Hi Lucia,

I know this post is a little bit redundant to Simons Post but I would also
think that it's worth checking logFC and mean Expression Values of the
Microarray DE transcripts that have not been flagged as DE by RNA-Seq and
maybe as Simon stated, technical noise is just too large to overcome the
effect size of your treatment. There is a nice Paper
http://bioinformatics.oxfordjournals.org/content/27/13/i383.full that
investigates the relationship of transcript coverage (or number of reads
per transcript) and measurement precision. As far as I remember the authors
are rather pessimistic regarding measurement precision of low expressed
transcripts using nowadays common sequencing depth. For instance they could
only reliably (with 20% or less error) detect 41% of transcripts in mammary
epithelial cells using a whole SOLID Flowcell (331 Mreads). Also there is
non neglectable noise occuring during library preparation and finally low
amounts of RNA used for library prep could introduce a large bias.

Hope that helped a little bit

Moritz
2013/5/23 Simon Anders <anders@embl.de>

> Hi Lucia
>
>
> On 21/05/13 23:19, Lucia Peixoto wrote:
>
>> In terms of my count table, the counts are generated by RUM as described
>> in the paper, I just parse them out into a table format using custom
>> scripts. I do not have much experience with this, but in principle I do
>> not see anything wrong with the way the counts are generated, neither do
>> the developers which are down the hall. It produces very high
>> correlation to Ct values by qPCR after logRPKM transformation based on
>> the data I have seen from them.
>>
>
> I did not know that RUM produces a count table, but if this works well, it
> sounds like a useful feature. However, as I pointed out in my reply to
> Thomas, counting for expression strength estimation and counting for
> differential testing are slightly different tasks, and it would be
> important to know what the RUM developers aimed for when they decided on a
> strategy to deal with ambiguously mapped reads. If you ask them, let us
> know what they say, please.
>
> To get back to the original issue: The sensitivity of RNA-Seq obviously
> depends on read depth. If you have few reads, Poisson noise becomes strong,
> and such an RNA-Seq experiment will be less precise than a microarray
> assay, especially if overall expression strength of genes is all you care
> about.
>
> The break-even point where RNA-Seq becomes better than microarrays is
> probably somewhere at 1 to 10 million reads. (This is a rough guess, the
> papers mentioned in the thread may contain proper figures.)
>
> You seem to have more than that. But you also mentioned that you have an
> unusually high number of PCR duplicates, which means that the information
> content of your reads is reduced. Also, check the column sums of your count
> table: Which fraction of your reads actually gets assigned to a gene? It's
> rather common for things to go wrong here.
>
>   Simon
>
>
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