polysat (version 1.7-7)

read.Structure: Read Genotypes and Other Data from a Structure File

Description

read.Structure creates a genambig object by reading a text file formatted for the software Structure. Ploidies and PopInfo (if available) are also written to the object, and data from additional columns can optionally be extracted as well.

Usage

read.Structure(infile, ploidy, missingin = -9, sep = "\t",
               markernames = TRUE, labels = TRUE, extrarows = 1,
               popinfocol = 1, extracols = 1, getexcols = FALSE,
               ploidyoutput="one")

Value

If getexcols=FALSE, the function returns only a genambig object.

If getexcols=TRUE, the function returns a list with two elements. The first, named ExtraCol, is a data frame, where the row names are the sample names and each column is one of the extra columns from the file (but with each sample only once instead of being repeated ploidy

number of times). The second element is named Dataset and is the genotype object described above.

Arguments

infile

Character string. The file path to be read.

ploidy

Integer. The ploidy of the file, i.e. how many rows there are for each individual.

missingin

The symbol used to represent missing data in the Structure file.

sep

The character used to delimit the fields of the Structure file (tab by default).

markernames

Boolean, indicating whether the file has a header containing marker names.

labels

Boolean, indicating whether the file has a column containing sample names.

extrarows

Integer. The number of extra rows that the file has, not counting marker names. This could include rows for recessive alleles, inter-marker distances, or phase information.

popinfocol

Integer. The column number (after the labels column, if present) where the data to be used for PopInfo are stored. Can be NA to indicate that PopInfo should not be extracted from the file.

extracols

Integer. The number of extra columns that the file has, not counting sample names (labels) but counting the column to be used for PopInfo. This could include PopData, PopFlag, LocData, Phenotype, or any other extra columns.

getexcols

Boolean, indicating whether the function should return the data from any extra columns.

ploidyoutput

This argument determines what is assigned to the Ploidies slot of the "genambig" dataset that is output. It should be a string, either "one", "samplemax", or "matrix". This indicates, respectively, that ploidy should be used as the ploidy of the entire dataset, that the maximum number of alleles for each sample should be used as the ploidy of that sample, or that ploidies should be stored as a matrix of allele counts for each sample*locus.

Author

Lindsay V. Clark

Details

The current version of read.Structure does not support the ONEROWPERIND option in the file format. Each locus must only have one column. If your data are in ONEROWPERIND format, it should be fairly simple to manipulate it in a spreadsheet program so that it can be read by read.GeneMapper instead.

read.Structure uses read.table to initially read the file into a data frame, then extracts information from the data frame. Because of this, any header rows (particularly the one containing marker names) should have leading tabs (or spaces if sep=" ") so that the marker names align correctly with their corresponding genotypes. You should be able to open the file in a spreadsheet program and have everything align correctly.

If the file does not contain sample names, set labels=FALSE. The samples will be numbered instead, and if you like you can use the Samples<- function to edit the sample names of the genotype object after import. Likewise, if markernames=FALSE, the loci will be numbered automatically by the column names that read.table creates, but these can also be edited after the fact.

The Ploidies slot of the "genambig" object that is created is initially indexed by both sample and locus, with ploidy being written to the slot on a per-genotype basis. After all genotypes have been imported, reformatPloidies is used to convert Ploidies to the simplest possible format before the object is returned.

References

https://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.4/structure_doc.pdf

Hubisz, M. J., Falush, D., Stephens, M. and Pritchard, J. K. (2009) Inferring weak population structure with the assistance of sample group information. Molecular Ecology Resources 9, 1322--1332.

Falush, D., Stephens, M. and Pritchard, J. K. (2007) Inferences of population structure using multilocus genotype data: dominant markers and null alleles. Molecular Ecology Notes 7, 574--578.

See Also

write.Structure, read.GeneMapper, read.Tetrasat, read.ATetra, read.GenoDive, read.SPAGeDi, read.POPDIST, read.STRand

Examples

# create a file to read (normally done in a text editor or spreadsheet
# software)
myfile <- tempfile()
cat("\t\tRhCBA15\tRhCBA23\tRhCBA28\tRhCBA14\tRUB126\tRUB262\tRhCBA6\tRUB26",
    "\t\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "WIN1B\t1\t197\t98\t152\t170\t136\t208\t151\t99",
    "WIN1B\t1\t208\t106\t174\t180\t166\t208\t164\t99",
    "WIN1B\t1\t211\t98\t182\t187\t184\t208\t174\t99",
    "WIN1B\t1\t212\t98\t193\t170\t203\t208\t151\t99",
    "WIN1B\t1\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "WIN1B\t1\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "WIN1B\t1\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "WIN1B\t1\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD1\t2\t208\t100\t138\t160\t127\t202\t151\t124",
    "MCD1\t2\t208\t102\t153\t168\t138\t207\t151\t134",
    "MCD1\t2\t208\t106\t157\t180\t162\t211\t151\t137",
    "MCD1\t2\t208\t110\t159\t187\t127\t215\t151\t124",
    "MCD1\t2\t208\t114\t168\t160\t127\t224\t151\t124",
    "MCD1\t2\t208\t124\t193\t160\t127\t228\t151\t124",
    "MCD1\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD1\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD2\t2\t208\t98\t138\t160\t136\t202\t150\t120",
    "MCD2\t2\t208\t102\t144\t174\t145\t214\t150\t132",
    "MCD2\t2\t208\t105\t148\t178\t136\t217\t150\t135",
    "MCD2\t2\t208\t114\t151\t184\t136\t227\t150\t120",
    "MCD2\t2\t208\t98\t155\t160\t136\t202\t150\t120",
    "MCD2\t2\t208\t98\t157\t160\t136\t202\t150\t120",
    "MCD2\t2\t208\t98\t163\t160\t136\t202\t150\t120",
    "MCD2\t2\t208\t98\t138\t160\t136\t202\t150\t120",
    "MCD3\t2\t197\t100\t172\t170\t159\t213\t174\t134",
    "MCD3\t2\t197\t106\t174\t178\t193\t213\t176\t132",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    "MCD3\t2\t-9\t-9\t-9\t-9\t-9\t-9\t-9\t-9",
    sep="\n",file=myfile)

# view the file
cat(readLines(myfile), sep="\n")

# read the structure file into genotypes and populations
testdata <- read.Structure(myfile, ploidy=8)

# examine the results
testdata