Hi,
First off all, I want to thank you for this nice forum in general. I already learned a lot!
I´ve got a question concerning differing readnumbers between biological replicates. We want to analyse differential expression in two different tissues from maize.
First we started with one replicate only, to test the whole procedure and to have a first glance on potential results.
After a evaluation of this first dataset, we decided to go on and sequenced additional replicates afterwards.
so, now we have one replicate with around 70 million reads and another replicate with around 30 million reads. I did some checks and we are quite sure that 30 million reads are sufficient to do the anylsis. So would you be concerned about the different read numbers? Or would you try to sample the first one down?
As far as I understood, the RPKM value would take this into account. But I am not sure if it is also ok for the EdgeR analysis, since it uses total counts, right?
Sorry if this question is redundant or stupid; but I am really a Greenhorn
First off all, I want to thank you for this nice forum in general. I already learned a lot!
I´ve got a question concerning differing readnumbers between biological replicates. We want to analyse differential expression in two different tissues from maize.
First we started with one replicate only, to test the whole procedure and to have a first glance on potential results.
After a evaluation of this first dataset, we decided to go on and sequenced additional replicates afterwards.
so, now we have one replicate with around 70 million reads and another replicate with around 30 million reads. I did some checks and we are quite sure that 30 million reads are sufficient to do the anylsis. So would you be concerned about the different read numbers? Or would you try to sample the first one down?
As far as I understood, the RPKM value would take this into account. But I am not sure if it is also ok for the EdgeR analysis, since it uses total counts, right?
Sorry if this question is redundant or stupid; but I am really a Greenhorn
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