Dear Simon,

My apologies for not being concise in explaining the problem. I am pretty
new to the list and I can see that it can be frustrating to try to help
someone that is not giving all the details you need to do so. I appreciate
your apology, since some of the initial responses to my question had a very
unfortunate choice of words. Going through the suggestions of the thread
has been very helpful. So here is a more thorough description of what I
did, as well as some observations made after taking into account
suggestions:

My experiment has 2 conditions, with 9 replicates of each being hybridized
to independent micro arrays, 5 of which were also sequenced by RNA-seq. The
comparisons I am making are between those RNA samples that were both
hybridized and sequenced. I have also an independent experiment in which I
have looked at the same samples by qPCR in several genes, so I have a
decent set of positive controls.

RNA-seq was obtained after polyA selection, with an average of 70 million
pairs of 100bp reads per sample, each sample having very similar number of
reads. I did my alignments using RUM (
http://www.ncbi.nlm.nih.gov/pubmed/21775302?dopt=Abstract). I have ~65
million unique pairs, which cover about 10% of the genome, and I am using
for DE analysis using both edgeR and DESeq.

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.

Here are the DESeq size factors
CC3                                 1.1395037
CC5                                 1.2704539
CC6                                 0.7973817
CC7                                 1.1593902
CC8                                 0.9147930
FC3                                 0.9944328
FC5                                 0.8701476
FC6                                 0.9405719
FC7                                 0.8326854
FC8                                 1.1706489

Now, using BH instead of locfdr shrinks the DE list produced by the
microarray considerably and then they are more similar to those produced by
DESeq, so the choice for multiple testing correction does have a big effect
The locfdr procedure is better able to detect true positives. It can be
something particular about my data set, or maybe it is a good idea to use
locfdr always, not sure.
even then, my RNASeq data has an obvious bias towards up-regulation, as it
detects almost no down-regulated genes. I did filter low counts  as follows:

>rs <- rowSums ( counts ( cds))
>use <- (rs > 10)
>cdsFilt <- cds[ use, ]

 but my filter is not too stringent and shouldn't really have a big effect

thanks very much for all your help and suggestions.
Lucia




On Mon, May 20, 2013 at 7:15 PM, Simon Anders <anders@embl.de> wrote:

> Dear Lucia and list
>
> On second reading, I noticed that my previous post sounded quite
> aggressive, which was not my intention. Sorry. I shouldn't write e-mails
> that late at night.
>
> Anyway: We had a lot of discussion on this list and others recently about
> how to correctly obtain a count table for differential expression analysis
> from aligned RNA-Seq reads. From these discussions, it has become clear
> that this is a task with many more pitfalls than one might expect at first.
> In microarray analysis, there is no need to do this, and so read counting
> is a likely culprit when such discrepancies are noted. This is why exact
> details on the procedure are so important.
>
>   Simon
>



-- 
Lucia Peixoto PhD
Postdoctoral Research Fellow
Laboratory of Dr. Ted Abel
Department of Biology
School of Arts and Sciences
University of Pennsylvania

"Think boldly, don't be afraid of making mistakes, don't miss small
details, keep your eyes open, and be modest in everything except your
aims."
Albert Szent-Gyorgyi

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