Dear List,

Normally for Illumina arrays, instead of the functions given based in 
the limma user guide (e.g. neqc, read.ilmn etc.), I use:

    * read.delim - to load probe profile data and sample table control
      data respectively
    * perform bg correction using the negative control probes from the
      sample table control
    * filter data based on _"detection scores"_
    * normalise data using the _"vsn2"_ function


However, as I have just realised that these can be used I have some queries:

   1. Will there be much difference between the quantile normalisation
      in the neqc function (as compared to vsn2 ?)
   2. How does one interpret the boxplots for the various controls
      (apart from x$genes$Status=="regular")?
          * as the median/mean vary a lot
          * much more for my samples (than the example shown in the user
            guide)
   3. When filtering: based on the help of read.ilmn
          * The "Detection" column appears to be detection p-value by
            default
          * What does one do if the output is different from the
            GenomeStudio and it gives a "Detection Score" instead??
                o Would: expressed <- apply(y$other$Detection < 0.05,1,any)
                      + change to: expressed <- apply(y$other$Detection
                         > 0.95,1,any)
   4. Also, I do not fully understand the estimation of probes expressed
      using the propexpr function
          * one of my samples A7 shows 0.0 (I see that the housekeeping
            gene intensity for this is ~ 200 whereas for others its
            1000+), its a similar case for samples A11 and A12
                o propexpr(x)
                o             A1           A2             A7          
                  A8             A3            A4          A11          A12
                  0.3380243 0.4066500 0.0000000 0.4232871 0.3131936
                  0.3819055 0.1934197 0.2036340
                              A5            A6            A9          A10
                  0.3363844 0.3476216 0.3445201 0.3834617


sessionInfo()
R version 2.13.0 (2011-04-13)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C
  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8
  [5] LC_MONETARY=C              LC_MESSAGES=en_GB.UTF-8
  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C
  [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] gdata_2.8.2 limma_3.8.2

loaded via a namespace (and not attached):
[1] gtools_2.6.2 tools_2.13.0

Many Thanks,
Natasha



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