Variance in DESeq is the sum of shot noise and raw variance. The raw variance is the smooth function of estimated read counts and experimental condition. In the corresponding paper, it is mentioned that this function aim to pool the genes with the same strength to estimate the variance(due to the more probable few number of replicates).

I am not sure if I have understand it correctly. Does this means that DESeq groups n genes (i1, i2, ...in) from a common condition with similar expression levels and then calculate the variance for them? So, instead of calculating the variance for a specific gene, it calculates the variance for a group of similar gene and associate that value for all of them?

And how does it decide which genes have similar strength (? similar expression levels)?

Then can we conclude that DESeq does not calculate the gene-specific variance but group of genes-specific variance?

I am not sure if I have understand it correctly. Does this means that DESeq groups n genes (i1, i2, ...in) from a common condition with similar expression levels and then calculate the variance for them? So, instead of calculating the variance for a specific gene, it calculates the variance for a group of similar gene and associate that value for all of them?

And how does it decide which genes have similar strength (? similar expression levels)?

Then can we conclude that DESeq does not calculate the gene-specific variance but group of genes-specific variance?

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