the function "vsn" has not changed, it is identical to previous
releases. If there is demand, I can leave it there for quite a while.
"vsn2" optimizes the same likelihood function, but the implementation
different (and I hope better). There will be numerical differences,
they should not be consequential. If someone does discover substantial
differences, please tell me, this should in general not happen.
1. I did change the way the overall baseline (additive offset) of the
result is computed (see "Value" section of vsn2 man page), before,
was based on array number 1, now it is based on a mean of all arrays.
2. The likelihood function can sometimes be quite flat, and the
found by numerical optimization can vary. In these cases, different
normalization parameter values are almost as good as each other, i.e.
the result may numerically differ but not in a consequential manner.
3. In some (ill-determined) cases, the optimizer may run off into
space and converge in a meaningless local maximum... Some quality
control and sanity check on the result (function "meanSdPlot") is
Hope this helps, please let me know,
How do the scatterplots of old versus new glog-ratios look like?
> Dear all,
> I want to normalize a matrix of gene expression values G using VSN.
> VSN 1.x I did
> > vsnRes <- vsn(G)
> > Gvsn_old <- exprs(vsnRes) / log(2)
> This still works with vsn 2.2 (besides deprecated warnings) and
> exactly the same values as the older version of vsn does. Now, with
> 2.2 I type
> > vsnFit <- vsn2(G)
> > Gvsn_new <- predict(vsnFit, G)
> In both cases the normalization works. However, I noticed that the
> values are slightly different. It is not worth worrying about it,
> would appreciate if someone could explain me the reason for that -
> to satisfy my curiosity.
> > Gvsn_new[1:5,1:3] - Gvsn_old[1:5,1:3]
> 01N 01T 03N
> [1,] 0.1387763 -0.15328649 -0.011482728
> [2,] 0.1398214 -0.09982814 -0.005172216
> [3,] 0.1394453 -0.04704412 0.128297523
> [4,] 0.1389546 -0.08527670 0.088480265
> [5,] 0.1396382 -0.03739234 0.144517027
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Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber