[BioC] transformations and expressionQCPipeline

Thu Feb 24 22:26:19 CET 2011

Hi Mike et al.,
  I still have not had any response to my question about transformations in beadarray that yield non-negative values - does the current version of beadarray still have this capability? Or are the neagtive values just ignored in the bead summary?

To make things easier, I have been trying to use the expressionQCPipeline function of bead array, but it keeps giving me the message that plots already exist and are skipped! I've tried different chips and keep getting the same message! Please see below. Any suggestion why this is happening? I cannot find any plots that 'already exist'.

Many thanks, Ina

> expressionQCPipeline(BLData,transFun=logGreenChannelTransform,qcDir="QC.Dir[iChip]",plotType=".jpeg",horizontal=TRUE,controlProfile=NULL,
+ overWrite=FALSE,nSegments=9,outlierFun=illuminaOutlierMethod,tagsToDetect=list(housekeeping="housekeeping",Biotin="phage_lambda_genome",
+ Hybridisation="phage_lambda_genome:high"),zlim=c(5,7),positiveControlTags=c("housekeeping","phage_lambda_genome"),
+ hybridisationTags=c("phage_lambda_genome:low","phage_lambda_genome:med","phage_lambda_genome:high"),negativeTag="permuted_negative",
+ boxplotFun=logGreenChannelTransform,imageplotFun=logGreenChannelTransform)
Making per-array plots for section 1 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Loading required package: hwriter
Making per-array plots for section 2 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 3 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 4 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 5 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 6 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 7 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 8 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 9 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 10 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 11 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Making per-array plots for section 12 
Positive controls
Positive control plot exists. Skipping to next plot
Hyb controls
Hybridisation control plot exists. Skipping to next plot
Outliers
Outlier plot exists. Skipping to next plot
imageplot
Positive control plot exists. Skipping to next plot
Creating probe metrics
Calculating outlier Metrics
35901  outliers found on the section
33408  outliers found on the section
30521  outliers found on the section
32013  outliers found on the section
32032  outliers found on the section
30939  outliers found on the section
32048  outliers found on the section
30359  outliers found on the section
32026  outliers found on the section
28328  outliers found on the section
28367  outliers found on the section
27180  outliers found on the section
> sessionInfo()
R version 2.12.1 (2010-12-16)
Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] hwriter_1.3          geneplotter_1.28.0   lattice_0.19-13     
 [4] annotate_1.28.0      AnnotationDbi_1.12.0 RColorBrewer_1.0-2  
 [7] gplots_2.8.0         caTools_1.11         bitops_1.0-4.1      
[10] gdata_2.8.1          gtools_2.6.2         beadarray_2.0.4     
[13] Biobase_2.10.0      

loaded via a namespace (and not attached):
[1] DBI_0.2-5     limma_3.6.9   RSQLite_0.9-4 tools_2.12.1  xtable_1.5-6 
> 

----- Original Message -----
From: "Ina Hoeschele" <inah at vbi.vt.edu>
To: "Mike Smith" <grimbough at gmail.com>
Cc: bioconductor at stat.math.ethz.ch
Sent: Sunday, February 20, 2011 9:20:07 PM
Subject: Re: [BioC] problems with readIllumina() for many BeadChips

Hi Mike et al.,
   first thanks for the help, I can now read and process the bead-level data from multiple (HumanHT-12 v4) Expression BeadChips just fine. Unfortunately I've run into the next hurdle, where I have a hard time finding information in the beadarray vignette or manual. The background corrected intensities in the txt files (expectedly) contain some negative values, but I can only find the log2 transformation. When I worked with the old version of beadarray a couple of years ago I used the background correction method of rma (backgroundMethod="normexp" in readIllumina) to obtain positive intensities prior to transformation, but this option seems no longer available? So how does beadarray deal with the negative intensities? Also, is there any opinion on the order of steps - transform bead-level intensities, then summarize, or summarize and then log2 transform, (either followed by normalization)?
Many thanks ... Ina

----- Original Message -----
From: "Mike Smith" <grimbough at gmail.com>
To: "Ina Hoeschele" <inah at vbi.vt.edu>
Cc: bioconductor at stat.math.ethz.ch
Sent: Thursday, February 17, 2011 1:59:11 PM
Subject: Re: [BioC] problems with readIllumina() for many BeadChips

Hi Ina,

Sorry, I think my message has probably created some confusion.

Version 2.0.4 is not the developmental version, its a patched version of the
release.  You can install it using biocLite("beadarray").  I uploaded the
patch yesterday, so it may take a day or so to filter through.

If you want to install the developmental version (currently 2.1.11) you
normally need to be running a developmental version of R, although there was
some discussion about this a few weeks back on this list.  This version
should be considered experimental and potentially buggy, but as you've seen
that can probably be applied to parts of the release package too!

Mike Smith
PhD Student
Computational Biology Group
Department of Oncology
Cambridge University
On 17 Feb 2011 16:52, "Ina Hoeschele" <inah at vbi.vt.edu> wrote:

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