Hi All,

Thanks in advance for the help! I am still new to edgeR and welcome for any suggestions!

I am trying to figure out which method is better off to analyze my data. I am interesting in translational change before vs. after stress in yeast(s. cerevisiae). I carried out polysome profiles before and after stress, then RNA-seq of collected fractions of whole polysome profiles (total 9 fractions per polysome profile). During RNA sample extraction, I spike-in equal amount of RNA (from S.pombe) for later normalization. I used the normalized counts (based on spike-in counts), summed up the reads of polysome fractions(poly) and reads of the whole polysome profile(total), and tried to use the ratio (poly/total) to access the translation change. I have 2 biological replicates and 2 conditions, before and after stress. I have used the ratio number in limma and got reasonable results. Meanwhile, I also get suggestions to use edgeR with summed poly-counts as input and total counts as offsets. But, when I run the edgeR, it crushed after a certain code. Here is my code for running edgeR:

> x = DGEList(counts = inp) # inp contains normalized reads of summed polysome fractions

> x$offest = off # off is the sum of total reads, have the same dimension as inp

> x = estimateCommonDisp(x)

>x = estimateGLMTrendedDisp(x, design)

The R is crushed on this step.

Any suggestions and thoughts with my codes? I am also wondering what people think about using limma vs. edgeR for this analysis.

Thanks,

Elisha

edgeRhas no concept of "normalized counts", you must supply the raw counts.P.S.Donotadd extra details with the "Add answer" button. Either add a comment or edit your original post.Hi Aaron,

Thanks for the comments, I have edited the original post. Let me know if anything is unclear. And yes, I understand that edgeR only takes raw counts, but under my case, I am worried that using raw counts will cause biases due to tech variations among fractions reads. For example, one fraction of polysomes has raw read at 5 and the other one is 50, but after normalization, they are actually 3 vs. 4. If I sum up the raw counts, the one have higher counts will contribute more than the others, and I will get mislead by one particular fraction. Let me know if you think I shouldn't use edgeR or the normalized counts and welcome any thoughts and suggestions. Thanks!

From reading

"summed up the reads of polysome fractions(poly) and reads of the whole polysome profile(total)", I still don't understand the difference between the poly and total reads. What are the fractions? Why do you need to collect 9 of them? If the 9 fractions are different, why do you sum them together? What's the difference between the summed fractions and the whole polysome profile?Polysome profile is a technique used to study the mRNAs associated with ribosomes. The polysome fraction indicates the amount of mRNAs that is associated with ribosomes, and the total (whole polysome profiles) represents the total mRNAs amount (no matter they are free or engaged with ribosomes). It's common to use the ratio as a proximate estimate of a certain transcript's translation status in molecular biology. We already did a spline test, and I'd like to use the sum up reads as an alternative way to analyze my data.

It seems like something is wrong with the offset setting, are you familiar with offset?

I was one of the authors of the

`scaleOffset`

function, so yes, I would say I am familiar with offsets.