cateGOry: Construct a category membership matrix from a list of gene identifiers and their annotated GO categories.

Description

The function constructs a category membership matrix, such as used by applyByCategory, from a list of gene identifiers and their annotated GO categories. For each of the GO categories stated in categ, all less specific terms (ancestors) are also included, thus one need only obtain the most specific set of GO term mappings, which can be obtained from Bioconductor annotation packages or via biomaRt. The ancestor relationships are obtained from the GO.db package.

Usage

cateGOry(x, categ, sparse=FALSE)

Arguments

Character vector with (arbitrary) gene identifiers. They will be used for the column names of the resulting matrix.

categ

A character vector of the same length as x with GO annotations for the genes in x. If a gene has multiple GO annotations, it is expected to occur multiple times in x, once for each different annotation.

sparse

Logical. If TRUE, the resulting matrix is constructed using Matrix, otherwise, R's base matrix is used.

Value

The adjacency matrix of the bipartite category membership graph, rows are categories and columns genes.

Details

The function requires the GO package.

For subsequent analyses, it is often useful to remove categories that have only a small number of members. Use the normal matrix subsetting syntax for this, see example.

If a GO category in categ is not found in the GO annotation package, a warning will be generated, and no ancestors for that GO category are added (but that category itself will be part of the returned adjacency matrix).

Examples

Run this code

  g = cateGOry(c("CG2671", "CG2671", "CG2950"),
               c("GO:0090079", "GO:0001738", "GO:0003676"), sparse=TRUE)
  g

  rowSums(g)   ## number of genes in each category

  ## Filter out categories with less than minMem and more than maxMem members.
  ## This is toy data, in real applications, a choice of minMem higher
  ## than 2 will be more appropriate.
  filter = function(x, minMemb = 2, maxMemb = 35) ((x>=minMemb) & (x<=maxMemb))
  g[filter(rowSums(g)),,drop=FALSE ]