- voom(dge, design)
fit <- lmFit(v, design)
fit <- eBayes(fit)
topTable(fit, coef="age")

limma is more powerful that computing correlation coefficients and will generally give smaller p-values for the most significant genes.

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written by Gordon Smyth Gordon Smyth

That is very kind of your advice. I corrected the mistake in voom() using. but the plot is not smooth , which seems indicate a bad fit of data? enter image description here

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written by Yibin • Yibin

There should not be a increasing trend of standard deviation with log2(count size) on the right hand side of the plot. I have never seen a voom plot of RNA-seq data that looks like this so it seems there is something wrong with your data or with the way you have input it to limma. Are you sure that your original data consists of RNA-seq read counts?

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written by Gordon Smyth Gordon Smyth

using a model with a continuous variable age, how do you interpret the logFC ?

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written by Picasa Picasa

The coefficient (logFC) is the log2 expression change for each unit change in the continuous covariate.

The interpretation follows the usual principles for interpreting coefficients in multiple regression equations.

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written by Gordon Smyth Gordon Smyth

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