Hi all, I'm packaging my code for a meta-clustering method to submit to Bioconductor, and I have the following design dilemma: my method can be used on either a matrix of gene expression data (well really any biological data), or a matrix of clinical data. I would like to have the input data matrices (one for each experiment) to be in standardized S4 objects, as Bioconductor encourages.
But the eSet class is specifically designed for biological data. Is there a broader S4 class I could use, or would I just instruct users with clinical data to put their data matrix in the "featureData" slot? This clinical data matrix would not contain outcomes variables; that could still potentially be stored in the phenoData slot. The AnnotatedDataFrame appears to be too simplistic, given that a user may still want to store several outcomes variables in a phenoData or phenoData-like slot (varMetaData does from AnnotatedDataFrame does not appear to fit this purpose). Best, Katie -- Katie Planey https://www.linkedin.com/in/katieplaney PhD Candidate | Stanford Biomedical Informatics [[alternative HTML version deleted]] _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
