polysat (version 0.1)

read.GeneMapper: Read GeneMapper Genotypes Tables

Description

Given a vector of filepaths to tab-delimited text files containing genotype data in the ABI GeneMapper Genotypes Table format, read.GeneMapper produces a two-dimensional list of genotypes that can be read by other functions in the polysat package.

Usage

read.GeneMapper(infiles, missing = -9)

Arguments

infiles

A character vector of paths to the files to be read.

missing

A numerical value used to indicate missing data for a given sample and locus.

Value

• The object produced is a two-dimensional list of vectors representing the genotypes. Samples are represented by the first dimension and loci by the second dimension. The names of the samples and loci are taken from the Sample Names and Markers columns, respectively, of the GeneMapper files. The vectors at each position of the list are numerical and are only as long as needed to contain each allele (for that sample and locus) as one element.

Details

read.GeneMapper can read the genotypes tables that are exported by the Applied Biosystems GeneMapper software. The only alterations to the files that the user may have to make are 1) delete any rows with missing data or fill in a numerical missing data symbol of your choice (such as -9) in the first allele slot for that row, 2) make sure that all allele names are numeric representations of fragment length (no question marks or dashes), and 3) put sample names into the Sample Name column, if the names that you wish to use in analysis are not already there. Each file should have the standard header row produced by the software. If any sample has more than one genotype listed for a given locus, only the last genotype listed will be used. The file format is simple enough that the user can easily create files manually if GeneMapper is not the software used in allele calling. The files are tab-delimited text files. There should be a header row with column names. The column labeled Sample Name should contain the names of the samples, and the column labeled Marker should contain the names of the loci. You can have as many or as few columns as needed to contain the alleles, and each of these columns should be labeled Allele X where X is a number unique to each column. Row labels and any other columns are ignored. For any given sample, each allele is listed only once and is given as an integer that is the length of the fragment in nucleotides. Alleles are separated by tabs. If you have more allele columns than alleles for any given sample, leave the extra cells blank so that read.table will read them as NA. Example data files in this format are included in the package. read.GeneMapper will read all of your data at once. It takes as its first argument a character vector containing paths to all of the files to be read. How the data are distributed over these files does not matter. The function finds all unique sample names and all unique markers across all the files, and automatically puts a missing data symbol into the list if a particular sample and locus combination is not found. Rows in which all allele cells are blank should NOT be included in the input files; either delete these rows or put the missing data symbol into the first allele cell. Sample and locus names must be consistent within and across the files. The list that is produced is indexed by these names. For example, if the object produced was called mygenotypes, mygenotypes[["AB1","ABC5"]] would be a vector containing alleles for sample AB1 at locus ABC5. mygenotypes[,"ABC5"] would be a list of all genotypes at locus ABC5, and mygenotypes["AB1",] would be a list of all genotypes for sample AB1.

References

http://www.appliedbiosystems.com/genemapper

See Also

read.Tetrasat, read.ATetra, read.Structure, read.GenoDive, write.GeneMapper, dominant.to.codominant, read.SPAGeDi

Examples

myinfiles<-c("data\sample CBA15.txt","data\sample
    CBA23.txt","data\sample CBA28.txt")
    mygenotypes<-read.GeneMapper(myinfiles)

    #Look at the object produced.  Alleles are not listed but you can
    #see that the array was filled.
    mygenotypes

    #Look at the genotype of individual FCR5.
    mygenotypes["FCR5",]

    #Correct one of the genotypes
    mygenotypes[["FCR5","RhCBA15"]]<-c(208)

# an example with defined data:
# create a table of data
gentable <- data.frame(Sample.Name=rep(c("ind1","ind2","ind3"),2),
                       Marker=rep(c("loc1","loc2"), each=3),
                       Allele.1=c(202,200,204,133,133,130),
                       Allele.2=c(206,202,208,136,142,136),
                       Allele.3=c(NA,208,212,145,148,NA),
                       Allele.4=c(NA,216,NA,151,157,NA)
                       )
# create a file (inspect this file in a text editor or spreadsheet
# software to see the required format)
write.table(gentable, file="readGMtest.txt", quote=FALSE, sep="t",
            na="", row.names=FALSE, col.names=TRUE)

# read the file
mygenotypes <- read.GeneMapper("readGMtest.txt")

# inspect the results
mygenotypes[,"loc1"]
mygenotypes[,"loc2"]