find_genes: Map SNPs to genes
In cbaumbach/genFun: Functions For Dealing With Genetic Data

Description Usage Arguments Details Value See Also Examples

Given a data frame with SNPs (CpGs, etc.) and a data frame with genes return the first data frame with an additional column that contains for every SNP a comma-separated list with the identifiers of genes that contain the given SNP.

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find_genes(d, genes, chr1 = "chr", pos = "pos", out = "genes",
           chr2 = chr1, start = "start", end = "end", id = "id",
           quiet = FALSE)

`d`	Data frame with SNPs.
`genes`	Data frame with genes.
`chr1`	Name of column in ‘d’ containing chromosome number.
`pos`	Name of column in ‘d’ containing basepair position.
`out`	Name of column in output data frame that contains the comma-separated lists of genes.
`chr2`	Name of column in ‘genes’ containing chromosome number.
`start`	Name of column in ‘genes’ containing gene start positions.
`end`	Name of column in ‘genes’ containing gene end positions.
`id`	Name of column in ‘genes’ containing gene identifiers that will appear in the comma-separated lists of the new column ‘out’ in the output data frame.
`quiet`	If FALSE (default) progress messages are printed to the screen.

Both ‘d’ and ‘genes’ can contain other columns beside ‘chr1’, ‘pos’, ‘chr2’, ‘start’, ‘end’, and ‘id’. The order of SNPs and genes within the data frames is of no importance. If a SNP does not belong to any of the genes, it will get a value of NA in the ‘out’ column of the resulting data frame. NAs in relevant columns of the SNP data frame lead to NAs in ‘out’ column in output. NAs in genes data frame are dropped before SNPs are matched to genes.

Returns a data frame like ‘d’ but with an additional column called ‘out’ that contains for every SNP in ‘d’ a comma-separated list of identifiers of genes from the ‘id’ column in ‘genes’ that contain the given SNP.

match_intervals

library(genFun)

d <- read.table(textConnection("\
snp chr pos
rs1 1 1
rs2 1 5
rs3 2 3
rs4 3 4
", "r"), header = TRUE, stringsAsFactors = FALSE)

genes <- read.table(textConnection("\
id chr start end
g1 1 1 3
g2 1 1 6
g3 2 1 4
g4 4 1 5
", "r"), header = TRUE, stringsAsFactors = FALSE)

find_genes(d, genes)