
Dear All,I have a question regarding the finding the annotation of differentially express genes between two conditions. I want to submit the list to database and map the gene list to the pathways they are involved in. I am only able to use DAVID which allow me to submit a list, I tried KEGG but I can only do one gene at a time. Can someone please let me know if there is any free database out there that I can use to map my gene to pathways.Regards,Saikou

Me Sybah2

> From: bioconductor-request@r-project.org
> Subject: Bioconductor Digest, Vol 108, Issue 19
> To: bioconductor@r-project.org
> Date: Sun, 19 Feb 2012 12:00:03 +0100
> 
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> 
> Today's Topics:
> 
>    1. Re: Siggenes SAM analysis: log2 transformation and
>       Understanding output (Holger Schwender)
>    2. how to identify affymetrix control probe sets and then remove
>       them from an expression set (Salwa Eid)
>    3. Re: EBImage package (Dan Tenenbaum)
>    4. Re: Compensating using spillover function of flowCore (John Pura)
>    5. Removing phenoData and assayData (tyrone [guest])
>    6. Re: meta-analysis with RankProd (Maciej Jo?czyk)
>    7. Re: Removing phenoData and assayData (Sean Davis)
>    8. Installation errors for DESeq, Genomic Ranges etc. (Alpesh Querer)
>    9. Re: Installation errors for DESeq, Genomic Ranges etc.
>       (Martin Morgan)
>   10. ChIPpeakAnno (Brian James Gadd)
>   11. use of duplicateCorrelation in Limma with agilent one-color
>       arrays (Gordon K Smyth)
>   12. edgeR: GLM - adjusting for unwanted effect?! (Gordon K Smyth)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Sat, 18 Feb 2012 13:21:24 +0100
> From: "Holger Schwender" <holger.schw@gmx.de>
> To: "James W. MacDonald" <jmacdon@uw.edu>, david@harsk.dk
> Cc: bioconductor@r-project.org
> Subject: Re: [BioC] Siggenes SAM analysis: log2 transformation and
> 	Understanding output
> Message-ID: <20120218122124.204480@gmx.net>
> Content-Type: text/plain; charset="utf-8"
> 
> Hi Jim, hi David,
> 
> thanks a lot, Jim, for answering these questions. Just one or two comments:
> 
> > > Lastly, is there an option to specify a FDR threshold? I am running
> > > through multiple data sets, and I would like to automate it, instead
> > > of having to looking at a table for each one individually.
> > 
> 
> > I don't see how you could automate things with siggenes. It is designed 
> > to be run interactively.
> > 
> 
> There exists a function called findDelta in siggenes, which allows you to find the value of delta and thus the set of genes for which the FDR is below some specified threshold. So if you really wanna automate your analysis by specifyig a specific threshold for the FDR (which is actually not really the idea behind SAM), then you can do this by using findDelta.
> 
> (Alternatively, you can use the q-values that are computed for all genes -- available via the slot q.value, i.e. sam.out@q.value -- to select all genes with a q-value, i.e. FDR adjusted p-value, below some threshold. Please note the set of genes might slightly differ between the SAM FDR and the q-value approach, mainly because in the former approach the thresholds are not necessary symmetric to the origin.)
> 
> 
> > limma package, which estimates FDR using p.adjust(), rather than via 
> > permutation. So if you are interested in automation, particularly 
> > automating the annotation/output side of things, take a look at 
> > affycoretools.
> > </shameless plug>
> 
> Don't think that this is a shameless plug. Just some nice hints to other great, helpful packages. 
> 
> Just wanted to note that you can also use the results of a limma analysis in siggenes. There exists a function called limma2sam in siggenes which allows you to perform a SAM analysis with the limma statistic.
> 
> Moreover, SAM does not really rely on permutations. E.g., all the stuff in siggenes for SNPs uses asymptotic null distributions. If you wanna use the moderated t-statistic proposed in the original SAM paper, then you need permutations. But it is also possible to use the ordinary parametric t-test (which might make sense if you have a sufficient number of samples). I have not directly implemented this in siggenes to make things not more confusing. But code for a parametric t is available in the siggenes vignette and can thus be extracted from it.
> 
> Best,
> Holger
> 
> 
> > 
> > Best,
> > 
> > Jim
> > 
> > 
> > >
> > > Best,
> > > David
> > >
> > >
> > >
> > > 2012/2/16 James W. MacDonald<jmacdon@uw.edu>:
> > >> Hi David,
> > >>
> > >>
> > >> On 2/15/2012 8:30 AM, David Westergaard wrote:
> > >>> Hello,
> > >>>
> > >>> I am currently working on a data set about kiwi consumption for my
> > >>> bachelors project. The data is available at
> > >>> http://www.ebi.ac.uk/arrayexpress/experiments/E-MEXP-2030
> > >>>
> > >>> I'm abit confused as to how to interpret the output parameters,
> > >>> specifically p0. I've run the following code:
> > >>>
> > >>> dataset<- read.table("OAS_RMA.txt",header=TRUE)
> > >>> controls<-
> > >>>
> > cbind(dataset$CEL12.1,dataset$CEL13.1,dataset$CEL23.1,dataset$CEL25.1,dataset$CEL37.1,dataset$CEL59.1,dataset$CEL61.1,dataset$CEL78.1,dataset$CEL9.1,dataset$CEL92.1)
> > >>> experiments<-
> > >>>
> > cbind(dataset$CEL18.1,dataset$CEL21.1,dataset$CEL3.1,dataset$CEL31.1,dataset$CEL46.1,dataset$CEL50.1,dataset$CEL56.1,dataset$CEL57.1,dataset$CEL7.1)
> > >>>
> > >>> library('siggenes')
> > >>> datamatrix<- matrix(cbind(controls,experiments),ncol=19)
> > >>> y<- rep(0,19)
> > >>> y[11:19]<- 1
> > >>> gene_names<- as.character(dataset$Hybridization.REF)
> > >>> sam.obj = sam(datamatrix,y,gene.names=gene_names,rand=12345)
> > >>>
> > >>> Output:
> > >>> AM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances
> > >>>
> > >>>   s0 = 0
> > >>>
> > >>>   Number of permutations: 100
> > >>>
> > >>>   MEAN number of falsely called variables is computed.
> > >>>
> > >>>     Delta    p0    False Called    FDR cutlow cutup   j2    j1
> > >>> 1    0.1 0.634 28335.89  37013 0.4851 -1.058 0.354 9709 27372
> > >>> 2    0.5 0.634 11200.82  21273 0.3336 -2.271 0.910 2447 35850
> > >>> 3    0.9 0.634   249.38   1522 0.1038 -3.374 3.088  541 53695
> > >>> 4    1.3 0.634     9.67    134 0.0457 -4.402 5.577  127 54669
> > >>> 5    1.7 0.634     0.69     20 0.0219 -5.596   Inf   20 54676
> > >>> 6    2.1 0.634        0      1      0 -9.072   Inf    1 54676
> > >>> 7    2.5 0.634        0      1      0 -9.072   Inf    1 54676
> > >>> 8    2.9 0.634        0      1      0 -9.072   Inf    1 54676
> > >>> 9    3.3 0.634        0      1      0 -9.072   Inf    1 54676
> > >>> 10   3.7 0.634        0      0      0   -Inf   Inf    0 54676
> > >>>
> > >>> I'm using the rand parameter because results seems to vary a bit. p0
> > >>> is in this case 0.634, and I'm not sure how to interpret this. From
> > >>> literature, this is described as "Prior probability that a gene is not
> > >>> differentially expressed" - What does this exactly mean? Does this
> > >>> imply, that there is a ~63% percent chance, that the genes in
> > >>> question, are actually NOT differentially expressed?
> > >>
> > >> It means that about 63% of your genes appear to be not differentially
> > >> expressed. So if you choose a gene at random, there is a 63%
> > probability
> > >> that you will choose one that isn't differentially expressed.
> > >>
> > >> However, depending on the value of Delta that you choose, the
> > expectation is
> > >> that a far fewer percentage of the genes selected will be
> > differentially
> > >> expressed. In other words, you are trying to grab genes with a higher
> > >> probability of differential expression, and you are then estimating
> > what
> > >> percentage of those genes are still likely false positives (e.g., if
> > you
> > >> choose a Delta of 1.3, you will get 134 significant genes, and will
> > expect
> > >> that about 10 of those will be false positives).
> > >>
> > >>
> > >>> I've also found some varying sources saying that it is a good idea to
> > >>> log2 transform data before inputting into SAM. Does this still apply,
> > >>> and if so, why?
> > >>
> > >> This is because the t-test is based on means, which are not very robust
> > to
> > >> outliers. Gene expression data tend to have a strong right skew,
> > meaning
> > >> that most of the data are within a certain range, but there are some
> > values
> > >> much higher. If you take logs, it tends to minimize the skew, so the
> > large
> > >> values have less of an effect (on the linear scale, expression values
> > range
> > >> from 0-65,000, on log2 scale, they range from 0-16). It doesn't matter
> > what
> > >> base you use, but people have tended to use log base 2 because then a
> > >> difference of 1 indicates a two-fold difference on the linear scale.
> > >>
> > >> Best,
> > >>
> > >> Jim
> > >>
> > >>
> > >>> Best Regards,
> > >>>
> > >>> David Westergaard
> > >>> Undergraduate student
> > >>> Technical University of Denmark
> > >>>
> > >>> _______________________________________________
> > >>> Bioconductor mailing list
> > >>> Bioconductor@r-project.org
> > >>> https://stat.ethz.ch/mailman/listinfo/bioconductor
> > >>> Search the archives:
> > >>> http://news.gmane.org/gmane.science.biology.informatics.conductor
> > 
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> > http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> --
> 
> 
> 
> ------------------------------
> 
> Message: 2
> Date: Sat, 18 Feb 2012 13:16:40 +0000
> From: Salwa Eid <salwaeid@hotmail.com>
> To: Bioconductor mailing list <bioconductor@r-project.org>
> Subject: [BioC] how to identify affymetrix control probe sets and then
> 	remove them from an expression set
> Message-ID: <SNT115-W43E16DE2C2A6CEB26F71E9CF600@phx.gbl>
> Content-Type: text/plain
> 
> 
> 
>  
> 
> 
> 
> 
> 
> 
> 
> 
> 
> Dear all,
>  
>   I read cel files(hgu133a2 genechip) from ncbi website using the readaffy() .  Then i performed the expresso function to normalize using quantile normalization and retrieve rma expressions.  Next I want to identify the control probe sets and remove them. Any help?
>  
> Thanks,
> salwa
>  		 	   		   		 	   		  
> 	[[alternative HTML version deleted]]
> 
> 
> 
> ------------------------------
> 
> Message: 3
> Date: Sat, 18 Feb 2012 10:45:46 -0600
> From: Dan Tenenbaum <dtenenba@fhcrc.org>
> To: Jordi van Gestel <jb.jordi@gmail.com>
> Cc: bioconductor@r-project.org
> Subject: Re: [BioC] EBImage package
> Message-ID:
> 	<CAF42j23U9-LzSQVnNvpHT6gu+PpyNYxe-kptwXJs4kbJvUbC0w@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
> 
> On Fri, Feb 17, 2012 at 7:13 AM, Jordi van Gestel <jb.jordi@gmail.com> wrote:
> > Dear Mrs./Mr.,
> >
> >
> >
> > I am Jordi, PhD student at the University of Groningen (the Netherlands). I
> > am working on *Bacillus subtilis* at the Molecular Genetics lab, in which
> > we study the expression of some genes by using fluorescent proteins. To
> > analyze the pictures that are made during the experiments I was advised to
> > use the EBImage package in R (I originally used MATLAB, but this was rather
> > unstable).
> >
> >
> >
> > After reading the manual I was really impressed by the possibilities of the
> > EBImage package. However, unfortunately, I didn't manage to install the
> > package (I am not really a computer specialist, so I had some difficulties
> > with following the tutorials). I performed the following steps in
> > correspondence to the manuals that are published online:
> >
> >
> >
> > 1. First, I installed GTK+ from ?"http://gladewin32.sf.net" (since "
> > http://ftp.gnome.org/pub/gnome/binaries/win32/gtk+/2.16/gtk+-bundle_2.16.0-20090317_win32.zip"
> > and "http://www.gtk.org/download-windows.html" didn't function properly).
> > In addition, I added to the "C:\GTK\bin" path to the system environment
> > PATH variable.
> >
> 
> Use the version of GTK  specified in the EBImage installation vignette:
> http://bioconductor.org/packages/release/bioc/vignettes/EBImage/inst/doc/EBImage-installation.pdf
> 
> The version specified is gtk+-2.0 2.12.9.
> 
> 
> >
> >
> > 2. Second, I installed ImageMagick from "
> > http://www.imagemagick.org/script/binary-releases.php#windows" and placed
> > in the following folder "C:\ImageMagick". During the installation of
> > ImageMagick I checked the checkbox of 'Install development headers and
> > libraries' (the 'Update executable search path' was already checked).
> >
> >
> >
> > 3. Third, I downloaded the source zip-package from "
> > http://www.bioconductor.org/packages/release/bioc/html/EBImage.html" (I
> > tried both 'EBImage_3.10.0.tar.gz' and 'EBImage_3.10.0.zip'). In RGui I
> > subsequently used the botton 'Install package(s) from local zip files...'
> > (Menu of Packages). Resulting in the following two lines in the console:
> >
> >> utils:::menuInstallLocal()
> >
> > package 'EBImage' successfully unpacked and MD5 sums checked
> >
> > updating HTML package descriptions
> >
> >
> >
> > After this the problems appeared: When trying to load the package
> > (library("EBImage")):
> >
> >> library("EBImage")
> >
> > Error in library.dynam(lib, package, package.lib) :
> >
> > ?shared library 'EBImage' not found
> >
> > In addition: Warning message:
> >
> > package 'EBImage' was built under R version 2.14.0
> >
> > Error: package/namespace load failed for 'EBImage'
> >
> >
> 
> 
> Install EBImage as recommended by its page:
> http://bioconductor.org/packages/release/bioc/html/EBImage.html
> 
> namely:
> source("http://bioconductor.org/biocLite.R")
> biocLite("EBImage")
> 
> Dan
> 
> >
> > Perhaps important to know I have Windows XP Professional on my laptop. I
> > really hope that you know the solution to this problem. Sorry, for this
> > inconvenience. Please, let me know if you require more information. In
> > advance, many thanks for your time. I am really appreciative for your help!
> >
> >
> >
> > Best regards,
> >
> >
> >
> > Jordi
> >
> > ? ? ? ?[[alternative HTML version deleted]]
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> 
> 
> ------------------------------
> 
> Message: 4
> Date: Sat, 18 Feb 2012 12:34:01 -0500
> From: "John Pura" <johnandrewpura@gmail.com>
> To: <bioconductor@r-project.org>
> Subject: Re: [BioC] Compensating using spillover function of flowCore
> Message-ID: <000001ccee63$81123990$8336acb0$@gmail.com>
> Content-Type: text/plain
> 
> Thanks for the feedback, Dr. Finak. I've provided the outputs for
> sessionInfo(), summary(compdat), and traceback().
> 
>  
> 
> Here is the output of sessionInfo():
> 
>  
> 
> R version 2.14.1 (2011-12-22)
> 
> Platform: x86_64-redhat-linux-gnu (64-bit)
> 
>  
> 
> locale:
> 
> [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
> 
>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
> 
>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
> 
>  [7] LC_PAPER=C                 LC_NAME=C                 
> 
>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
> 
> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
> 
>  
> 
> attached base packages:
> 
> [1] tools     grid      splines   stats     graphics  grDevices utils    
> 
>  [8] datasets  methods   base     
> 
>  
> 
> other attached packages:
> 
> [1] flowQ_1.14.1          latticeExtra_0.6-19   RColorBrewer_1.0-5   
> 
>  [4] parody_1.12.0         bioDist_1.26.0        KernSmooth_2.23-7    
> 
>  [7] outliers_0.14         flowMerge_2.2.0       foreach_1.3.2        
> 
> [10] codetools_0.2-8       iterators_1.0.5       snow_0.3-8           
> 
> [13] Rgraphviz_1.32.0      graph_1.32.0          flowType_1.0.0       
> 
> [16] flowTrans_1.6.0       flowClust_2.12.1      ellipse_0.3-5        
> 
> [19] mnormt_1.4-5          flowViz_1.18.0        lattice_0.20-0       
> 
> [22] flowMeans_1.6.0       flowStats_1.12.0      cluster_1.14.1       
> 
> [25] mvoutlier_1.9.4       robCompositions_1.5.0 car_2.0-12           
> 
> [28] nnet_7.3-1            compositions_1.10-2   energy_1.4-0         
> 
> [31] MASS_7.3-16           boot_1.3-4            tensorA_0.36         
> 
> [34] rgl_0.92.798          fda_2.2.7             zoo_1.7-6            
> 
> [37] flowCore_1.20.0       rrcov_1.3-01          pcaPP_1.9-45         
> 
> [40] mvtnorm_0.9-9992      robustbase_0.8-0      Biobase_2.14.0       
> 
>  
> 
> loaded via a namespace (and not attached):
> 
> [1] annotate_1.32.1       AnnotationDbi_1.16.11 DBI_0.2-5            
> 
>  [4] feature_1.2.8         geneplotter_1.32.1    IRanges_1.12.5       
> 
>  [7] ks_1.8.5              RSQLite_0.11.1        sfsmisc_1.0-19       
> 
> [10] stats4_2.14.1         xtable_1.6-0         
> 
>  
> 
> Here is the output of summary(compdat)
> 
>  
> 
> > summary(compdat)
> 
> $`Compensation Controls_0_Unstained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H   FITC-A     PE-A PE-Cy7-A  V450-A
> 
> Min.     22440  20030   -133.6     15   -67.32   -88.11   -90.09  -129.6
> 
> 1st Qu.  73220  54480  38040.0  30560   160.40    59.40    12.87    68.4
> 
> Median  103100  78330 192800.0 142500   255.40   115.80    45.54   133.2
> 
> Mean    111300  85040 154900.0 117700   277.80   132.30    63.45   197.5
> 
> 3rd Qu. 137600 109000 251500.0 178100   329.70   167.30    88.11   200.7
> 
> Max.    262100 255100 262100.0 256800 47470.00 18800.00 13190.00 51810.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -56.28      -57.96     -387.9   -115.10        -92.7    0.9
> 
> 1st Qu.   127.70       10.08      194.4    -11.76        274.5  459.9
> 
> Median    615.70       34.44      472.5     14.28        454.5  932.0
> 
> Mean      620.90       40.98      510.9     18.88        553.3  943.6
> 
> 3rd Qu.   915.80       60.48      731.7     42.00        604.8 1390.0
> 
> Max.    16950.00     6660.00    29000.0   3879.00     131300.0 2086.0
> 
>  
> 
> $`Compensation Controls_1_FITC Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H   FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.     21500  20120   -187.1     50   -391.1   -182.20   -84.15   -122.4
> 
> 1st Qu.  72140  53460  41700.0  33510    180.2     70.29    14.85     90.0
> 
> Median  113500  90580 175600.0 130700    266.3    121.80    50.49    156.1
> 
> Mean    119700  91690 149200.0 111600   2615.0    473.80    69.82    285.3
> 
> 3rd Qu. 173500 130600 234000.0 166900    352.4    177.20    93.06    237.6
> 
> Max.    262100 255100 262100.0 256800 179500.0 228800.00 23330.00 262100.0
> 
>           APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.     -186.5      -92.40     -370.8   -104.20       -181.8   67.7
> 
> 1st Qu.   173.9       22.68      220.5     -5.04        302.4  444.6
> 
> Median    564.9       48.72      445.5     21.00        487.8  950.6
> 
> Mean      614.0       64.29      522.9     29.12        739.1  993.2
> 
> 3rd Qu.   843.6       80.64      687.8     51.24        669.6 1515.0
> 
> Max.    32250.0    11370.00    34180.0   6395.00     262100.0 2051.0
> 
>  
> 
> $`Compensation Controls_2_PE Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A    V450-A
> 
> Min.     22940  20020    127.7    182    -68.31   -105.90   -89.10   -108.90
> 
> 1st Qu.  68660  50800  43150.0  34500    180.20     72.27    15.84     87.97
> 
> Median  113700  91500 173900.0 130400    252.40    123.80    50.49    148.00
> 
> Mean    119000  91440 150200.0 112000    439.40    602.40    89.98    354.50
> 
> 3rd Qu. 172200 131000 230800.0 164500    323.70    177.20    93.06    215.10
> 
> Max.    262100 255100 262100.0 256800 262100.00 262100.00 63440.00 262100.00
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -37.8      -78.12     -297.9   -106.70        -77.4    2.3
> 
> 1st Qu.    174.7       14.28      245.7     -7.56        296.1  589.0
> 
> Median     568.7       44.52      466.2     20.16        458.1 1244.0
> 
> Mean       658.3       82.72      595.4     36.42        729.6 1262.0
> 
> 3rd Qu.    835.0       81.48      693.9     50.61        612.0 1905.0
> 
> Max.    127100.0    65180.00   190400.0  29760.00     262100.0 2608.0
> 
>  
> 
> $`Compensation Controls_3_PE-Cy7 Stained Control.fcs`
> 
>            FSC-A  FSC-H  SSC-A  SSC-H    FITC-A     PE-A  PE-Cy7-A   V450-A
> 
> Min.    -2874000  20060   5497   4909    -40.59   -92.07   -293.00   -110.7
> 
> 1st Qu.    75670  57080  53390  42090    184.10    77.22     95.04     90.9
> 
> Median    106000  82500 198800 148100    255.40   125.70    253.40    154.8
> 
> Mean      116000  89860 168900 129600    365.90   197.30    694.50    245.4
> 
> 3rd Qu.   165100 126700 253800 182300    326.70   174.20    524.90    225.0
> 
> Max.      262100 255100 262100 256800 154400.00 90470.00 262100.00 130900.0
> 
>             APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -28.56      -57.12     -335.7   -107.50        -87.3   38.0
> 
> 1st Qu.    197.40       15.12      272.7     -1.68        314.1  420.0
> 
> Median     655.60       37.80      531.0     28.56        473.4  936.5
> 
> Mean       716.30       57.11      650.4    134.70        660.3  964.9
> 
> 3rd Qu.    932.40       63.00      763.2     62.16        617.4 1482.0
> 
> Max.    122700.00    32030.00   247200.0 122200.00     262100.0 2016.0
> 
>  
> 
> $`Compensation Controls_4_V450 Stained Control.fcs`
> 
>          FSC-A  FSC-H     SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A  V450-A
> 
> Min.     22690  20020    -91.08     13   -53.46 -104.00  -121.80  -130.5
> 
> 1st Qu.  77500  58050  43490.00  34750   177.20   64.35    15.84   157.5
> 
> Median  103100  78780 198700.00 146500   254.40  114.80    50.49   252.0
> 
> Mean    110800  85190 161500.00 123200   274.10  124.70    60.86   851.9
> 
> 3rd Qu. 133600 106100 253500.00 181400   327.70  161.40    92.07   376.2
> 
> Max.    262100 255100 262100.00 256800 12860.00 6253.00  3270.00 33160.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -42.84      -55.44     -327.6    -99.12        -78.3    0.1
> 
> 1st Qu.   156.20        8.40      236.7    -11.76        383.4  463.5
> 
> Median    630.80       29.40      503.1     13.44        537.3 1136.0
> 
> Mean      655.60       32.99      548.6     16.26        773.1 1171.0
> 
> 3rd Qu.   917.30       52.08      749.0     40.32        722.7 1816.0
> 
> Max.    37890.00     2163.00    45720.0   1352.00      34480.0 2549.0
> 
>  
> 
> $`Compensation Controls_5_APC Stained Control.fcs`
> 
>          FSC-A  FSC-H  SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A  V450-A
> APC-A
> 
> Min.     20240  20020   5228   4612   -53.46 -126.70   -90.09  -139.5
> -541.0
> 
> 1st Qu.  70300  52930  37010  30020   151.50   50.49    12.87    74.7
> 279.5
> 
> Median  102300  78760 188400 140200   230.70  101.00    46.53   143.5
> 754.3
> 
> Mean    110700  84420 152400 115900   248.70  113.20    58.26   181.6
> 2726.0
> 
> 3rd Qu. 141300 110400 243800 174800   300.00  149.50    87.12   214.2
> 1123.0
> 
> Max.    262100 255100 262100 256800 12960.00 6089.00  2752.00 19060.0
> 262100.0
> 
>         Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.        -113.40     -419.4   -124.30       -124.2  884.8
> 
> 1st Qu.       14.28      243.0    -10.92        246.6 1236.0
> 
> Median        52.08      528.8     20.16        408.6 1587.0
> 
> Mean         510.40      765.0    115.30        482.1 1593.0
> 
> 3rd Qu.       97.44      794.7     57.96        562.7 1944.0
> 
> Max.       73160.00    37700.0  15620.00      27730.0 2321.0
> 
>  
> 
> $`Compensation Controls_6_Alexa 700 Stained Control.fcs`
> 
>            FSC-A  FSC-H    SSC-A  SSC-H   FITC-A     PE-A PE-Cy7-A  V450-A
> 
> Min.    -2622000  20020    141.6    120   -67.32  -121.80   -77.22  -121.5
> 
> 1st Qu.    72730  54960  40080.0  32480   173.20    61.38    22.77    74.7
> 
> Median    102100  77940 198800.0 147400   251.50   110.90    61.38   139.5
> 
> Mean      109700  84430 160100.0 122300   278.50   126.00    74.92   171.4
> 
> 3rd Qu.   135400 107500 254700.0 181500   322.70   160.40   102.00   209.7
> 
> Max.      262100 255100 262100.0 256800 63190.00 29600.00 15580.00 43060.0
> 
>             APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -55.44      -90.72     -389.7   -110.00       -101.7    0.1
> 
> 1st Qu.    203.30       94.92      234.9     18.48        282.6  461.0
> 
> Median     665.30      157.90      506.2     60.48        444.6  956.8
> 
> Mean       695.40      702.00      573.8    213.50        524.1  974.9
> 
> 3rd Qu.    932.40      259.60      760.7    125.20        594.9 1483.0
> 
> Max.    134000.00   153800.00   159500.0  93380.00     123600.0 2011.0
> 
>  
> 
> $`Compensation Controls_7_QDot 655 Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.     22650  20040   -176.2     17    -47.52   -105.90   -82.17   -131.4
> 
> 1st Qu.  57470  43840  45760.0  36230    166.30     60.39    17.82     71.1
> 
> Median   83440  62800 198700.0 144300    236.60    107.90    51.48    134.1
> 
> Mean     96230  73220 163700.0 123100    336.20    168.70    98.69    262.6
> 
> 3rd Qu. 120200  94580 251500.0 177500    306.90    154.40    91.08    203.4
> 
> Max.    262100 255000 262100.0 256800 262100.00 172500.00 56410.00 262100.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -71.4      -47.88     -641.7    -99.12        -73.8   81.7
> 
> 1st Qu.    679.6       24.36      879.1     -9.24        269.1  859.6
> 
> Median     983.6       47.04     2632.0     15.12        411.3 2204.0
> 
> Mean      4198.0      175.30    23880.0     24.77        614.0 2265.0
> 
> 3rd Qu.   1548.0       78.12    10840.0     43.68        552.6 3623.0
> 
> Max.    262100.0    60750.00   262100.0   7120.00     262100.0 4832.0
> 
>  
> 
> $`Compensation Controls_8_APC-Cy7 Stained Control.fcs`
> 
>            FSC-A  FSC-H  SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.    -3196000  20040   5199   4744    -46.53   -101.00   -80.19   -208.8
> 
> 1st Qu.    70120  52830  44540  35680    174.20     60.39    18.81     82.8
> 
> Median     94710  70700 206200 151400    246.50    108.90    53.46    146.7
> 
> Mean      104300  80270 167700 128600    348.30    161.90   120.60    294.3
> 
> 3rd Qu.   126400 100600 262100 188700    316.80    158.40    97.02    219.6
> 
> Max.      262100 255100 262100 256800 220600.00 100300.00 86300.00 262100.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -432.6      -56.28     -370.8   -239.40        -64.8    0.7
> 
> 1st Qu.    254.3       10.92      271.8     10.92        285.3  445.3
> 
> Median     724.9       32.76      537.3     52.92        429.3  974.1
> 
> Mean       859.7       73.50      834.9    545.40        637.8 1017.0
> 
> 3rd Qu.   1029.0       56.28      785.7    122.60        572.4 1563.0
> 
> Max.    262100.0    24640.00   262100.0 262100.00     262100.0 2164.0
> 
>  
> 
> $`Compensation Controls_9_Pac Orange Stained Control.fcs`
> 
>          FSC-A  FSC-H  SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A   V450-A
> APC-A
> 
> Min.     22550  20060   5352   4647   -50.49  -94.05   -70.29   -122.4
> -26.88
> 
> 1st Qu.  73900  55310  44820  35960   174.20   61.38    11.88     85.5
> 162.10
> 
> Median  101600  77290 198400 147400   246.50  111.90    44.55    153.0
> 641.80
> 
> Mean    111700  85420 165000 126600   279.10  128.20    58.68    308.4
> 662.00
> 
> 3rd Qu. 137100 108100 260100 185800   317.80  159.40    83.16    224.1
> 938.50
> 
> Max.    262100 255100 262100 256800 11950.00 7378.00 10690.00 197800.0
> 8495.00
> 
>         Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.         -61.32     -493.2    -97.44        -64.8    0.5
> 
> 1st Qu.        6.72      243.7    -11.76        306.0  299.4
> 
> Median        26.88      546.8     12.60        483.3  701.1
> 
> Mean          32.90      709.5     25.83        998.8  723.9
> 
> 3rd Qu.       48.72      822.8     40.32        654.3 1133.0
> 
> Max.        2895.00    87860.0  12060.00     244400.0 1565.0
> 
>  
> 
> > print(compdat)
> 
> A flowSet with 10 experiments.
> 
>  
> 
>   column names:
> 
>   FSC-A FSC-H SSC-A SSC-H FITC-A PE-A PE-Cy7-A V450-A APC-A Alexa 700-A QDot
> 655-A APC-Cy7-A Pac Orange-A Time
> 
> > summary(compdat)
> 
> $`Compensation Controls_0_Unstained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H   FITC-A     PE-A PE-Cy7-A  V450-A
> 
> Min.     22440  20030   -133.6     15   -67.32   -88.11   -90.09  -129.6
> 
> 1st Qu.  73220  54480  38040.0  30560   160.40    59.40    12.87    68.4
> 
> Median  103100  78330 192800.0 142500   255.40   115.80    45.54   133.2
> 
> Mean    111300  85040 154900.0 117700   277.80   132.30    63.45   197.5
> 
> 3rd Qu. 137600 109000 251500.0 178100   329.70   167.30    88.11   200.7
> 
> Max.    262100 255100 262100.0 256800 47470.00 18800.00 13190.00 51810.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -56.28      -57.96     -387.9   -115.10        -92.7    0.9
> 
> 1st Qu.   127.70       10.08      194.4    -11.76        274.5  459.9
> 
> Median    615.70       34.44      472.5     14.28        454.5  932.0
> 
> Mean      620.90       40.98      510.9     18.88        553.3  943.6
> 
> 3rd Qu.   915.80       60.48      731.7     42.00        604.8 1390.0
> 
> Max.    16950.00     6660.00    29000.0   3879.00     131300.0 2086.0
> 
>  
> 
> $`Compensation Controls_1_FITC Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H   FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.     21500  20120   -187.1     50   -391.1   -182.20   -84.15   -122.4
> 
> 1st Qu.  72140  53460  41700.0  33510    180.2     70.29    14.85     90.0
> 
> Median  113500  90580 175600.0 130700    266.3    121.80    50.49    156.1
> 
> Mean    119700  91690 149200.0 111600   2615.0    473.80    69.82    285.3
> 
> 3rd Qu. 173500 130600 234000.0 166900    352.4    177.20    93.06    237.6
> 
> Max.    262100 255100 262100.0 256800 179500.0 228800.00 23330.00 262100.0
> 
>           APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.     -186.5      -92.40     -370.8   -104.20       -181.8   67.7
> 
> 1st Qu.   173.9       22.68      220.5     -5.04        302.4  444.6
> 
> Median    564.9       48.72      445.5     21.00        487.8  950.6
> 
> Mean      614.0       64.29      522.9     29.12        739.1  993.2
> 
> 3rd Qu.   843.6       80.64      687.8     51.24        669.6 1515.0
> 
> Max.    32250.0    11370.00    34180.0   6395.00     262100.0 2051.0
> 
>  
> 
> $`Compensation Controls_2_PE Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A    V450-A
> 
> Min.     22940  20020    127.7    182    -68.31   -105.90   -89.10   -108.90
> 
> 1st Qu.  68660  50800  43150.0  34500    180.20     72.27    15.84     87.97
> 
> Median  113700  91500 173900.0 130400    252.40    123.80    50.49    148.00
> 
> Mean    119000  91440 150200.0 112000    439.40    602.40    89.98    354.50
> 
> 3rd Qu. 172200 131000 230800.0 164500    323.70    177.20    93.06    215.10
> 
> Max.    262100 255100 262100.0 256800 262100.00 262100.00 63440.00 262100.00
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -37.8      -78.12     -297.9   -106.70        -77.4    2.3
> 
> 1st Qu.    174.7       14.28      245.7     -7.56        296.1  589.0
> 
> Median     568.7       44.52      466.2     20.16        458.1 1244.0
> 
> Mean       658.3       82.72      595.4     36.42        729.6 1262.0
> 
> 3rd Qu.    835.0       81.48      693.9     50.61        612.0 1905.0
> 
> Max.    127100.0    65180.00   190400.0  29760.00     262100.0 2608.0
> 
>  
> 
> $`Compensation Controls_3_PE-Cy7 Stained Control.fcs`
> 
>            FSC-A  FSC-H  SSC-A  SSC-H    FITC-A     PE-A  PE-Cy7-A   V450-A
> 
> Min.    -2874000  20060   5497   4909    -40.59   -92.07   -293.00   -110.7
> 
> 1st Qu.    75670  57080  53390  42090    184.10    77.22     95.04     90.9
> 
> Median    106000  82500 198800 148100    255.40   125.70    253.40    154.8
> 
> Mean      116000  89860 168900 129600    365.90   197.30    694.50    245.4
> 
> 3rd Qu.   165100 126700 253800 182300    326.70   174.20    524.90    225.0
> 
> Max.      262100 255100 262100 256800 154400.00 90470.00 262100.00 130900.0
> 
>             APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -28.56      -57.12     -335.7   -107.50        -87.3   38.0
> 
> 1st Qu.    197.40       15.12      272.7     -1.68        314.1  420.0
> 
> Median     655.60       37.80      531.0     28.56        473.4  936.5
> 
> Mean       716.30       57.11      650.4    134.70        660.3  964.9
> 
> 3rd Qu.    932.40       63.00      763.2     62.16        617.4 1482.0
> 
> Max.    122700.00    32030.00   247200.0 122200.00     262100.0 2016.0
> 
>  
> 
> $`Compensation Controls_4_V450 Stained Control.fcs`
> 
>          FSC-A  FSC-H     SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A  V450-A
> 
> Min.     22690  20020    -91.08     13   -53.46 -104.00  -121.80  -130.5
> 
> 1st Qu.  77500  58050  43490.00  34750   177.20   64.35    15.84   157.5
> 
> Median  103100  78780 198700.00 146500   254.40  114.80    50.49   252.0
> 
> Mean    110800  85190 161500.00 123200   274.10  124.70    60.86   851.9
> 
> 3rd Qu. 133600 106100 253500.00 181400   327.70  161.40    92.07   376.2
> 
> Max.    262100 255100 262100.00 256800 12860.00 6253.00  3270.00 33160.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -42.84      -55.44     -327.6    -99.12        -78.3    0.1
> 
> 1st Qu.   156.20        8.40      236.7    -11.76        383.4  463.5
> 
> Median    630.80       29.40      503.1     13.44        537.3 1136.0
> 
> Mean      655.60       32.99      548.6     16.26        773.1 1171.0
> 
> 3rd Qu.   917.30       52.08      749.0     40.32        722.7 1816.0
> 
> Max.    37890.00     2163.00    45720.0   1352.00      34480.0 2549.0
> 
>  
> 
> $`Compensation Controls_5_APC Stained Control.fcs`
> 
>          FSC-A  FSC-H  SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A  V450-A
> APC-A
> 
> Min.     20240  20020   5228   4612   -53.46 -126.70   -90.09  -139.5
> -541.0
> 
> 1st Qu.  70300  52930  37010  30020   151.50   50.49    12.87    74.7
> 279.5
> 
> Median  102300  78760 188400 140200   230.70  101.00    46.53   143.5
> 754.3
> 
> Mean    110700  84420 152400 115900   248.70  113.20    58.26   181.6
> 2726.0
> 
> 3rd Qu. 141300 110400 243800 174800   300.00  149.50    87.12   214.2
> 1123.0
> 
> Max.    262100 255100 262100 256800 12960.00 6089.00  2752.00 19060.0
> 262100.0
> 
>         Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.        -113.40     -419.4   -124.30       -124.2  884.8
> 
> 1st Qu.       14.28      243.0    -10.92        246.6 1236.0
> 
> Median        52.08      528.8     20.16        408.6 1587.0
> 
> Mean         510.40      765.0    115.30        482.1 1593.0
> 
> 3rd Qu.       97.44      794.7     57.96        562.7 1944.0
> 
> Max.       73160.00    37700.0  15620.00      27730.0 2321.0
> 
>  
> 
> $`Compensation Controls_6_Alexa 700 Stained Control.fcs`
> 
>            FSC-A  FSC-H    SSC-A  SSC-H   FITC-A     PE-A PE-Cy7-A  V450-A
> 
> Min.    -2622000  20020    141.6    120   -67.32  -121.80   -77.22  -121.5
> 
> 1st Qu.    72730  54960  40080.0  32480   173.20    61.38    22.77    74.7
> 
> Median    102100  77940 198800.0 147400   251.50   110.90    61.38   139.5
> 
> Mean      109700  84430 160100.0 122300   278.50   126.00    74.92   171.4
> 
> 3rd Qu.   135400 107500 254700.0 181500   322.70   160.40   102.00   209.7
> 
> Max.      262100 255100 262100.0 256800 63190.00 29600.00 15580.00 43060.0
> 
>             APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -55.44      -90.72     -389.7   -110.00       -101.7    0.1
> 
> 1st Qu.    203.30       94.92      234.9     18.48        282.6  461.0
> 
> Median     665.30      157.90      506.2     60.48        444.6  956.8
> 
> Mean       695.40      702.00      573.8    213.50        524.1  974.9
> 
> 3rd Qu.    932.40      259.60      760.7    125.20        594.9 1483.0
> 
> Max.    134000.00   153800.00   159500.0  93380.00     123600.0 2011.0
> 
>  
> 
> $`Compensation Controls_7_QDot 655 Stained Control.fcs`
> 
>          FSC-A  FSC-H    SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.     22650  20040   -176.2     17    -47.52   -105.90   -82.17   -131.4
> 
> 1st Qu.  57470  43840  45760.0  36230    166.30     60.39    17.82     71.1
> 
> Median   83440  62800 198700.0 144300    236.60    107.90    51.48    134.1
> 
> Mean     96230  73220 163700.0 123100    336.20    168.70    98.69    262.6
> 
> 3rd Qu. 120200  94580 251500.0 177500    306.90    154.40    91.08    203.4
> 
> Max.    262100 255000 262100.0 256800 262100.00 172500.00 56410.00 262100.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.       -71.4      -47.88     -641.7    -99.12        -73.8   81.7
> 
> 1st Qu.    679.6       24.36      879.1     -9.24        269.1  859.6
> 
> Median     983.6       47.04     2632.0     15.12        411.3 2204.0
> 
> Mean      4198.0      175.30    23880.0     24.77        614.0 2265.0
> 
> 3rd Qu.   1548.0       78.12    10840.0     43.68        552.6 3623.0
> 
> Max.    262100.0    60750.00   262100.0   7120.00     262100.0 4832.0
> 
>  
> 
> $`Compensation Controls_8_APC-Cy7 Stained Control.fcs`
> 
>            FSC-A  FSC-H  SSC-A  SSC-H    FITC-A      PE-A PE-Cy7-A   V450-A
> 
> Min.    -3196000  20040   5199   4744    -46.53   -101.00   -80.19   -208.8
> 
> 1st Qu.    70120  52830  44540  35680    174.20     60.39    18.81     82.8
> 
> Median     94710  70700 206200 151400    246.50    108.90    53.46    146.7
> 
> Mean      104300  80270 167700 128600    348.30    161.90   120.60    294.3
> 
> 3rd Qu.   126400 100600 262100 188700    316.80    158.40    97.02    219.6
> 
> Max.      262100 255100 262100 256800 220600.00 100300.00 86300.00 262100.0
> 
>            APC-A Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.      -432.6      -56.28     -370.8   -239.40        -64.8    0.7
> 
> 1st Qu.    254.3       10.92      271.8     10.92        285.3  445.3
> 
> Median     724.9       32.76      537.3     52.92        429.3  974.1
> 
> Mean       859.7       73.50      834.9    545.40        637.8 1017.0
> 
> 3rd Qu.   1029.0       56.28      785.7    122.60        572.4 1563.0
> 
> Max.    262100.0    24640.00   262100.0 262100.00     262100.0 2164.0
> 
>  
> 
> $`Compensation Controls_9_Pac Orange Stained Control.fcs`
> 
>          FSC-A  FSC-H  SSC-A  SSC-H   FITC-A    PE-A PE-Cy7-A   V450-A
> APC-A
> 
> Min.     22550  20060   5352   4647   -50.49  -94.05   -70.29   -122.4
> -26.88
> 
> 1st Qu.  73900  55310  44820  35960   174.20   61.38    11.88     85.5
> 162.10
> 
> Median  101600  77290 198400 147400   246.50  111.90    44.55    153.0
> 641.80
> 
> Mean    111700  85420 165000 126600   279.10  128.20    58.68    308.4
> 662.00
> 
> 3rd Qu. 137100 108100 260100 185800   317.80  159.40    83.16    224.1
> 938.50
> 
> Max.    262100 255100 262100 256800 11950.00 7378.00 10690.00 197800.0
> 8495.00
> 
>         Alexa 700-A QDot 655-A APC-Cy7-A Pac Orange-A   Time
> 
> Min.         -61.32     -493.2    -97.44        -64.8    0.5
> 
> 1st Qu.        6.72      243.7    -11.76        306.0  299.4
> 
> Median        26.88      546.8     12.60        483.3  701.1
> 
> Mean          32.90      709.5     25.83        998.8  723.9
> 
> 3rd Qu.       48.72      822.8     40.32        654.3 1133.0
> 
> Max.        2895.00    87860.0  12060.00     244400.0 1565.0
> 
>  
> 
> Finally, here is the output of traceback():
> 
>  
> 
> 3: .local(x, ...)
> 
> 2: spillover(compdat[, c(1, 3, 5:13)], unstained = sampleNames(compdat)[1], 
> 
>        patt = namepatt, fsc = "FSC-A", ssc = "SSC-A", method = "median", 
> 
>        useNormFilt = TRUE)
> 
> 1: spillover(compdat[, c(1, 3, 5:13)], unstained = sampleNames(compdat)[1], 
> 
>        patt = namepatt, fsc = "FSC-A", ssc = "SSC-A", method = "median", 
> 
>        useNormFilt = TRUE)
> 
> 
> 	[[alternative HTML version deleted]]
> 
> 
> 
> ------------------------------
> 
> Message: 5
> Date: Sat, 18 Feb 2012 09:55:09 -0800 (PST)
> From: "tyrone [guest]" <guest@bioconductor.org>
> To: bioconductor@r-project.org, ieee529@gmail.com
> Subject: [BioC] Removing phenoData and assayData
> Message-ID: <20120218175509.19E75146E0E@mamba.fhcrc.org>
> 
> 
> How can I delete/remove phenoData and assayData elements?  thanks
> 
>  -- output of sessionInfo(): 
> 
> R version 2.12.1 (2010-12-16)
> Platform: x86_64-pc-linux-gnu (64-bit)
> 
> locale:
>  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
>  [5] LC_MONETARY=C              LC_MESSAGES=en_GB.UTF-8    LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
> 
> attached base packages:
> [1] grid      stats     graphics  grDevices utils     datasets  methods   base     
> 
> other attached packages:
> [1] marray_1.28.0        lattice_0.19-17      impute_1.24.0        limma_3.6.9          annotate_1.28.1     
> [6] AnnotationDbi_1.12.1 affy_1.28.1          Biobase_2.10.0      
> 
> loaded via a namespace (and not attached):
> [1] affyio_1.16.0         DBI_0.2-5             preprocessCore_1.10.0 RSQLite_0.9-1         tools_2.12.1         
> [6] xtable_1.6-0   
> 
> --
> Sent via the guest posting facility at bioconductor.org.
> 
> 
> 
> ------------------------------
> 
> Message: 6
> Date: Sat, 18 Feb 2012 22:25:32 +0100
> From: Maciej Jo?czyk <mjonczyk@biol.uw.edu.pl>
> To: <Barbara.B.Shih@manchester.ac.uk>, <bioconductor@r-project.org>
> Subject: Re: [BioC] meta-analysis with RankProd
> Message-ID: <0a5074be7200a0d33262c3148eace4c1@biol.uw.edu.pl>
> Content-Type: text/plain; charset=UTF-8; format=flowed
> 
> Hi Barbara,
> 
> > Can I just log2-transform the normalised intensity in the one channel
> > data sets and compare them like that?
> 
> I advise you to transform your log2 data to probability of expression 
> scale (POE, available in metaArray and poe packages).
> 
> see paper Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues 
> in
> conducting a meta-analysis of gene expression microarray datasets. PLoS 
> Med 5(9):
> e184. doi:10.1371/journal.pmed.0050184.
> 
> I've found it useful.
> 
> Best Wishes,
> Maciej
> 
> -- 
> Maciej Jonczyk,
> Department of Plant Molecular Ecophysiology
> Faculty of Biology, University of Warsaw
> 02-096 Warsaw, Miecznikowa 1
> Poland
> 
> 
> 
> -- 
> This email was Anti Virus checked by Astaro Security Gateway. http://www.astaro.com
> 
> 
> 
> ------------------------------
> 
> Message: 7
> Date: Sat, 18 Feb 2012 17:17:52 -0500
> From: Sean Davis <sdavis2@mail.nih.gov>
> To: "tyrone [guest]" <guest@bioconductor.org>
> Cc: ieee529@gmail.com, bioconductor@r-project.org
> Subject: Re: [BioC] Removing phenoData and assayData
> Message-ID:
> 	<CANeAVBkLi+OJMk0=EVsmCV=+GkyOYOzY1SMCVndoP1HQx2E91A@mail.gmail.com>
> Content-Type: text/plain; charset=UTF-8
> 
> On Sat, Feb 18, 2012 at 12:55 PM, tyrone [guest] <guest@bioconductor.org> wrote:
> >
> > How can I delete/remove phenoData and assayData elements? ?thanks
> 
> Hi, Tyrone.  In R, one does not typically delete something.  If you
> want to remove something, you simply replace the original item with a
> new version that does not contain the item you want to remove.  If you
> want more help, could you be a bit more specific about what you are
> trying to do?
> 
> Sean
> 
> 
> > ?-- output of sessionInfo():
> >
> > R version 2.12.1 (2010-12-16)
> > Platform: x86_64-pc-linux-gnu (64-bit)
> >
> > locale:
> > ?[1] LC_CTYPE=en_GB.UTF-8 ? ? ? LC_NUMERIC=C ? ? ? ? ? ? ? LC_TIME=en_GB.UTF-8 ? ? ? ?LC_COLLATE=en_GB.UTF-8
> > ?[5] LC_MONETARY=C ? ? ? ? ? ? ?LC_MESSAGES=en_GB.UTF-8 ? ?LC_PAPER=en_GB.UTF-8 ? ? ? LC_NAME=C
> > ?[9] LC_ADDRESS=C ? ? ? ? ? ? ? LC_TELEPHONE=C ? ? ? ? ? ? LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
> >
> > attached base packages:
> > [1] grid ? ? ?stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base
> >
> > other attached packages:
> > [1] marray_1.28.0 ? ? ? ?lattice_0.19-17 ? ? ?impute_1.24.0 ? ? ? ?limma_3.6.9 ? ? ? ? ?annotate_1.28.1
> > [6] AnnotationDbi_1.12.1 affy_1.28.1 ? ? ? ? ?Biobase_2.10.0
> >
> > loaded via a namespace (and not attached):
> > [1] affyio_1.16.0 ? ? ? ? DBI_0.2-5 ? ? ? ? ? ? preprocessCore_1.10.0 RSQLite_0.9-1 ? ? ? ? tools_2.12.1
> > [6] xtable_1.6-0
> >
> > --
> > Sent via the guest posting facility at bioconductor.org.
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> 
> 
> ------------------------------
> 
> Message: 8
> Date: Sat, 18 Feb 2012 18:18:43 -0600
> From: Alpesh Querer <alpeshq@gmail.com>
> To: bioconductor@r-project.org
> Subject: [BioC] Installation errors for DESeq, Genomic Ranges etc.
> Message-ID:
> 	<CAO0xdQPxBrGMKqPMd4zrPTHkKNi9dFMdx5Tp9F3z52FF5vaGWQ@mail.gmail.com>
> Content-Type: text/plain
> 
> Hi all,
> 
> I`m trying to install some packages like DESeq, GenomicRanges etc with R
> 2.14.
> I am getting error like the following. Same with dependent packages like
> RCurl, rtracklayer.
> Can someone help me with correct installation instructions here?
> 
> 
> >     source("http://www.bioconductor.org/biocLite.R")
> >     biocLite("GenomicRanges")
> BioC_mirror: 'http://www.bioconductor.org'
> Using R version 2.14, BiocInstaller version 1.2.1.
> Installing package(s) 'GenomicRanges'
> also installing the dependency ‘IRanges’
> 
> trying URL '
> http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/IRanges_1.12.5.zip
> '
> Error in download.file(url, destfile, method, mode = "wb", ...) :
>   cannot open URL '
> http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/IRanges_1.12.5.zip
> '
> In addition: Warning message:
> In download.file(url, destfile, method, mode = "wb", ...) :
>   cannot open: HTTP status was '404 Not Found'
> Warning in download.packages(pkgs, destdir = tmpd, available = available,  :
>   download of package ‘IRanges’ failed
> trying URL '
> http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/GenomicRanges_1.6.6.zip
> '
> Error in download.file(url, destfile, method, mode = "wb", ...) :
>   cannot open URL '
> http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/GenomicRanges_1.6.6.zip
> '
> In addition: Warning message:
> In download.file(url, destfile, method, mode = "wb", ...) :
>   cannot open: HTTP status was '404 Not Found'
> Warning in download.packages(pkgs, destdir = tmpd, available = available,  :
>   download of package ‘GenomicRanges’ failed
> 
> 
> 
> > sessionInfo()
> R version 2.14.1 (2011-12-22)
> Platform: i386-pc-mingw32/i386 (32-bit)
> 
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252 LC_NUMERIC=C
> 
> [5] LC_TIME=English_United States.1252
> 
> attached base packages:
> [1] splines   stats     graphics  grDevices utils     datasets  methods
> base
> 
> other attached packages:
>  [1] Biostrings_2.22.0   locfit_1.5-6        lattice_0.20-0
>  akima_0.5-7         DEXSeq_1.0.2        Biobase_2.14.0
>  GenomicRanges_1.6.7 IRanges_1.12.6
>  [9] edgeR_2.4.3         limma_3.10.2        BiocInstaller_1.2.1
> 
> loaded via a namespace (and not attached):
> [1] bitops_1.0-4.1 grid_2.14.1    hwriter_1.3    plyr_1.7.1
> statmod_1.4.14 stringr_0.6    tools_2.14.1   zlibbioc_1.0.0
> 
> Many thanks
> 
> 	[[alternative HTML version deleted]]
> 
> 
> 
> ------------------------------
> 
> Message: 9
> Date: Sat, 18 Feb 2012 16:38:35 -0800
> From: Martin Morgan <mtmorgan@fhcrc.org>
> To: Alpesh Querer <alpeshq@gmail.com>
> Cc: bioconductor@r-project.org
> Subject: Re: [BioC] Installation errors for DESeq, Genomic Ranges etc.
> Message-ID: <4F40448B.7010805@fhcrc.org>
> Content-Type: text/plain; charset=windows-1252; format=flowed
> 
> On 02/18/2012 04:18 PM, Alpesh Querer wrote:
> > Hi all,
> >
> > I`m trying to install some packages like DESeq, GenomicRanges etc with R
> > 2.14.
> > I am getting error like the following. Same with dependent packages like
> > RCurl, rtracklayer.
> > Can someone help me with correct installation instructions here?
> >
> >
> >>      source("http://www.bioconductor.org/biocLite.R")
> >>      biocLite("GenomicRanges")
> > BioC_mirror: 'http://www.bioconductor.org'
> > Using R version 2.14, BiocInstaller version 1.2.1.
> > Installing package(s) 'GenomicRanges'
> > also installing the dependency ?IRanges?
> >
> > trying URL '
> > http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/IRanges_1.12.5.zip
> > '
> > Error in download.file(url, destfile, method, mode = "wb", ...) :
> >    cannot open URL '
> > http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/IRanges_1.12.5.zip
> 
> I'm not sure exactly why you're seeing this, but the problem is that the 
> current version of IRanges is 1.12.6, but biocLite is pointing you 
> toward 1.12.5. I would suggest starting a new R session and trying 
> again. In particular, open a DOS prompt and run
> 
>    Rgui --vanilla
> 
> perhaps proving the full path to Rgui, along the lines of
> 
>    "c:\Program Files\R\R-2.14.1\bin\i386\Rgui.exe" --vanilla
> 
> Martin
> 
> > '
> > In addition: Warning message:
> > In download.file(url, destfile, method, mode = "wb", ...) :
> >    cannot open: HTTP status was '404 Not Found'
> > Warning in download.packages(pkgs, destdir = tmpd, available = available,  :
> >    download of package ?IRanges? failed
> > trying URL '
> > http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/GenomicRanges_1.6.6.zip
> > '
> > Error in download.file(url, destfile, method, mode = "wb", ...) :
> >    cannot open URL '
> > http://www.bioconductor.org/packages/2.9/bioc/bin/windows/contrib/2.14/GenomicRanges_1.6.6.zip
> > '
> > In addition: Warning message:
> > In download.file(url, destfile, method, mode = "wb", ...) :
> >    cannot open: HTTP status was '404 Not Found'
> > Warning in download.packages(pkgs, destdir = tmpd, available = available,  :
> >    download of package ?GenomicRanges? failed
> >
> >
> >
> >> sessionInfo()
> > R version 2.14.1 (2011-12-22)
> > Platform: i386-pc-mingw32/i386 (32-bit)
> >
> > locale:
> > [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> > States.1252    LC_MONETARY=English_United States.1252 LC_NUMERIC=C
> >
> > [5] LC_TIME=English_United States.1252
> >
> > attached base packages:
> > [1] splines   stats     graphics  grDevices utils     datasets  methods
> > base
> >
> > other attached packages:
> >   [1] Biostrings_2.22.0   locfit_1.5-6        lattice_0.20-0
> >   akima_0.5-7         DEXSeq_1.0.2        Biobase_2.14.0
> >   GenomicRanges_1.6.7 IRanges_1.12.6
> >   [9] edgeR_2.4.3         limma_3.10.2        BiocInstaller_1.2.1
> >
> > loaded via a namespace (and not attached):
> > [1] bitops_1.0-4.1 grid_2.14.1    hwriter_1.3    plyr_1.7.1
> > statmod_1.4.14 stringr_0.6    tools_2.14.1   zlibbioc_1.0.0
> >
> > Many thanks
> >
> > 	[[alternative HTML version deleted]]
> >
> >
> >
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> 
> -- 
> Computational Biology
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109
> 
> Location: M1-B861
> Telephone: 206 667-2793
> 
> 
> 
> ------------------------------
> 
> Message: 10
> Date: Sat, 18 Feb 2012 20:17:55 -0800
> From: Brian James Gadd <bgadd001@student.ucr.edu>
> To: bioconductor@r-project.org
> Subject: [BioC] ChIPpeakAnno
> Message-ID:
> 	<CAAALKFMRsOh31+eypjSjhN-8trquBsxukAhhr0QOOuhJQbtaaA@mail.gmail.com>
> Content-Type: text/plain; charset=windows-1252
> 
> I am relatively new to R and am trying to use ChIPpeakAnno.  I am
> having some difficulty getting the ChIPpeakAnno library to load
> correctly. The annotatePeakInBatch function still runs but fails to
> properly annotate genes on the minus strand.
> 
> Specifically, upon loading the ChIPpeakAnno library a variety of
> objects are masked from several attached packages as detailed in the
> printout below.  The sessionInfo() print out is below as well.
> 
> Any advice on how to ensure everything loads correctly would be
> greatly appreciated; I haven't been able to find anything after
> scouring bioconductor, CRAN, or Google and I'm at a loss.  Thanks!
> 
> > library(ChIPpeakAnno)
> Loading required package: biomaRt
> Loading required package: multtest
> Loading required package: Biobase
> 
> Welcome to Bioconductor
> 
>   Vignettes contain introductory material. To view, type
>   'browseVignettes()'. To cite Bioconductor, see
>   'citation("Biobase")' and for packages 'citation("pkgname")'.
> 
> Loading required package: IRanges
> 
> Attaching package: ?IRanges?
> 
> The following object(s) are masked from ?package:Biobase?:
> 
>     updateObject
> 
> The following object(s) are masked from ?package:base?:
> 
>     cbind, eval, intersect, Map, mapply, order, paste, pmax, pmax.int,
> pmin, pmin.int, rbind, rep.int, setdiff, table, union
> 
> Loading required package: Biostrings
> Loading required package: BSgenome
> Loading required package: GenomicRanges
> Loading required package: BSgenome.Ecoli.NCBI.20080805
> Loading required package: GO.db
> Loading required package: AnnotationDbi
> Loading required package: DBI
> 
> Loading required package: org.Hs.eg.db
> 
> Loading required package: limma
> Loading required package: gplots
> Loading required package: gtools
> Loading required package: gdata
> gdata: Unable to locate valid perl interpreter
> gdata:
> gdata: read.xls() will be unable to read Excel XLS and XLSX files
> unless the 'perl=' argument is used to specify the location of a valid
> perl
> gdata: intrpreter.
> gdata:
> gdata: (To avoid display of this message in the future, please ensure
> perl is installed and available on the executable search path.)
> gdata: Unable to load perl libaries needed by read.xls()
> gdata: to support 'XLX' (Excel 97-2004) files.
> 
> gdata: Unable to load perl libaries needed by read.xls()
> gdata: to support 'XLSX' (Excel 2007+) files.
> 
> gdata: Run the function 'installXLSXsupport()'
> gdata: to automatically download and install the perl
> gdata: libaries needed to support Excel XLS and XLSX formats.
> 
> Attaching package: ?gdata?
> 
> The following object(s) are masked from ?package:IRanges?:
> 
>     trim
> 
> The following object(s) are masked from ?package:Biobase?:
> 
>     combine
> 
> The following object(s) are masked from ?package:stats?:
> 
>     nobs
> 
> The following object(s) are masked from ?package:utils?:
> 
>     object.size
> 
> Loading required package: caTools
> Loading required package: bitops
> 
> Attaching package: ?caTools?
> 
> The following object(s) are masked from ?package:IRanges?:
> 
>     runmean
> 
> Loading required package: grid
> Loading required package: KernSmooth
> KernSmooth 2.23 loaded
> Copyright M. P. Wand 1997-2009
> 
> Attaching package: ?gplots?
> 
> The following object(s) are masked from ?package:IRanges?:
> 
>     space
> 
> The following object(s) are masked from ?package:multtest?:
> 
>     wapply
> 
> The following object(s) are masked from ?package:stats?:
> 
>     lowess
> 
> Warning message:
> replacing previous import ?space? when loading ?IRanges?
> >
> 
> > sessionInfo()
> R version 2.14.1 (2011-12-22)
> Platform: i386-pc-mingw32/i386 (32-bit)
> 
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252 LC_NUMERIC=C
> [5] LC_TIME=English_United States.1252
> 
> attached base packages:
> [1] grid      stats     graphics  grDevices utils     datasets
> methods   base
> 
> other attached packages:
>  [1] ChIPpeakAnno_2.2.0                  gplots_2.10.1
>       KernSmooth_2.23-7                   caTools_1.12
>  [5] bitops_1.0-4.1                      gdata_2.8.2
>       gtools_2.6.2                        limma_3.10.2
>  [9] org.Hs.eg.db_2.6.4                  GO.db_2.6.1
>       RSQLite_0.11.1                      DBI_0.2-5
> [13] AnnotationDbi_1.16.16
> BSgenome.Ecoli.NCBI.20080805_1.3.17 BSgenome_1.22.0
>  GenomicRanges_1.6.7
> [17] Biostrings_2.22.0                   IRanges_1.12.6
>       multtest_2.10.0                     Biobase_2.14.0
> [21] biomaRt_2.10.0                      BiocInstaller_1.2.1
> 
> loaded via a namespace (and not attached):
> [1] MASS_7.3-17      RCurl_1.91-1.1   splines_2.14.1
> survival_2.36-12 tools_2.14.1     XML_3.9-4.1
> 
> 
> 
> I attempted this after getting R and bioconductor up and running first:
> Fresh Install of R 2.14.1 in Windows 7.
> Assigned write permission to the library folder to allow package installation.
> source("http://bioconductor.org/biocLite.R")
> biocLite()
> biocLite(c("ChIPpeakAnno"))
> update.packages(repos=biocinstallRepos(), ask=FALSE)
> 
> 
> ----
> Brian Gadd
> PhD Candidate
> Cell, Molecular and Developmental Biology
> University of California Riverside
> 
> Tel: 19513130505
> Email: bgadd001@student.ucr.edu
> 
> 
> 
> ------------------------------
> 
> Message: 11
> Date: Sun, 19 Feb 2012 21:20:46 +1100 (AUS Eastern Daylight Time)
> From: Gordon K Smyth <smyth@wehi.EDU.AU>
> To: Jabez Wilson <jabezwuk@yahoo.co.uk>
> Cc: Bioconductor mailing list <bioconductor@r-project.org>
> Subject: [BioC] use of duplicateCorrelation in Limma with agilent
> 	one-color	arrays
> Message-ID: <Pine.WNT.4.64.1202192113210.5448@PC765.wehi.edu.au>
> Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
> 
> Dear Jabez,
> 
> There a number of strange things about your code, which seem to be to do 
> with trying to work around storing single color data in a 2-color data 
> object.  Could you please use read.maimages() with green.only=TRUE, so 
> that the data is read into a single color data object.  None of the 
> work-around will then be necessary.
> 
> You might also try computing the duplicate correlation without averaging 
> replicate probes.
> 
> Best wishes
> Gordon
> 
> > Date: Fri, 17 Feb 2012 12:14:23 +0000 (GMT)
> > From: Jabez Wilson <jabezwuk@yahoo.co.uk>
> > To: bioconductor@r-project.org
> > Subject: [BioC] use of duplicateCorrelation in Limma with agilent
> > 	one-color	arrays
> > Message-ID:
> > 	<1329480863.2527.YahooMailClassic@web132503.mail.ird.yahoo.com>
> > Content-Type: text/plain; charset=iso-8859-1
> >
> > Hi, everyone, I am using limma to analyse an agilent one-color array experiment, and have run into difficulties with duplicateCorrelation.
> > My experiment is as follows: single color agilent arrays, 4 WT samples, and 3 samples of each of 4 treatment (treatments 1-4). I also have technical replicates (replicated once) for each sample. There are therefore 32 files. The targets file looks like this:
> > ?
> > ?? SampleNumber????????????????? FileName Condition Notes
> > 1???????????? 1? RH_02_1_77_Oct11_1_1.txt??? Treat1?? 1.1
> > 2???????????? 2? RH_02_1_77_Oct11_2_1.txt??? Treat1?? 1.2
> > 3???????????? 3? RH_04_1_77_Oct11_1_1.txt??? Treat1?? 2.1
> > 4???????????? 4? RH_07_1_77_Oct11_1_1.txt??? Treat1?? 2.2
> > 5???????????? 5? RH_04_1_77_Oct11_1_2.txt??? Treat1?? 3.1
> > 6???????????? 6? RH_07_1_77_Oct11_1_2.txt??? Treat1?? 3.2
> > 7???????????? 7? RH_04_1_77_Oct11_1_3.txt??? Treat2?? 4.1
> > 8???????????? 8? RH_07_1_77_Oct11_1_3.txt??? Treat2?? 4.2
> > 9???????????? 9? RH_04_1_77_Oct11_1_4.txt??? Treat2?? 5.1
> > 10?????????? 10? RH_07_1_77_Oct11_1_4.txt??? Treat2?? 5.2
> > 11?????????? 11? RH_04_1_77_Oct11_2_1.txt??? Treat2?? 6.1
> > 12?????????? 12? RH_07_1_77_Oct11_2_1.txt??? Treat2?? 6.2
> > 13?????????? 13 US0_05_1_77_Oct11_1_1.txt??? Treat3?? 7.1
> > 14?????????? 14? RH_01_1_77_Oct11_1_1.txt??? Treat3?? 7.2
> > 15?????????? 15 US0_05_1_77_Oct11_1_2.txt??? Treat3?? 8.1
> > 16?????????? 16? RH_01_1_77_Oct11_1_4.txt??? Treat3?? 8.2
> > 17?????????? 17? RH_02_1_77_Oct11_1_2.txt??? Treat3?? 9.1
> > 18?????????? 18? RH_02_1_77_Oct11_2_2.txt??? Treat3?? 9.2
> > 19?????????? 19 US0_05_1_77_Oct11_1_3.txt??? Treat4? 10.1
> > 20?????????? 20? RH_01_1_77_Oct11_1_2.txt??? Treat4? 10.2
> > 21?????????? 21 US0_05_1_77_Oct11_1_4.txt??? Treat4? 11.1
> > 22?????????? 22? RH_01_1_77_Oct11_1_3.txt??? Treat4? 11.2
> > 23?????????? 23? RH_04_1_77_Oct11_2_2.txt??? Treat4? 12.1
> > 24?????????? 24? RH_07_1_77_Oct11_2_2.txt??? Treat4? 12.2
> > 25?????????? 25 US0_05_1_77_Oct11_2_1.txt??????? WT? 13.1
> > 26?????????? 26? RH_01_1_77_Oct11_2_1.txt??????? WT? 13.2
> > 27?????????? 27 US0_05_1_77_Oct11_2_2.txt??????? WT? 14.1
> > 28?????????? 28? RH_01_1_77_Oct11_2_2.txt??????? WT? 14.2
> > 29?????????? 29 US0_05_1_77_Oct11_2_3.txt??????? WT? 15.1
> > 30?????????? 30? RH_01_1_77_Oct11_2_3.txt??????? WT? 15.2
> > 31?????????? 31 US0_05_1_77_Oct11_2_4.txt??????? WT? 16.1
> > 32?????????? 32? RH_01_1_77_Oct11_2_4.txt??????? WT? 16.2
> >
> > I run the following commands to process the data and create the design:
> > RG <- read.maimages(targets, columns = list(G = "gMedianSignal", Gb = "gBGMedianSignal", R = "gProcessedSignal",Rb = "gIsPosAndSignif"), annotation = c("Row", "Col","FeatureNum","ControlType","ProbeName"))
> > RG <- backgroundCorrect(RG, method="normexp", offset=1)
> > E <- normalizeBetweenArrays(RG, method="Aquantile")
> > E.avg <- avereps(E, ID=E$genes$ProbeName)
> > f <- factor(targets$Condition, levels = unique(targets$Condition))
> > design <- model.matrix(~0 + f)
> > colnames(design) <- levels(f)
> > ?
> > The problem arises when I do the duplicateCorrelation.
> > biolrep <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,15,16,16)
> > corfit <- duplicateCorrelation(E.avg, design, ndups=1,block=biolrep)
> > fit <- lmFit(E.avg$A, design, block=biolrep, cor=corfit$consensus.correlation)
> > contrast.matrix <- makeContrasts("Treat1-WT",levels=design)????????????????????????????????????????????????????????????fit2 <- contrasts.fit(fit, contrast.matrix)
> > fit2 <- eBayes(fit2)
> > topTable(fit2, adjust="BH", coef="Treat1-WT", genelist=E.avg$genes, number=10)
> >
> > Whereas I would expect the corfit$consensus.correlation to be generally very positive, I get the value 0.01385223.?Does anyone have any suggestions? Any help would be appreciated
> > ?
> > Jabez
> >
> 
> ______________________________________________________________________
> The information in this email is confidential and intend...{{dropped:4}}
> 
> 
> 
> ------------------------------
> 
> Message: 12
> Date: Sun, 19 Feb 2012 21:27:39 +1100 (AUS Eastern Daylight Time)
> From: Gordon K Smyth <smyth@wehi.EDU.AU>
> To: Lina Weber <linamweb@googlemail.com>
> Cc: Bioconductor mailing list <bioconductor@r-project.org>
> Subject: [BioC] edgeR: GLM - adjusting for unwanted effect?!
> Message-ID: <Pine.WNT.4.64.1202192121260.5448@PC765.wehi.edu.au>
> Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
> 
> Dear Lina,
> 
> Responses interpolated below.
> 
> > Date: Fri, 17 Feb 2012 12:56:13 +0100
> > From: Lina Weber <linamweb@googlemail.com>
> > To: bioconductor@r-project.org
> > Subject: [BioC] edgeR: GLM - adjusting for unwanted effect?!
> >
> > Dear all,
> >
> > I have an RNA-seq experiment where I want to test for differential
> > expression in response to my applied treatment. As biological replicates I
> > have two different genotypes of my clonal species, which were each exposed
> > to treated and untreated conditions.
> >
> > The straight forward way to test for a treatment effect would therefore be:
> >
> > genotype <- as.factor(c("g1","g1","g2","g2"))
> > treat <- as.factor(c("U","T","U","T"))
> >
> > design <- model.matrix(~treat)
> > design
> >  (Intercept) treatU
> > 1           1      1
> > 2           1      0
> > 3           1      1
> > 4           1      0
> >
> > ...
> >
> > lrt.tagd_treat <- glmLRT(D, glmfit.tagd, coef=2)
> > topTags(lrt.tagd_treat)
> >
> > However, when I am looking at the MDS plot of my 4 samples I can see the
> > the effect of the genotype is also not neglectable. Would it therefore make
> > sense to include the factor genotype in the design matrix as well to adjust
> > for the genotype effect in my model, e.g.:
> >
> > design <- model.matrix(~treat+genotype)
> > design
> >  (Intercept) treatU genotypeg2
> > 1           1      1          0
> > 2           1      0          0
> > 3           1      1          1
> > 4           1      0          1
> >
> > ...
> >
> > lrt.tagd_treat <- glmLRT(D, glmfit.tagd, coef=2)
> > topTags(lrt.tagd_treat)
> >
> > 1) Is an adjustment for an "unwanted effect" possible this way in general?
> 
> Yes, this a standard technique.
> 
> > 2) Does it also make sense in my case regarding the very low level of biol.
> > replication (-> 2)?
> 
> You have one degree of freedom left over for estimating the dispersion. 
> It's the minimum, and it would be better to have more, but it is usable.
> 
> Best wishes
> Gordon
> 
> > Thanks a lot,
> > Lina
> >
> 
> ______________________________________________________________________
> The information in this email is confidential and intend...{{dropped:4}}
> 
> 
> 
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> End of Bioconductor Digest, Vol 108, Issue 19
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