[BioC] LPE error caused by gcRMA [Error invar.M.adap[i] <- ifelse(!is.na(var.M.adap[i - 1]), mean(var.M.adap[i + : replacement has length zero]
zwu at stat.brown.edu
Wed Nov 12 00:42:38 CET 2008
Dear gcrma users,
The reason for LPE error following gcrma is because gcrma shrinks some
values to the same lower bound, if it appears to be indistinguishable
from background distribution. In earlier versions of gcrma, the
GSB.adjust step was performed on all probes after background adjustment,
and since GSB.adjsut is slightly different array to array, the final
values for each gene will not be identical even if the gene appears to
be not expressed.
In recent versions we stopped performing GSB.adjust for those values
that appear to be indistinguishable from background (thus have no
specific binding), following the suggestion in the paper by Lim et al
(omparative analysis of microarray normalization procedures: effects on
reverse engineering gene networks, Bioinformatics 2007 23(13)).
I do not think it is an error or something that should be avoided to
have identical values for some genes across all samples: if these genes
or targets appear to be well within background in all samples, then
probably the best thing to do is to acknowledge that their specific
signal is 0 in all samples. In gcrma we used a small lower bound instead
of 0 to avoid -inf in log transformation.
Maybe LPE can have a check on 0 sample variance it has trouble
handling genes with 0 variance right now. Or, possibly a prior on
variance distribution can be introduced?
Wolfgang Huber huber at ebi.ac.uk
Tue Nov 4 11:47:28 CET 2008
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var.M.adap[i] <- ifelse(!is.na(var.M.adap[i - 1]), mean(var.M.adap[i + :
replacement has length zero]
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I agree a lot with you that this is troublesome... but I would be
interested in discussing
(i) do you (and others) consider this an "error", or rather "bad
behaviour" or "poor performance"?
(ii) and is it gcrma, or LPE that errs or poorly performs?
(iii) and are any of the maintainers of these packages interested in
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