
Dear list,

Because I like the undelying idea, I have began using the re-mapped CDF files provided by the MBNI. However, triggered by a remark made by Dr MacDonald "... note that there are some downsides to using these cdfs, mainly that the standard errors of your estimates will be highly variable, since the 
probesets for these cdfs are quite variable in size (unlike the stock affy chip, where the vast majority have 11 probes)" from this thread http://article.gmane.org/gmane.science.biology.informatics.conductor/11282, I determined the number of probes that map to a probe set for both default Affymetrix CDF file and Entrez-gene based re-mapped CDF file for the Mouse430_2 array.

Outcomes:
library(mouse4302probe)
a <- as.data.frame(mouse4302probe)
b <- as.factor(a[,4])
table(table(b))

    8     9    10    11    20    21 
    1     5    20 45032    40     3 



library(mm430mmentrezgprobe)
a <- as.data.frame(mm430mmentrezgprobe)
b <- as.factor(a[,4])
table(table(b))


   3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
 230  213  219  283  419  663 1265 1741 5092  284  261  234  193  205  206  255 

  19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34 
 412  569  639 1249  121   98   96   91   72   89  113  122  173  166  279   38 

  35   36   37   38   39   40   41   42   43   44   45   46   47   48   49   50 
  39   30   32   36   20   35   41   46   40   50   18   15   10    6    8    9 

  51   52   53   54   55   56   57   58   60   61   62   63   64   65   66   67 
   9   14   13   12   18    6    6    1    4    3    4    2    2    2    1    1 

  68   70   71   73   74   75   76   80   89 
   3    3    3    3    2    2    1    1    1 


This indeed confirms Dr MacDonald's observations, which I would like to address in more detail...
However, as a biologists with limited experience with statistics & R/BioC, I do have some (practical) questions:

- How can I extract the name of (lets's say) the 230 probesets that consists of 3 probes?
- When applying RMA, probe set expression levels are summerized according to Median Polish. What is the minimum number of probes (x) that have to be summerized to obtain a robust average using Median Polish? In other words, probe sets consisting of less than x probes are better not dealt with?
- Can the standard error of the estimated expression according to RMA be extracted from an eSet? If so, how could this be propagated into the statistical analysis (eg. limma) that is used to identify DEGs? 

FYI: as a biologist I have concluded that re-mapping improved my analyses: when comparing the lists of most regulated genes based on analyses with Affy or re-mapped CDF, the latter identified genes that were missing in the Affy top-list, altough those genes were expected to present based on prior knowledge. However, this only applies to the top-regulated genes (that are expressed at relatively high levels), I haven't carefully evaluated the complete lists yet.  
   
Guido
 
------------------------------------------------
Guido Hooiveld, PhD
Nutrition, Metabolism & Genomics Group
Division of Human Nutrition
Wageningen University
Biotechnion, Bomenweg 2
NL-6703 HD Wageningen
the Netherlands
 
tel: (+)31 317 485788
fax: (+)31 317 483342
 
internet:   http://nutrigene.4t.com
email:      guido.hooiveld@wur.nl


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