Hi Jose,

I am not in the office this week, so I don't have easy access to a computer
running R.

I don't see anything obviously wrong with your code, and as you note you
can get results using annaffy directly.

Internally limma2annaffy is just getting an index of  significant probesets
and then getting the IDs by

probeids <- featureNames(eset_filt)[index]

So you might check to see that you don't have any NA values for the
probeset IDs. Not sure if that is the problem, but worth checking I suppose.

Lately I have been working away from annaffy and towards ReportingTools,
which is still actively maintained and a bit nicer for the end user. It's
not that fully integrated, but see for example ?vennPage for html Venn
diagrams with clickable links. To replace limma2annaffy I use the internal
function tableFilt.

Something like
library(ReportingTools)
eb$genes = select(hugene20sttranscriptcluster.db, feature names(eset_filt),
c("SYMBOL","ENTREZID","GENENAME")) #add more if you like
tab = lapply(1:ncol(contrast), function(x) affycoretools:::tableFilt(eb, x,
pfilt=0.05, fldfilt=0.585))
out =lapply(seq(along=tab), function(x) {
htab = HTMLReport(gsub(" ", "_", colnames(contrast)[x]),
colnames(contrast)[x], reportDirectory = "./reports")
publish(tab[x], htab, .modifyDF = list(affyLinks, entrezLinks))
finish(htab)
#you can use write.table here to output text version
return(htab)
})

Note this is not tested and was typed on a tablet, so there may well be
some autocorrect errors in there.

Then

indx = HTMLReport("index", "Put a header here")
publish(Link(out, indx), indx)
finish(indx)

Which will give you a nice HTML page with links to each table. The vennPage
function is also intended to be passed in via the publish() step in that
last code block, so you can have a single index page that acts as a point
of entry.

Also note that ReportingTools will also accept an MarrayLM object as input
and will create an HTML page. See the vignettes for that package. I just
don't really like the stock output you get, so have rolled my own.

Best,

Jim

Dear Jim,

I am performing differential expression analysis using affymetrix human
gene 2.0 ST arrays. I get an error message from limma2annaffy() function.

Error in .checkKeysAreWellFormed(keys) :
  keys must be supplied in a character vector with no NAs

I get the annotated HTML and txt tables for each desired contrast by using
topTable and annaffy tools but I would like to know what is going wrong
with limma2annaffy since I find this function truly useful and convenient.

Thank you in advance for your time and your help,

 Jose

 -- output of sessionInfo():


R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin10.8.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

[R.app GUI 1.62 (6558) x86_64-apple-darwin10.8.0]

[History restored from /Users/Jose/.Rapp.history]

> setwd("/Users/Jose/Documents/Oscar/MDA_261213/MDA_271213")
> library(limma)
> library(oligo)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‚ÄòBiocGenerics‚Äô

The following objects are masked from ‚Äòpackage:parallel‚Äô:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport,
clusterMap,
    parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply,
parSapplyLB

The following object is masked from ‚Äòpackage:limma‚Äô:

    plotMA

The following object is masked from ‚Äòpackage:stats‚Äô:

    xtabs

The following objects are masked from ‚Äòpackage:base‚Äô:

    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,
duplicated, eval, evalq,
    Filter, Find, get, intersect, is.unsorted, lapply, Map, mapply, match,
mget, order, paste,
    pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int,
rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unlist

Loading required package: oligoClasses
Welcome to oligoClasses version 1.24.0
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'.
To cite
    Bioconductor, see 'citation("Biobase")', and for packages
'citation("pkgname")'.

============================================================================================================
Welcome to oligo version 1.26.0
============================================================================================================

Attaching package: ‚Äòoligo‚Äô

The following object is masked from ‚Äòpackage:BiocGenerics‚Äô:

    normalize

The following object is masked from ‚Äòpackage:limma‚Äô:

    backgroundCorrect

> Data<-read.celfiles(list.celfiles())
Loading required package: pd.hugene.2.0.st
Loading required package: RSQLite
Loading required package: DBI
Platform design info loaded.
Reading in : OC_MDAC1_(HuGene-2_0-st).CEL
Reading in : OC_MDAC2_(HuGene-2_0-st).CEL
Reading in : OC_MDAC3_(HuGene-2_0-st).CEL
Reading in : OC_MDAP+T1_(HuGene-2_0-st).CEL
Reading in : OC_MDAP+T2_(HuGene-2_0-st).CEL
Reading in : OC_MDAP+T3_(HuGene-2_0-st).CEL
Reading in : OC_MDAP1_(HuGene-2_0-st).CEL
Reading in : OC_MDAP2_(HuGene-2_0-st).CEL
Reading in : OC_MDAP3_(HuGene-2_0-st).CEL
Reading in : OC_MDAT1_(HuGene-2_0-st).CEL
Reading in : OC_MDAT2_(HuGene-2_0-st).CEL
Reading in : OC_MDAT3_(HuGene-2_0-st).CEL
> Data
GeneFeatureSet (storageMode: lockedEnvironment)
assayData: 2598544 features, 12 samples
  element names: exprs
protocolData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.hugene.2.0.st
> eset<-rma(Data)
Background correcting
Normalizing
Calculating Expression
> eset
ExpressionSet (storageMode: lockedEnvironment)
assayData: 53617 features, 12 samples
  element names: exprs
protocolData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.hugene.2.0.st
> library(affycoretools)
Loading required package: affy

Attaching package: ‚Äòaffy‚Äô

The following objects are masked from ‚Äòpackage:oligo‚Äô:

    intensity, MAplot, mm, mm<-, mmindex, pm, pm<-, pmindex, probeNames, rma

The following object is masked from ‚Äòpackage:oligoClasses‚Äô:

    list.celfiles

Loading required package: GO.db
Loading required package: AnnotationDbi

Loading required package: KEGG.db

KEGG.db contains mappings based on older data because the original resource
was removed from the
  the public domain before the most recent update was produced. This
package should now be
  considered deprecated and future versions of Bioconductor may not have it
available.  Users
  who want more current data are encouraged to look at the KEGGREST or
reactome.db packages

KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> eset_m <- getMainProbes(eset)
> eset_m
ExpressionSet (storageMode: lockedEnvironment)
assayData: 46255 features, 12 samples
  element names: exprs
protocolData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.hugene.2.0.st

## getMainProbes subsets main (44629) but also normgene->exon (1626) probes
from eset…

> library("hugene20sttranscriptcluster.db")
Loading required package: org.Hs.eg.db

> annotation(eset_m) <- "hugene20sttranscriptcluster.db"
> eset_m
ExpressionSet (storageMode: lockedEnvironment)
assayData: 46255 features, 12 samples
  element names: exprs
protocolData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hugene20sttranscriptcluster.db
> library(arrayQualityMetrics)
> arrayQualityMetrics(expressionset=eset, outdir="QC normalized",
force=TRUE)
The directory 'QC normalized' has been created.
Error in tmp[i] : invalid subscript type 'list'
> hist(eset_m)
> dev.copy2eps(file="eset_m.eps")
quartz
     2
> library(genefilter)
> f1 = kOverA(3,2)
> filt = filterfun(f1)
> index = genefilter(eset_m,filt)
> eset_filt = eset_m[index,]
> dim(eset_filt)
Features  Samples
   45290       12
> dim(eset_m)
Features  Samples
   46255       12
> eset_filt
ExpressionSet (storageMode: lockedEnvironment)
assayData: 45290 features, 12 samples
  element names: exprs
protocolData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: OC_MDAC1_(HuGene-2_0-st).CEL OC_MDAC2_(HuGene-2_0-st).CEL ...
    OC_MDAT3_(HuGene-2_0-st).CEL (12 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hugene20sttranscriptcluster.db
> hist(eset_filt)
> dev.copy2eps(file="eset_filt.eps")
quartz
     2
> pData(eset_filt)
                               index
OC_MDAC1_(HuGene-2_0-st).CEL       1
OC_MDAC2_(HuGene-2_0-st).CEL       2
OC_MDAC3_(HuGene-2_0-st).CEL       3
OC_MDAP+T1_(HuGene-2_0-st).CEL     4
OC_MDAP+T2_(HuGene-2_0-st).CEL     5
OC_MDAP+T3_(HuGene-2_0-st).CEL     6
OC_MDAP1_(HuGene-2_0-st).CEL       7
OC_MDAP2_(HuGene-2_0-st).CEL       8
OC_MDAP3_(HuGene-2_0-st).CEL       9
OC_MDAT1_(HuGene-2_0-st).CEL      10
OC_MDAT2_(HuGene-2_0-st).CEL      11
OC_MDAT3_(HuGene-2_0-st).CEL      12
> label <- read.delim("label.txt")
> label
                            array index  label
1    OC_MDAC1_(HuGene-2_0-st).CEL     1  MDA_C
2    OC_MDAC2_(HuGene-2_0-st).CEL     2  MDA_C
3    OC_MDAC3_(HuGene-2_0-st).CEL     3  MDA_C
4  OC_MDAP+T1_(HuGene-2_0-st).CEL     4 MDA_PT
5  OC_MDAP+T2_(HuGene-2_0-st).CEL     5 MDA_PT
6  OC_MDAP+T3_(HuGene-2_0-st).CEL     6 MDA_PT
7    OC_MDAP1_(HuGene-2_0-st).CEL     7  MDA_P
8    OC_MDAP2_(HuGene-2_0-st).CEL     8  MDA_P
9    OC_MDAP3_(HuGene-2_0-st).CEL     9  MDA_P
10   OC_MDAT1_(HuGene-2_0-st).CEL    10  MDA_T
11   OC_MDAT2_(HuGene-2_0-st).CEL    11  MDA_T
12   OC_MDAT3_(HuGene-2_0-st).CEL    12  MDA_T
> plotPCA(eset_filt, groups=rep(1:4, each=3),
groupnames=unique(paste(label$label)))
> dev.copy2eps(file="pca.eps")
quartz
     2
> plotPCA(eset_filt, groups=rep(1:4, each=3),
groupnames=unique(paste(label$label)), screeplot=TRUE)
> dev.copy2eps(file="pca_scree.eps")
quartz
     2
> plotPCA(eset_filt, groups=rep(1:4, each=3),
groupnames=unique(paste(label$label)), squarepca=TRUE)
> dev.copy2eps(file="pca_sq.eps")
quartz
     2
> plotPCA(eset_filt, groups=rep(1:4, each=3),
groupnames=unique(paste(label$label)), squarepca=TRUE, plot3d=TRUE,
pcs=c(1,2,3))
Sometimes rgl doesn't plot the first time.
If there isn't anything in the plotting window, close it and re-run
plotPCA().
> rgl.postscript("pca_3d.eps","eps",drawText=TRUE)
> sampleNames(eset_filt)
 [1] "OC_MDAC1_(HuGene-2_0-st).CEL"   "OC_MDAC2_(HuGene-2_0-st).CEL"
"OC_MDAC3_(HuGene-2_0-st).CEL"
 [4] "OC_MDAP+T1_(HuGene-2_0-st).CEL" "OC_MDAP+T2_(HuGene-2_0-st).CEL"
"OC_MDAP+T3_(HuGene-2_0-st).CEL"
 [7] "OC_MDAP1_(HuGene-2_0-st).CEL"   "OC_MDAP2_(HuGene-2_0-st).CEL"
"OC_MDAP3_(HuGene-2_0-st).CEL"
[10] "OC_MDAT1_(HuGene-2_0-st).CEL"   "OC_MDAT2_(HuGene-2_0-st).CEL"
"OC_MDAT3_(HuGene-2_0-st).CEL"
> cond.factor <-
factor(c("MDAC","MDAC","MDAC","MDA_PT","MDA_PT","MDA_PT","MDA_P","MDA_P","MDA_P","MDA_T","MDA_T","MDA_T"),
levels = c("MDAC","MDA_PT","MDA_P","MDA_T"))
> design <- model.matrix(~0+cond.factor)
> colnames(design)<-c("MDAC","MDA_PT","MDA_P","MDA_T")
> design
   MDAC MDA_PT MDA_P MDA_T
1     1      0     0     0
2     1      0     0     0
3     1      0     0     0
4     0      1     0     0
5     0      1     0     0
6     0      1     0     0
7     0      0     1     0
8     0      0     1     0
9     0      0     1     0
10    0      0     0     1
11    0      0     0     1
12    0      0     0     1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$cond.factor
[1] "contr.treatment"

> lm <- lmFit(eset_filt, design)
> contrast.matrix <- makeContrasts(MDA_PT-MDAC, MDA_P-MDAC, MDA_T-MDAC,
levels=design)
> contrast.matrix
        Contrasts
Levels   MDA_PT - MDAC MDA_P - MDAC MDA_T - MDAC
  MDAC              -1           -1           -1
  MDA_PT             1            0            0
  MDA_P              0            1            0
  MDA_T              0            0            1
> fit2 <- contrasts.fit(lm, contrast.matrix)
> eb <- eBayes(fit2)
> results <- decideTests(eb)
> vc <- vennCounts2(results, method ="same")
> vennDiagram(vc, cex=0.8)
> dev.copy2eps(file="venn.eps")
quartz
     2
> vennSelect(eset_filt,design,results,contrast.matrix,eb)
>
limma2annaffy(eset_filt,eb,design,contrast.matrix,annotation(eset_filt),pfilt=0.05,fldfilt=0.585,save=TRUE)

You are going to output 3 tables,
With this many genes in each:
 2134 3415 275
Do you want to accept or change these values? [ a/c ]
a
Error in .checkKeysAreWellFormed(keys) :
  keys must be supplied in a character vector with no NAs

## gene numbers are ok but function does not work out further…

>
limma2annaffy(eset_filt,eb,design,contrast.matrix,annotation(eset_filt),number=10)

You are going to output 3 tables,
With this many genes in each:
 10 10 10
Do you want to accept or change these values? [ a/c ]
a
Error in .checkKeysAreWellFormed(keys) :
  keys must be supplied in a character vector with no NAs
>
limma2annaffy(eset_filt,eb,design,contrast.matrix,annotation(eset_filt),number=10)

You are going to output 3 tables,
With this many genes in each:
 10 10 10
Do you want to accept or change these values? [ a/c ]
c
Do you want to change the p-value? [ y/n ]
n
Do you want to change the fold filter? [ y/n ]
n

You are going to output 3 tables,
With this many genes in each:
 30 30 30
Do you want to accept or change these values? [ a/c ]
a
Error in .checkKeysAreWellFormed(keys) :
  keys must be supplied in a character vector with no NAs

## error with gene number modification (10 -> 30 (default setting)) and
function…

> fStat <- topTableF(eb, number=Inf)
> head(fStat)
         MDA_PT...MDAC MDA_P...MDAC MDA_T...MDAC  AveExpr        F
 P.Value    adj.P.Val
16859205     -1.286312   -4.2941076   0.01920662 9.660757 810.8735
1.102616e-14 4.993749e-10
16672452     -3.395777   -3.6027376  -3.30340265 5.170242 719.3912
2.336551e-14 5.291120e-10
16853028      5.559876    0.7256685   0.12395099 4.229877 662.0674
3.932584e-14 5.936891e-10
16833204      4.791469    0.4702840   0.61199749 6.136889 567.6096
1.031520e-13 1.167939e-09
17052685     -4.434038   -1.9167135  -1.44742803 8.153717 511.1014
1.988363e-13 1.801059e-09
17067314     -3.330336   -3.0812685  -0.35449045 5.620444 390.7517
1.064119e-12 7.861126e-09
> write.table(fStat,file="Fstat.txt",sep = "\t", row.names = TRUE)
> dim(fStat)
[1] 45290     7
> dim(eset_filt)
Features  Samples
   45290       12
> MDA_PT_sig <- topTable(eb, coef = 1, number = Inf, p.value =
0.05,lfc=0.585)
> dim(MDA_PT_sig) #2134
[1] 2134    6
> head(MDA_PT_sig)
             logFC  AveExpr         t      P.Value    adj.P.Val        B
17052685 -4.434038 8.153717 -38.39125 1.882904e-14 3.930583e-10 20.70057
16853028  5.559876 4.229877  38.06801 2.093170e-14 3.930583e-10 20.64453
16672452 -3.395777 5.170242 -37.41013 2.603610e-14 3.930583e-10 20.52750
16833204  4.791469 6.136889  36.18503 3.949201e-14 4.471483e-10 20.29845
16743091  2.715139 7.201918  27.99236 9.712218e-13 8.797327e-09 18.29467
17095194  2.547995 5.724030  27.07882 1.466936e-12 1.002695e-08 18.00646
> MDA_PT_sig$tcid <- rownames(MDA_PT_sig)
> head(MDA_PT_sig)
             logFC  AveExpr         t      P.Value    adj.P.Val        B
  tcid
17052685 -4.434038 8.153717 -38.39125 1.882904e-14 3.930583e-10 20.70057
17052685
16853028  5.559876 4.229877  38.06801 2.093170e-14 3.930583e-10 20.64453
16853028
16672452 -3.395777 5.170242 -37.41013 2.603610e-14 3.930583e-10 20.52750
16672452
16833204  4.791469 6.136889  36.18503 3.949201e-14 4.471483e-10 20.29845
16833204
16743091  2.715139 7.201918  27.99236 9.712218e-13 8.797327e-09 18.29467
16743091
17095194  2.547995 5.724030  27.07882 1.466936e-12 1.002695e-08 18.00646
17095194
> library(annaffy)
> aaf.handler()
 [1] "Probe"               "Symbol"              "Description"
"Chromosome"
 [5] "Chromosome Location" "GenBank"             "Gene"
 "Cytoband"
 [9] "UniGene"             "PubMed"              "Gene Ontology"
"Pathway"
> anncols <- aaf.handler()[c(1:3,6:7,9:12)]
> anntable <- aafTableAnn(MDA_PT_sig$tcid,
"hugene20sttranscriptcluster.db", anncols)
> head(MDA_PT_sig)
             logFC  AveExpr         t      P.Value    adj.P.Val        B
  tcid
17052685 -4.434038 8.153717 -38.39125 1.882904e-14 3.930583e-10 20.70057
17052685
16853028  5.559876 4.229877  38.06801 2.093170e-14 3.930583e-10 20.64453
16853028
16672452 -3.395777 5.170242 -37.41013 2.603610e-14 3.930583e-10 20.52750
16672452
16833204  4.791469 6.136889  36.18503 3.949201e-14 4.471483e-10 20.29845
16833204
16743091  2.715139 7.201918  27.99236 9.712218e-13 8.797327e-09 18.29467
16743091
17095194  2.547995 5.724030  27.07882 1.466936e-12 1.002695e-08 18.00646
17095194
> class(MDA_PT_sig)
[1] "data.frame"
> testtable <- aafTable(logFC = round(MDA_PT_sig$logFC,2),
tstat=round(MDA_PT_sig$t,1),adjPval =signif(MDA_PT_sig$adj.P.Val,1),
probeids= MDA_PT_sig$tcid)
> table <- merge(anntable,testtable)
> exprtable <- aafTableInt(eset_filt,probeids=MDA_PT_sig$tcid)
> tableF <- merge(table,exprtable)
> head(tableF)
An object of class "aafTable"
Slot "probeids":
[1] "17052685"

Slot "table":
$Probe
An object of class "aafList"
[[1]]
An object of class "aafProbe"
[1] "17052685"


$Symbol
An object of class "aafList"
[[1]]
character(0)
attr(,"class")
[1] "aafSymbol"


$Description
An object of class "aafList"
[[1]]
character(0)
attr(,"class")
[1] "aafDescription"


$GenBank
An object of class "aafList"
[[1]]
character(0)
attr(,"class")
[1] "aafGenBank"


$Gene
An object of class "aafList"
[[1]]
An object of class "aafLocusLink"
integer(0)


$UniGene
An object of class "aafList"
[[1]]
character(0)
attr(,"class")
[1] "aafUniGene"


$PubMed
An object of class "aafList"
[[1]]
An object of class "aafPubMed"
integer(0)


$`Gene Ontology`
An object of class "aafList"
[[1]]
An object of class "aafGO"
list()


$Pathway
An object of class "aafList"
[[1]]
An object of class "aafPathway"
list()


$logFC
An object of class "aafList"
[[1]]
[1] -4.43


$tstat
An object of class "aafList"
[[1]]
[1] -38.4


$adjPval
An object of class "aafList"
[[1]]
[1] 4e-10


$`OC_MDAC1_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 9.961219


$`OC_MDAC2_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 10.22801


$`OC_MDAC3_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 10.12056


$`OC_MDAP+T1_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 5.455098


$`OC_MDAP+T2_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 5.849167


$`OC_MDAP+T3_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 5.703406


$`OC_MDAP1_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.109914


$`OC_MDAP2_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.325627


$`OC_MDAP3_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.124103


$`OC_MDAT1_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.66443


$`OC_MDAT2_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.719271


$`OC_MDAT3_(HuGene-2_0-st).CEL`
An object of class "aafList"
[[1]]
An object of class "aafIntensity"
[1] 8.5838



> saveHTML(tableF,"tableF.html",title="MDA_PT pval<0.05 fc>1.5", open=TRUE)
> saveText(tableF,"table_MDA_PT.txt")

## idem for the other two contrast...

##volcanos and MA...
##heatmap...

> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] gplots_2.12.1                         annaffy_1.34.0
 [3] rgl_0.93.963                          genefilter_1.44.0
 [5] arrayQualityMetrics_3.18.0
 hugene20sttranscriptcluster.db_2.13.0
 [7] org.Hs.eg.db_2.10.1                   affycoretools_1.34.0
 [9] KEGG.db_2.10.1                        GO.db_2.10.1
[11] AnnotationDbi_1.24.0                  affy_1.40.0
[13] pd.hugene.2.0.st_3.8.0                RSQLite_0.11.4
[15] DBI_0.2-7                             oligo_1.26.0
[17] Biobase_2.22.0                        oligoClasses_1.24.0
[19] BiocGenerics_0.8.0                    limma_3.18.7

loaded via a namespace (and not attached):
 [1] affxparser_1.34.0       affyio_1.30.0           affyPLM_1.38.0
 annotate_1.40.0
 [5] AnnotationForge_1.4.4   beadarray_2.12.0        BeadDataPackR_1.14.0
 BiocInstaller_1.12.0
 [9] biomaRt_2.18.0          Biostrings_2.30.1       biovizBase_1.10.7
  bit_1.1-11
[13] bitops_1.0-6            BSgenome_1.30.0         Cairo_1.5-5
  Category_2.28.0
[17] caTools_1.16            cluster_1.14.4          codetools_0.2-8
  colorspace_1.2-4
[21] DESeq2_1.2.8            dichromat_2.0-0         digest_0.6.4
 edgeR_3.4.2
[25] ff_2.2-12               foreach_1.4.1           Formula_1.1-1
  gcrma_2.34.0
[29] gdata_2.13.2            GenomicFeatures_1.14.2  GenomicRanges_1.14.4
 ggbio_1.10.10
[33] ggplot2_0.9.3.1         GOstats_2.28.0          graph_1.40.1
 grid_3.0.2
[37] gridExtra_0.9.1         GSEABase_1.24.0         gtable_0.1.2
 gtools_3.1.1
[41] Hmisc_3.13-0            hwriter_1.3             IRanges_1.20.6
 iterators_1.0.6
[45] KernSmooth_2.23-10      labeling_0.2            lattice_0.20-24
  latticeExtra_0.6-26
[49] locfit_1.5-9.1          MASS_7.3-29             Matrix_1.1-0
 munsell_0.4.2
[53] PFAM.db_2.10.1          plyr_1.8                preprocessCore_1.24.0
  proto_0.3-10
[57] R.methodsS3_1.5.2       R.oo_1.15.8             R.utils_1.28.4
 R2HTML_2.2.1
[61] RBGL_1.38.0             RColorBrewer_1.0-5      Rcpp_0.10.6
  RcppArmadillo_0.3.930.1
[65] RCurl_1.95-4.1          ReportingTools_2.2.0    reshape2_1.2.2
 Rsamtools_1.14.2
[69] rtracklayer_1.22.0      scales_0.2.3            setRNG_2011.11-2
 splines_3.0.2
[73] stats4_3.0.2            stringr_0.6.2           survival_2.37-4
  SVGAnnotation_0.93-1
[77] tools_3.0.2             VariantAnnotation_1.8.8 vsn_3.30.0
 XML_3.95-0.2
[81] xtable_1.7-1            XVector_0.2.0           zlibbioc_1.8.0

--
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	[[alternative HTML version deleted]]

