allelematch (version 2.5.5)

amDataset: Prepare a dataset for use with allelematch

Description

Given an input matrix or data.frame produce a amDataset object suitable for use with other allelematch functions.

Usage

amDataset(
		multilocusDataset, 
		missingCode = "-99", 
		indexColumn = NULL, 
		metaDataColumn = NULL, 
		ignoreColumn = NULL
		)
	# S3 method for amDataset
print(x, ...)

Value

An amDataset object.

Arguments

multilocusDataset

A matrix or data.frame containing samples in rows and alleles in columns.
Sampling IDs and meta-data may be specified in up to two additional columns.

missingCode

A character string giving the code used for missing data.
Missing data may also be represented as NA.

indexColumn

Optional.
A character string giving the column name, or an integer giving the column number containing the sampling ID or index information.
If an index is not supplied, then the function creates an alphabetical index.

metaDataColumn

Optional.
A character string giving the column name, or an integer giving the column number containing the meta-data.

ignoreColumn

Optional.
A vector of character string(s) giving the column name(s) or integer(s) giving the column number(s) that should be removed from the input dataset (i.e., those that matching and clustering should not consider).

x

An amDataset object.

...

Additional arguments to summary.

Author

Paul Galpern ()

Details

Examine amExampleData for an example of a typical input dataset in the diploid case. (Typically these files will be the CSV output from allele calling software). Sample index or ID information and sample meta-data may be specified in two additional columns. Columns can optionally be given names, and these are carried through analyses. If column names are not given, appropriate names are produced.

Each datum is treated as a character string in allelematch functions, enabling the mixing of numeric and alphanumeric data.

The multilocus dataset can contain any number of diploid or haploid markers, and these can be in any order. Thus in the diploid case there should be two columns for each locus (named, say, locus1a and locus1b). Please note that AlleleMatch functions pay no attention to genetics. In other words, each column is considered a comparable state. Thus matching and clustering of multilocus genotypes is done on the basis of superficial similarity of the data matrix rows, rather than on any appreciation of the allelic states at each locus. See amPairwise for more discussion.

For this reason it is important when working with diploid data to ensure that identical individuals will have identical alleles in each column. This can be achieved by sorting each locus so that in each case the lower length allele appears in, say, a column "locus1a" and the higher in column "locus1b." This pattern is likely the default in allele calling software and sorting will typically not be required unless data are derived from an unusual source.

Only one meta-data column is possible with allelematch. If multiple columns must be associated with a given sample for downstream analyses, try pasting them together into one string with an appropriate separator, and separating them later when allelematch analyses are concluded.

References

For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.

See Also

amPairwise, amUnique, amExampleData

Examples

	if (FALSE) {
	data("amExample5")
	
	## Typical usage
	myDataset <- 
		amDataset(
			amExample5, 
			missingCode = "-99", 
			indexColumn = 1, 
			metaDataColumn = 2, 
			ignoreColumn = "gender"
			)
	
	## Access elements of amDataset object
	myMetaData <- myDataset$metaData
	mySamplingID <- myDataset$index
	myAlleles <- myDataset$multilocus
	
	## View the structure of amDataset object
	unclass(myDataset)
	}