[BioC] MetaArray - results - how to interpret

[Robert Scharpf]

Adrian,

You probably want poeRes$poe.  'poe' is short for "probability of expression" and is a transformed matrix of gene expression values (number of genes x number of samples).  Interpretation of under and over-expression depends on how the phenotype is defined.  According to the poe.mcmc helpfile, if normal is group 1 and is coded as '1' and group 2 is coded as '0', then positive values on the poe scale would be interpreted as the probability that the gene is over-expressed in group 2 relative to group 1.

POE for biologists:

http://www.biotechniques.com/multimedia/archive/00072/Mar03Scharpf_72034a.pdf

Since you have 3 datasets, one option is to run poe.mcmc on the three datasets independently and use ordinary measures of differential expression on the combined studies (I believe Shen et al., 2004 BMC Genomics describes this appoach).  fyi, other packages useful for analyzing multiple studies include the R packages RankProd (uses a rank product), XDE (a Bayesian multilevel model; Scharpf et al., 2009 JASA ), and the references therein.

Rob


On Oct 22, 2012, at 11:58 AM, Adrian Johnson <oriolebaltimore at gmail.com> wrote:

> Dear group,
> Pardon me for re-post.
> 
> I am writing to seek some help in interpreting MetaArray poe.mcmc results.
> 
> 
> After running poe.mcmc, the resulting results object is a complicated
> result ( I have biology training and minimal statistics).
> 
> I am trying to extract those genes that are consistently
> differentially expressed (over-expressed in condition 1 - metastasis)
> across all 3 datasets given in test data.
> 
> The result object poeRes has following names
>> names(poeRes)
> [1] "alpha"        "mug"          "kappaposg"    "kappanegg"    "sigmag"
> [6] "piposg"       "pinegg"       "mu"           "tausqinv"     "gamma"
> [11] "lambda"       "pil.pos.mean" "pil.pos.prec" "pil.neg.mean" "pil.neg.prec"
> [16] "kap.pos.rate" "kap.neg.rate" "poe"          "accept"
> 
> 
> How do I choose those genes that are over or under-expressed in
> metastatic tumors compared to normals.  I have 0 in accept.
> 
> I do not know which object (alpha, mug, kappa pos and neg, pi pos and
> neg, mu, tau, gamma, lambda etc..) has the result to pick from.
> 
> The vignette does not have additional details on interpretation.
> Could Drs. Choi or Ghosh, please help.
> 
> Thanks
> Adrian.
> 
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