read_geno_csv: Data Input in CSV format

In mappoly: Genetic Linkage Maps in Autopolyploids

Data Input in CSV format

Description

Reads an external comma-separated values (CSV) data file. The format of the file is described in the Details section. This function creates an object of class mappoly.data.

Usage

read_geno_csv(
  file.in,
  ploidy,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

Arguments

file.in	a character string with the name of (or full path to) the input file containing the data to be read
ploidy	the ploidy level
filter.non.conforming	if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.
elim.redundant	logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
verbose	if TRUE (default), the current progress is shown; if FALSE, no output is produced

Details

This is an alternative and a somewhat more straightforward version of the function read_geno. The input is a standard CSV file where the rows represent the markers, except for the first row which is used as a header. The first five columns contain the marker names, the dosage in parents 1 and 2, the chromosome information (i.e. chromosome, scaffold, contig, etc) and the position of the marker within the sequence. The remaining columns contain the dosage of the full-sib population. A tetraploid example of such file can be found in the Examples section.

Value

An object of class mappoly.data which contains a list with the following components:

ploidy	ploidy level
n.ind	number individuals
n.mrk	total number of markers
ind.names	the names of the individuals
mrk.names	the names of the markers
dosage.p1	a vector containing the dosage in parent P for all n.mrk markers
dosage.p2	a vector containing the dosage in parent Q for all n.mrk markers
chrom	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
genome.pos	Physical position of the markers into the sequence
seq.ref	NULL (unused in this type of data)
seq.alt	NULL (unused in this type of data)
all.mrk.depth	NULL (unused in this type of data)
geno.dose	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1
n.phen	number of phenotypic traits
phen	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
kept	if elim.redundant = TRUE, holds all non-redundant markers
elim.correspondence	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [e-mail], with minor changes by Gabriel Gesteira, [e-mail]

References

Mollinari M., Olukolu B. A., Pereira G. da S., Khan A., Gemenet D., Yencho G. C., Zeng Z-B. (2020), Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping, _G3: Genes, Genomes, Genetics_. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/g3.119.400620")}

Mollinari, M., and Garcia, A. A. F. (2019) Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models, _G3: Genes, Genomes, Genetics_. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/g3.119.400378")}

Examples

#### Tetraploid Example
ft = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/tetra_solcap.csv"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
SolCAP.dose <- read_geno_csv(file.in  = tempfl, ploidy = 4)
print(SolCAP.dose, detailed = TRUE)
plot(SolCAP.dose)

mappoly documentation built on May 29, 2024, 6:05 a.m.