genotypes: Create Core Hunter genotype data from data frame, matrix or...

In corehunter: Multi-Purpose Core Subset Selection

Create Core Hunter genotype data from data frame, matrix or file.

Description

Specify either a data frame or matrix, or a file from which to read the genotypes. See https://www.corehunter.org for documentation and examples of the genotype data file format used by Core Hunter.

Usage

genotypes(data, alleles, file, format)

Arguments

data	Data frame or matrix containing the genotypes (individuals x markers) depending on the chosen format: default Data frame. One row per individual and one or more columns per marker. Columns contain the names, numbers, references, ... of observed alleles. Unique row names (item ids) are required and columns should be named after the marker to which they belong, optionally extended with an arbitrary suffix starting with a dot (.), dash (-) or underscore (_) character. biparental Numeric matrix or data frame. One row per individual and one column per marker. Data consists of 0, 1 and 2 coding for homozygous (AA), heterozygous (AB) and homozygous (BB), respectively. Unique row names (item ids) are required and optionally column (marker) names may be included as well. frequency Numeric matrix or data frame. One row per individual (or bulk sample) and multiple columns per marker. Data consists of allele frequencies, grouped per marker in consecutive columns named after the corresponding marker, optionally extended with an arbitrary suffix starting with a dot (.), dash (-) or underscore (_) character.. The allele frequencies of each marker should sum to one in each sample. Unique row names (item ids) are required. In case a data frame is provided, an optional first column NAME may be included to specify item names. The remaining columns should follow the format as described above. See https://www.corehunter.org for more details about the supported genotype formats. Note that both the frequency and biparental format syntactically also comply with the default format but with different semantics, meaning that it is very important to specify the correct format. Some checks have been built in that raise warnings in case it seems that the wrong format might have been specified based on an inspection of the data. If you are sure that you have selected the correct format these warnings, if any, can be safely ignored.
alleles	Allele names per marker (character vector). Ignored except when creating frequency data from a matrix or data frame. Allele names should be ordered in correspondence with the data columns.
file	File containing the genotype data.
format	Genotype data format, one of default, biparental or frequency.

Value

Genotype data of class chgeno with elements

data

Genotypes. Data frame for default format, numeric matrix for other formats.

size

Number of individuals in the dataset.

ids

Unique item identifiers (character).

names

Item names (character). Names of individuals to which no explicit name has been assigned are equal to the unique ids.

markers

Marker names (character). May contain NA values in case only some or no marker names were specified. Marker names are always included for the default and frequency format but are optional for the biparental format.

alleles

List of character vectors with allele names per marker. Vectors may contain NA values in case only some or no allele names were specified. For biparental data the two alleles are name "0" and "1", respectively, for all markers. For the default format allele names are inferred from the provided data. Finally, for frequency data allele names are optional and may be specified either in the file or through the alleles argument when creating this type of data from a matrix or data frame.

java

Java version of the data object.

format

Genotype data format used.

file

Normalized path of file from which data was read (if applicable).

Examples

## Not run: 
# create from data frame or matrix

# default format
geno.data <- data.frame(
 NAME = c("Alice", "Bob", "Carol", "Dave", "Eve"),
 M1.1 = c(1,2,1,2,1),
 M1.2 = c(3,2,2,3,1),
 M2.1 = c("B","C","D","B",NA),
 M2.2 = c("B","A","D","B",NA),
 M3.1 = c("a1","a1","a2","a2","a1"),
 M3.2 = c("a1","a2","a2","a1","a1"),
 M4.1 = c(NA,"+","+","+","-"),
 M4.2 = c(NA,"-","+","-","-"),
 row.names = paste("g", 1:5, sep = "-")
)
geno <- genotypes(geno.data, format = "default")

# biparental (e.g. SNP)
geno.data <- matrix(
 sample(c(0,1,2), replace = TRUE, size = 1000),
 nrow = 10, ncol = 100
)
rownames(geno.data) <- paste("g", 1:10, sep = "-")
colnames(geno.data) <- paste("m", 1:100, sep = "-")
geno <- genotypes(geno.data, format = "biparental")

# frequencies
geno.data <- matrix(
 c(0.0, 0.3, 0.7, 0.5, 0.5, 0.0, 1.0,
   0.4, 0.0, 0.6, 0.1, 0.9, 0.0, 1.0,
   0.3, 0.3, 0.4, 1.0, 0.0, 0.6, 0.4),
 byrow = TRUE, nrow = 3, ncol = 7
)
rownames(geno.data) <- paste("g", 1:3, sep = "-")
colnames(geno.data) <- c("M1", "M1", "M1", "M2", "M2", "M3", "M3")
alleles <- c("M1-a", "M1-b", "M1-c", "M2-a", "M2-b", "M3-a", "M3-b")
geno <- genotypes(geno.data, alleles, format = "frequency")

# read from file

# default format
geno.file <- system.file("extdata", "genotypes.csv", package = "corehunter")
geno <- genotypes(file = geno.file, format = "default")

# biparental (e.g. SNP)
geno.file <- system.file("extdata", "genotypes-biparental.csv", package = "corehunter")
geno <- genotypes(file = geno.file, format = "biparental")

# frequencies
geno.file <- system.file("extdata", "genotypes-frequency.csv", package = "corehunter")
geno <- genotypes(file = geno.file, format = "frequency")

## End(Not run)

corehunter documentation built on Sept. 1, 2023, 5:07 p.m.