RNA-seq with DESeq2 shrinkage estimation
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@6fb6fae8
Last seen 11 weeks ago
Taiwan

Regarding the output of the DESeq2 package, I am a bit confused. When I use the deseq() function for estimation and the lfcShrink() function for shrinkage estimation, I understand that the T-ratio is obtained from the ratio of the estimated coefficient to its standard error, and this T-ratio is then used to calculate the P-value and the adjusted P-value after multiple testing correction. However, why doesn't the lfcShrink() function recalculate the T-ratio, P-value, and the adjusted P-value, even though the estimated coefficients and standard errors have been altered by shrinkage?

Regression DESeq2 rnaseqGene • 436 views
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@james-w-macdonald-5106
Last seen 14 hours ago
United States

There's no deseq function. I assume you mean DESeq? But anyway, lfcShrink isn't meant for inference, it's meant for visualization (see the vignette), so you should not expect it to affect any of the statistics.

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Yes, you are correct, I meant to describe the DESeq() function. But I want to know why lfcShrink does not imply statistical inference? As I know, this should be a robust estimation method used to shrink the log2 fold change of low express genes, making it closer to the prior expected value and correcting its standard error to make the estimation more robust. So, why doesn't this imply statistical inference?

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You should read the apeglm paper. I wasn't completely correct - the shrinkage methods are also meant to provide better estimates of the logFC, so if you want to rank your significant genes by the logFC, you can shrink them first to preclude low expressing genes with inflated logFC values from polluting your top genes. But it's not used for inference, which is why the t-statistics and p-values aren't affected.

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