Differential expression at pathway level
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9 weeks ago
Chris ▴ 260

Hi Biostar,

I would like to do differential expression at pathway level by following this tutorial. I have microarray data. Would you please tell me how to get an object like leukimia_eset? Thank you so much!

data(leukemia)
leukemia_eset

https://bioconductor.org/packages/devel/bioc/vignettes/GSVA/inst/doc/GSVA.html

GSVA • 418 views
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9 weeks ago
AquaDeath ▴ 80

Hi!

The dataset on the tutorial is an ExpressionSet object. There is a detailed manual for building this type of Bioconductor object here, but esentially you need the assay info (intensities measured in microarray experiment), the probes info and the samples info as basic elements of ExpressionSet.

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Thank you so much for the info!

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Hi,

microarray <- data.matrix(microarray, rownames.force = NA)
ExpressionSet_object <- ExpressionSet(assayData=microarray)

I can create an expressionset object but I don't have subtype to run the next step:

mod <- model.matrix(~ factor(leukemia_es$subtype))

My microarray data can classify samples into 2 or 3 sub groups. Do you have any suggestion to add subtype into my expressionset object:

ExpressionSet (storageMode: lockedEnvironment)
assayData: 17873 features, 5626 samples 
  element names: exprs 
protocolData: none
phenoData: none
featureData: none
experimentData: use 'experimentData(object)'
Annotation:  
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The ExpressionSet object you are building still lacks two key "slots": featureData and phenoData. In this context, features refer to information regarding microarray probes. You should have some type of file with probe IDs (depending of the array used) that can be mapped later to genes with packages such as AnnotationDbi.

Then, phenotypic data depends on the experimental design of your run. For what you're telling me you can classify your samples in several groups depending on a factor or biological condition you want to study. You should build a data.frame whose rows correspond to the columns of the microarray matrix with the samples info (I'm assuming your microarray matrix is in the standard -omics form: rows are different probes and columns are intensities measured for each sample), and add that as pData. With this, you'd be able to use that factor the same way as the tutorial uses subtype to build a model matrix for applying limma. This last one is crucial for differential expression analysis in microarrays, so definitely worth to study it carefully before performing any enrichment analysis.

There are good limma examples in the vignettes of the package, but I'll also leave you some practical example pages:

Good luck with your analysis!

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