[BioC] Biobase ExpressionSet: metadata on assayData
Martin Morgan
mtmorgan at fhcrc.org
Thu Dec 20 19:52:21 CET 2007
Hi Eric -- Glad to be of help.
I did not see your 'ps' in the original message; perahps the following
has been clarified, but in case not...
Are you sure you are using AnnotatedDataFrame as it is intended? As a
concrete example:
> library(Biobase)
> data(sample.ExpressionSet)
> dim(sample.ExpressionSet)
Features Samples
500 26
> phenoData(sample.ExpressionSet)
An object of class "AnnotatedDataFrame"
sampleNames: A, B, ..., Z (26 total)
varLabels and varMetadata description:
sex: Female/Male
type: Case/Control
score: Testing Score
> pData(sample.ExpressionSet)
sex type score
A Female Control 0.75
B Male Case 0.40
C Male Control 0.73
D Male Case 0.42
E Female Case 0.93
F Male Control 0.22
G Male Case 0.96
H Male Case 0.79
I Female Case 0.37
J Male Control 0.63
K Male Case 0.26
L Female Control 0.36
M Male Case 0.41
N Male Case 0.80
O Female Case 0.10
P Female Control 0.41
Q Female Case 0.16
R Male Control 0.72
S Male Case 0.17
T Female Case 0.74
U Male Control 0.35
V Female Control 0.77
W Male Control 0.27
X Male Control 0.98
Y Female Case 0.94
Z Female Case 0.32
> varMetadata(sample.ExpressionSet)
labelDescription
sex Female/Male
type Case/Control
score Testing Score
pData returns the data.frame describing phenotypes, varMetadata
returns the meta-data describing the columns of pData. It's not clear
from your example below what variables v1 through v5 are meant to
represent, but your 'meta-data'
>> treatment=c("D","192","233","192","233")
>> control=c(1,0,0,0,0)
>> dose=c(NA,30,10,10,0.3)
>> replicate=rep(1,5)
seems really to be meant (when appropriately rearranged) as components
of pData.
pData and varMetadata are defined to make access to the underyling
phenotypic data easy. The actual structure of the object is more
accurately represented by the function calls
> adf <- phenoData(sample.ExpressionSet) # phenotype AnnotatedDataFrame
> df <- pData(adf) # 'data' part of AnnotatedDataFrame
> md <- varMetadata(adf) # meta-data, of AnnotatedDataFrame
>From your function below, it looks like you're trying to select
columns of pData based on their varMetadata. I'm not sure whether
there is a strong use case of this, but here's a little example
> pData <- data.frame(X=1:5, Y=5:1, Z=letters[1:5])
> varMetadata <- data.frame(
+ labelDescription=c(
+ "X description", "Y description", "Z description"),
+ metaA=c(TRUE,TRUE,FALSE),
+ metaB=c(TRUE,FALSE,TRUE),
+ metaC=c("yes", "no", "no"))
> adf <- new("AnnotatedDataFrame",
+ data=pData, varMetadata=varMetadata)
For interactive use, I'd probably do something like
> idx <- with(varMetadata(adf), metaA & metaB)
> adf[,idx]
An object of class "AnnotatedDataFrame"
rowNames: 1, 2, ..., 5 (5 total)
varLabels and varMetadata description:
X: X description
additional varMetadata: metaA, metaB, metaC
(this could be written in a single line, e.g.,
adf[,varMetadata(adf)$metaA & varMetadata(adf)$megaB]
but such brevity both is less efficient and more confusing).
'with' is providing an easy way to access the variables in
varMetadata(adf). The second argument to 'with' can be a series of
statements of the form,
with(varMetadata(obj), { <your statements here...> })
Your goal seems to be to create complex selection criteria. For this
case I find it very useful to stick to the paradigm of constructing
logical vectors and using the vectorized logical operators &, | and t
If you really wanted to make this kind of operation into a function
call, I might
> adfMetaSelect <- function(adf, ..., how=all) {
+ dots <- match.call(expand.dots=FALSE)[["..."]]
+ res <- lapply(dots,
+ function(elt, vm) with(vm, eval(elt)),
+ vm=varMetadata(adf))
+ idx <- do.call(mapply, c(how, res))
+ adf[,idx]
+ }
> adfMetaSelect(adf, metaA, metaB)
An object of class "AnnotatedDataFrame"
rowNames: 1, 2, ..., 5 (5 total)
varLabels and varMetadata description:
X: X description
additional varMetadata: metaA, metaB, metaC
> adfMetaSelect(adf, metaA, !metaB)
An object of class "AnnotatedDataFrame"
rowNames: 1, 2, ..., 5 (5 total)
varLabels and varMetadata description:
Y: Y description
additional varMetadata: metaA, metaB, metaC
> adfMetaSelect(adf, metaC=="no")
An object of class "AnnotatedDataFrame"
rowNames: 1, 2, ..., 5 (5 total)
varLabels and varMetadata description:
Y: Y description
Z: Z description
additional varMetadata: metaA, metaB, metaC
The 'how' argument specifies how the logical conditions provided in
... will be combined, in this case all conditions must be
true. Probably the mapply could be replaced with how if 'how' were,
e.g., get("&").
Perhaps this provides you with some ideas.
Martin
"Eric Lecoutre" <ericlecoutre at gmail.com> writes:
> Hi Martin;
>
> With a little retard, thank for your detailed answer.
> I did some time to go on with my investigations and now things are more
> clear on what I should do with all those data (and mostly that I have to use
> phenotypic slot for my data on cell lines).
> There are nearly 100 cell lines used by my client, thus it is really worth
> using ExpressionSet structure for further analysis.
>
> Best wishes,
>
> Eric
>
>
>
> 2007/12/14, Eric Lecoutre <ericlecoutre at gmail.com>:
>>
>> Hi,
>>
>> I am new to Bioconductor and am studying both biobase and biostatistics
>> for a small project.
>> My client wants to know wether he should use ExpressionSet for part of its
>> assay R&D process.
>> For a experiment, I understand there is a lot of common metadata like
>> compound, dose level, replicate,...
>> I have seen phylo and feature dataframe class AnnotatedDataFrame and
>> already said to the client he could use that.
>> Fact is that those metadata (if I have weell understand) also could be
>> used for gene expression (so addayData).
>> What is the standard BioConductor way to handle those metadata? : there is
>> no metadata argument associated to assayData.
>> Should I use an AnnotatedDataFrame for feature repeting gene expression
>> with such metadata?
>>
>> btw, are there people here who really use ExpressionSet in their
>> processes?
>>
>> Thanks for any insight.
>>
>>
>> Eric
>>
>>
>> PS: as I looked at AnnotatedDataFrame class, I missed a helper function to
>> exploit metadata.
>> Here is such a little function and a sample use, where one requests for
>> variables in AnnotatedDataFrame with conditions on metadata (arbitrary ones,
>> handled by dots ...)
>>
>>
>>
>>
>> selectVariables <- function(x,logic=all,drop=FALSE,...){
>> listCriteria <- list(...)
>> metadata <- varMetadata(x)
>> retainedCriteria <- list()
>> sapply(names(listCriteria), function(critname) {
>> if(!critname %in% colnames(metadata)){
>> cat("\n Dropped criteria:",critname, "not in AnnotatedDataFrame\n")
>> }else{
>> if(is.null(listCriteria[critname])) listCriteria[[critname]]<-
>> unique(metadata[,critname])
>> retainedCriteria[[critname]] <<- metadata[,critname] %in%
>> listCriteria[critname]
>> }
>> })
>> criteriaValues <- do.call("cbind",retainedCriteria)
>> selectedColumns <<- apply(criteriaValues,1,logic)
>> cat('\n',sum(selectedColumns),' columns selected.\n',sep='')
>> return(selectedColumns)
>> }
>>
>>
>>
>>
>> library(Biobase)
>> # prepating metadata
>> treatment=c("D","192","233","192","233")
>> control=c(1,0,0,0,0)
>> dose=c(NA,30,10,10,0.3)
>> replicate=rep(1,5)
>> metadata <- data.frame
>> (cbind(treatment=treatment,control=control,dose=dose,replicate=replicate,
>> labelDescription=paste("treatment: ",treatment, ifelse(control==1, "
>> [control]","")," dose:",dose,"(",replicate,")",sep='')))
>>
>> data1=data.frame(cbind(v1=1:2,v2=2:3,v3=3:4,v4=4:5,v5=5:6))
>> anData1 = new("AnnotatedDataFrame",data=data1,varMetadata=metadata)
>>
>>
>> # use little function to create an subset data.frame
>>
>> anData1[,selectVariables(anData1,dose=10, dummy=0)]
>>
>>
>>
>>
>>
>> --
>> Eric Lecoutre
>> Consultant - Business & Decision
>> Business Intelligence & Customer Intelligence
>>
>
>
>
> --
> Eric Lecoutre
> Consultant - Business & Decision
> Business Intelligence & Customer Intelligence
>
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>
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--
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109
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