
Hello,

This is the first time I have used beadarray . I am using it  for the 
analysis of an illumina 27 methylation array and I am having a few issues I 
hope that you could help me with.

1.      The first time I tried to load the methylation data, I didn't write 
in singleChannel=FALSE. It happily read in my 12 arrays with no problems 
what so ever. I tried plotting a few things which worked fine. Seeing my 
mistake, I then went back to reload my data with the red channel 
(singleChannel=FALSE) and got the following error.

 > BLData = readIllumina(arrayNames = targets$ChipBarcode, textType=".csv", 
targets=targets, backgroundMethod="none", singleChannel=FALSE)
Found 12 arrays
Reading pixels of ./4408100017_A_Grn.tif
Calculating background
Sharpening Image
Calculating foregound
Background correcting: method = none
Reading pixels of ./4408100017_A_Red.tif
Calculating background

*** caught segfault ***
address (nil), cause 'memory not mapped'

Traceback:
1: .C("readBeadImage", as.character(tifFiles2[i]), as.double(RedX[ord]), 
as.double(RedY[ord]), as.integer(numBeads), foreGround = double(length = 
numBeads), backGround = double(length = numBeads), 
as.integer(backgroundSize), as.integer(manip), as.integer(fground), PACKAGE 
= "beadarray")
2: readIllumina(arrayNames = targets$ChipBarcode, textType = ".csv", 
targets = targets, backgroundMethod = "none", singleChannel = FALSE)

session info Below.


So I ended up loading in the data with images=FALSE, which worked, but I 
would like to be able to look at the background. Is there a way around this 
issue?

2. When I plotted the outliers (bar chart) I got an astounding 25% for the 
majority of my 12 samples, both in the red and green channel (unlogged 
data). In addition, 3 of the samples had a peak of intensity at 5 in the 
green channel, leading me to believe that I have some real quality control 
issues with my samples. Any opinions/suggestions on these results would be 
most welcome.

3. Is it correct that readBeadSummaryData, is not set up for two colour 
arrays such as the methylation arrays? So the only way to look at 
methylation data is through reading in BLData?

4. Some of my samples seem to have a large number of targets which have a p 
value detection rate above 0.05 (beadstudio output). Illumina have 
indicated that they disregard these. If I can not read in the bead summary 
data from bead studio, I am assuming that these detection p values can not 
be taken into account in the analysis? Or are there other methods that 
remove/down grade these less than optimal probes (most removed as outliers?).

5. Has any one had any experience with normalisation of the methylation 
arrays? I know that many of the usual array methods are out of the question 
due to the assumption that most probes on the array will not be 
differentially expressed is invalid. I read in a bioconductor list someone 
suggesting quantile normalisation? I would really appreciate any feeback 
from people who have tried this or other methods, especially if they have 
verified their methylation results.


Thanks for any help/advice you may be able to give.

Cheers
Katrina





 > sessionInfo() below
R version 2.7.0 (2008-04-22)
x86_64-redhat-linux-gnu

locale:
LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C

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

other attached packages:
[1] beadarray_1.8.0 affy_1.18.2 preprocessCore_1.2.1
[4] affyio_1.8.0 geneplotter_1.18.0 annotate_1.18.0
[7] xtable_1.5-2 AnnotationDbi_1.2.2 RSQLite_0.6-9
[10] DBI_0.2-4 lattice_0.17-6 Biobase_2.0.1
[13] limma_2.14.5

loaded via a namespace (and not attached):
[1] grid_2.7.0 KernSmooth_2.22-22 RColorBrewer_1.0-2



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