I was not clear in my original post, I filtered out duplicate entrez gene
IDs taking only the one with the lowest p-value. I do appreciate your
advice, we still don't have a perfect solution and it is good to use
multiple tools to look at each dataset, always taking into consideration a
priori the questions that we really want answered, the cost/benefit ratio of
increased sensitivity/decreased specificity, etc. with various techniques.

I do thank you for your efforts at developing APM, it is a great tool and
one that I am sure to use, and publish with, in the future,

Mark

On Sun, May 4, 2008 at 5:46 AM, <lgautier@altern.org> wrote:

> > I have recently explored the use of alternative CDFs from affyprobeminer
> (APM) or a 36 array dataset derived using the Affy rat2302 chipset. I
> used
> > both the Affy cdf and the transcript-level affyprobeminer cdf. I
> preprocessed using RMA, filtered using an A/P filter, and statistically
> analyzed using an appropriate lme model followed by qvalue FDR
> correction.
> > I
> > set my FDR threshold at 5%. I eliminated duplicate genes by picking the
> one
> > with the lowest p-value.
> >
> > Using the Affy cdf, I got ~2000 sig. genes, which APM ~1000. If I choose
> only those EntrezGene identifiers present on both cdfs, my number sig.
> with
> > the APM cdf was ~1000 and there was a 90% overlap with the Affy sig.
> list.
> > My conclusion from the latter observation is that I am measuring largely
> the
> > same transcripts/genes with both CDFs.
> >
> > I was interested in the ~1000 genes which are annotated with the Affy
> CDF
> > but not the APM cdf. Following the logic behind APM, I would assume that
> these would be largely incorrectly annotated probesets or probesets that
> are
> > not really measuring any "real" transcript. This list should, then,
> consist
> > largely of random genes. To test this hypothesis, I used the Category
> package to test for over-representation of GO and KEGG categories in my
> various lists. What I found was a huge degree of overlap between: 1. the
> affy genes also annotated with APM, 2. the affy genes not annotated with
> APM, 3. the genes derived solely from APM.
> >
> > My conclusion from this latest observation is that APM is not annotating
> a
> > large number of genes/transcripts that are in fact real. Assuming that
> APM
> > is correctly throwing out some "junk" probesets, is it throwing out the
> baby
> > with the bathwater?
>
> Not necessarily.
>
> With Affymetrix mappings, there are a large number of cases from which
> there are multiple probesets for a "gene" (in the example below
> with hgu133a, that represents 20% of the probesets), and those probesets
> can be collapsed into one when remapping.
>
> Here is an example with few probesets (the example is mostly a copy-paste
> from one of the examples in the vignette "altcdfenvs"):
>
> geneSymbols <- c("IGKC", "IL8", "NENF", "TRIO")
>
> # Count the probesets associated with our geneSymbols
> library(hgu133a.db)
> sapply(geneSymbols,
>       function(x) length(mappedkeys(subset(hgu133aSYMBOL, Rkeys=x))))
> # This returns:
> #IGKC  IL8 NENF TRIO
> #  15   12    6    9
> # Which means that there are 9 probesets for TRIO, 6 for NENF, etc...
>
>
>
> # Now lets check what comes out of remapping
> library(altcdfenvs)
> library(biomaRt)
> mart <- useMart("ensembl",dataset="hsapiens_gene_ensembl")
>
>
> getSeq <- function(name) {
>  seq <- getSequence(id=name, type="hgnc_symbol",
>                     seqType="cdna", mart = mart)
>
>  targets <- seq$cdna
>  if (is.null(targets))
>    return(character(0))
>  names(targets) <- paste(seq$hgnc_symbol, 1:nrow(seq), sep="-")
>  return(targets)
> }
>
>
> targets <- unlist(lapply(geneSymbols,
>                         getSeq))
> m <- matchAffyProbes(hgu133aprobe, targets, "HG-U133A")
>
> hg <- toHypergraph(m)
>
>
> gn <- toGraphNEL(hg)
>
> library(RColorBrewer)
> col <- brewer.pal(length(geneSymbols)+1, "Set1")
> tColors <- rep(col[length(col)], length=numNodes(gn))
> names(tColors) <- nodes(gn)
> for (col_i in 1:(length(col)-1)) {
>  node_i <- grep(paste("^", geneSymbols[col_i],
>                       "-", sep=""),
>                       names(tColors))
>  tColors[node_i] <- col[col_i]
> }
>
>
> nAttrs <- list(fillcolor = tColors)
>
> library(Rgraphviz)
> plot(gn, "twopi", nodeAttrs=nAttrs)
>
> # the plot will show that the situation is not as simple for TRIO as
> # as it is with the other gene symbols.
>
>
>
>
> > I'd be interested to hear the thoughts and experiences of others. I've
> certainly run into occasions where Affy annotated probesets turn out to
> represent introns or something other than they purport to be, and I was
> hoping that APM would solve this problem, but I don't want to use it if
> it
> > means a massive loss of truly significant data.
>
> The situation is indeed not always clear... at the moment, I would not
> advice you to follow blindly any particular mapping, yet have alternative
> mappings as part of your routine analysis: depending on the cost of
> follow-up experiments, or downstream analysis, time should be spent
> looking at probesets in details.
>
>
> Hoping this helps,
>
>
> L.
>
>
>
>
>
> > Mark
> >
> >
> >
> > --
> > Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry
> > Indiana University School of Medicine
> >
> > 15032 Hunter Court, Westfield, IN 46074
> >
> > (317) 490-5129 Work, & Mobile & VoiceMail
> > (317) 663-0513 Home (no voice mail please)
> >
> > **************************************************************
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > Bioconductor@stat.math.ethz.ch
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> >
>
>
>
>
>


-- 
Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine

15032 Hunter Court, Westfield, IN 46074

(317) 490-5129 Work, & Mobile & VoiceMail
(317) 663-0513 Home (no voice mail please)

******************************************************************

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