CreatingExpressionSets.R

tmp <- scan(what=character(0)) 56071 1052 1062 3061 3081 8052 8072 10061 10062 10072 1415670_at 8.430148 8.899385 8.625973 8.708319 8.759182 8.281378 8.905347 8.625347 9.029528 1415671_at 9.039655 9.244914 9.121714 9.002296 8.97237 8.599152 9.004381 9.267188 9.115415 1415672_at 8.86041 8.998826 9.077138 8.994297 8.885136 8.918512 9.087072 8.867808 8.841663 1415673_at 6.565344 6.384893 6.856466 6.17951 5.786523 6.507357 6.371563 5.886887 6.42499 1415674_a_at 7.877212 8.038635 8.120319 8.067843 7.56546 7.846677 7.921398 7.629843 7.787807 1415675_at 7.524559 7.496189 7.718928 7.164805 7.102158 7.331314 7.226036 7.424044 7.368011 1415676_a_at 9.315694 9.134394 9.224642 8.821193 8.886963 8.702572 8.883647 9.028728 8.921372 ""

1. Get and remove first 10 observations (look like slide IDs)

SlideIDs <- LETTERS[1:9] tmp <- tmp[-(1:10)]

1. Index and get the annotation

IDindex <- seq(1,length(tmp), by=10) probeIDs <- tmp[ IDindex ]

1. Construct a matrix of expressions

expressions <- matrix(as.numeric(tmp[(!seq(tmp)%in%IDindex)]), nc=10-1, byrow=TRUE)

1. Check names ok

rownames(expressions) <- probeIDs

pd <- new("phenoData",

          pData=data.frame(Slide=1:(10-1), row.names=SlideIDs),
          varLabels=list(Slide="Slide identifiers"))

eset <- new("exprSet", phenoData=pd, exprs=expressions) phenoData(eset)

1. Reply email:
2. swang <swang2000@gmail.com> writes:

1. > Dear List:
2. >
3. > I got a file like the following, I guess the data is M ( log2 expression
4. > ratio) from microarray:
5. >
6. > 56071 1052 1062 3061 3081 8052 8072 10061 10062 10072 1415670_at
7. > 8.430148 8.899385 8.625973 8.708319 8.759182 8.281378 8.905347 8.625347

1. > the rows are Affymetrix probe and columns are different mice number (arrays)
2. > I need to do a category analysis using category package, so I need to
3. > generate a MAList or ExprSet object.

1. Starting with a data matrix

samples <- 3 sampleNames <- letters[1:samples] features <- 10

1. 1. raw data

exprMatrix <- matrix(0, ncol=samples,

                    nrow=features,
                    dimnames=list(1:features, sampleNames))

1. To create an old-style exprSet (not sure what an ExprSet is, or which
2. package you mean by Category ;):

1. 1. phenoData for exprSet

pd2 <- new("phenoData",

          pData=data.frame(1:samples,
            row.names=sampleNames),
          varLabels=list(id="sample identifier"))

new("exprSet",

   phenoData=pd2,
   exprs=exprMatrix)

1. To create an ExpressionSet (using this will require different commands
2. from the vignette that comes with Category) object:

> ## phenoData for ExpressionSet pd1 <- new("AnnotatedDataFrame",

          data=
          data.frame(sampleId=1:samples,
                     row.names=sampleNames),
          varMetadata=
          data.frame(labelDescription=I(c("Sample numeric identifier")),
                     row.names=c("sampleId")))

new("ExpressionSet",

   phenoData=pd1, exprs=exprMatrix)

1. Much of the functionality of exprSet and ExpressionSet come from
2. associating phenoData with expression values; the skeletons above do
3. not have any meaningful phenoData. Typically you might incorporate
4. this by reading phenotypic data from a spreadsheet or tab-delimited
5. file (e.g., using read.table) into data.frames, and then incorporating
6. the data.frame into an ExpressionSet as outlined above.

1. sessionInfo()

Version 2.3.1 Patched (2006-06-20 r38364) x86_64-unknown-linux-gnu

attached base packages: [1] "tools" "methods" "stats" "graphics" "grDevices" "utils" [7] "datasets" "base"

other attached packages:

Biobase 

"1.10.1"

Martin -- Bioconductor

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