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Old 04-25-2012, 10:09 AM   #1
ccard28
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Location: Rhode Island

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Default qPCR Validation of Single Sample

I have a question regarding qPCR validation of some RNA-Seq data I am working with. We are working with a population that has never been characterized using RNA-Seq and due to sample availability our first run is a single sample of a pool RNA from multiple individuals. Due to it only being a single sample I am not sure how to report qPCR validation data. I am running 10 or so genes that span low to high FPKM from our expression data and should get a consistent pattern in terms of expression levels on qPCR and then will be sequencing the qPCR amplicons. However due to it only being on sample I see no way to use a housekeeping gene. Is it possible to somehow just take Ct values and report them in a manner that shows consistency with FPKM values for those genes from the seq data? Our next objective is comparing two different samples, I understand we could use housekeeping genes there but I am a little stuck on how to report this initial single sample global characterization study. Any help would be appreciated.
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Old 04-25-2012, 11:26 AM   #2
SeqByChoice
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Hi ccard28

So it seems your question carries the answer already.
Basically let me remind you: housekeeping genes have one sole purpose: to allow relative expression comparison between samples at a globla scale. The only thing signifying a HK gene is that it is (presumably) not differentially expressed in the compared samples.
So back to your one sample: I guess you want to confirm that several individual amplicons have a different level of representation in the sample. So to have an reference point to compare against you can choose any amplicon (since it doesnt have to stable across samples), or simply use the geometric mean of all the amplicons tested.
And yes you could also simply present the Cts, only that is graphically not so intuitive because it doesnt really illuminate fold differences.
A little hint for when you start comparing across samples: if you dont know any HK genes yet (?) the simplest way is to compare the seq data across all samples, searching for a statistically NOT diff expressed target. If its stable across your sample population, it is by definition a HK gene for these samples. -Off cause you need to include enough samples to have statiscial relevance, but guess you would anyway.
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