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No matter how you normalize, all genes will show huge differences. And you will have no way to figure out whether you might have seen as huge differences has you sequenced two eggs from the same developmental stage (this is well possible: maybe mollusc eggs differ a lot from each other, or maybe your wet-lab protocol is unstable). This is, of course, just another example while doing an experiment without at least duplicates is simply bad science and a waste of time and money (and by now, no decent journal will accept such studies any more, I hope), but I am repeating, what I already said often in previous threads.
Sorry for the pessimism, but quite frankly, I doubt that it's worth putting much more effort into this analysis. What exactly had you hoped to find?
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Originally posted by Simon Anders View PostNo matter how you normalize, all genes will show huge differences. And you will have no way to figure out whether you might have seen as huge differences has you sequenced two eggs from the same developmental stage (this is well possible: maybe mollusc eggs differ a lot from each other, or maybe your wet-lab protocol is unstable). This is, of course, just another example while doing an experiment without at least duplicates is simply bad science and a waste of time and money (and by now, no decent journal will accept such studies any more, I hope), but I am repeating, what I already said often in previous threads.
Sorry for the pessimism, but quite frankly, I doubt that it's worth putting much more effort into this analysis. What exactly had you hoped to find?
But I also hoped to find few genes differentially expressed, without a p-values and without statistical power, just to find transcripts (maybe 5-10) which show a huge difference between the two conditions. Just a first step to be validated, no more. To do this I need an inter-library normalization.
Considering the data, do you think that this objective is too ambitious?
Marianna
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I work on whole-transcriptome also. I believe there is a lot of alternatives for normalizing this kind of data. I have tried edgeR but it did not produce meaningfull normalization by human eyes. What I do instead is based on FPKM/RPKM counts e.g. via Cufflinks. E.g. select 5-10.000 genes that are expressed in all samples and then normalize by these. If samples are of different origin this approach may prove insufficient.
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Hi Puggie,
thanks for your reply.
Originally posted by puggie View PostE.g. select 5-10.000 genes that are expressed in all samples and then normalize by these
Originally posted by puggie View PostIf samples are of different origin this approach may prove insufficient.
Anyway RPKM normalization makes sense only if you don't need to compare different samples.
Marianna
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