Molecular data like gene or metabolite data are frequently annotated by various types of IDs. This function maps and summarize molecular data onto standard gene or compound IDs. It would be straightforward to integrate, analyze or visualize the "standardized" data with pathways or functional categories.
Either vector (single sample) or a matrix-like data (multiple sample). Vector should be numeric with molecule IDs as names or it may also be character of molecule IDs. Character vector is treated as discrete or count data. Matrix-like data structure has molecules as rows and samples as columns. Row names should be molecule IDs. Default mol.data=NULL. This argument is equivalent to gene.data or cpd.data in the pathview function. Check pahtview function for more information.
a two-column character matrix, giving the mapping between molecular IDs
used in mol.data and taget/standard molecular IDs. Then mol.data are
character, name of the gene annotation package. This package should be
one of the standard annotation packages from Bioconductor, such as
"org.Hs.eg.db" (default). Check
character, the method name to calculate node summary given that multiple genes or compounds are mapped to it. Poential options include "sum","mean", "median", "max", "max.abs" and "random". Default sum.method="sum".
This function is called in pathview main function when gene.idtype or
cpd.idtype is not the standard type, so that the molecular data can be
mapped and summarized onto standard IDs. This is needed for further
mapping to KEGG pathways. The same standard ID mapping is needed when
carry out pathway or functional analysis on molecular data, which are
labeled by non-standard (or alien) IDs or probe names, like in most of
the microarray or metabolomics datasets. In other words, function
mol.sum can be useful in all these situations.
a numeric vector or matrix. Its dimensionality is the same as the input mol.data except row names are standard molecular IDs.
Weijun Luo <email@example.com>
Luo, W. and Brouwer, C., Pathview: an R/Bioconductor package for pathway based data integration and visualization. Bioinformatics, 2013, 29(14): 1830-1831, doi: 10.1093/bioinformatics/btt285
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data(gene.idtype.list) #generate simulated gene data named with non-KEGG/Entrez gene IDs gene.ensprot <- sim.mol.data(mol.type = "gene", id.type = gene.idtype.list, nmol = 50000) #construct map between non-KEGG ID and KEGG ID (Entrez gene) id.map.ensprot <- id2eg(ids = names(gene.ensprot), category = gene.idtype.list, org = "Hs") #Map molecular data onto Entrez Gene IDs gene.entrez <- mol.sum(mol.data = gene.ensprot, id.map = id.map.ensprot) #check the results head(gene.ensprot) head(id.map.ensprot) head(gene.entrez)
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