mol.sum: Mapping and summation of molecular data onto standard IDs
In DGendooLab/pathviewM: a tool set for pathway based data integration and visualization

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2	mol.sum(mol.data, id.map, gene.annotpkg = "org.Hs.eg.db", sum.method = c("sum", "mean", "median", "max", "max.abs", "random")[1])

`mol.data`	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.
`id.map`	a two-column character matrix, giving the mapping between molecular IDs used in mol.data and taget/standard molecular IDs. Then mol.data are gene data, `id.map` may also be a character specifying the type of IDs used in mol.data. The two-column mapping matrix will be generated automatically.
`gene.annotpkg`	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 `data(bods); bods` for a full list of standard annotation packages. You may also use your custom annotation package built with AnnotationDbi, the Bioconductor Annotation Database Interface. Only effective when mol.data are gene.data and id.map gives the ID type being used.
`sum.method`	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 <luo_weijun@yahoo.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

node.map the node data mapper function. id2eg, cpd2kegg etc the auxillary molecular ID mappers, pathview the main function,

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[4], 
    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[4], 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)