read.interval {wgaim} | R Documentation |
Reads genotypic marker data in a wide range of formats and estimates the
appropriate linkage map via Bromans read.cross
function (see
Details). Additionally, this function imputes missing markers and also
estimates interval information appropriate for further analysis.
read.interval(format = c("csv", "csvr", "csvs", "csvsr", "mm", "qtx", "tlcart", "gary", "karl"), dir = getwd(), file, genfile, mapfile, phefile, chridfile, mnamesfile, pnamesfile, na.strings = c("-", "NA"), genotypes = c("A", "B"), estimate.map = TRUE, convertXdata = TRUE, missgeno = "MartinezCurnow", rem.mark = TRUE, id = "id", subset = NULL, ...)
format |
see read.cross |
dir |
see read.cross |
file |
see read.cross |
genfile |
see read.cross |
mapfile |
see read.cross |
phefile |
see read.cross |
chridfile |
see read.cross |
mnamesfile |
see read.cross |
pnamesfile |
see read.cross |
na.strings |
see read.cross |
genotypes |
see read.cross |
estimate.map |
see read.cross |
convertXdata |
see read.cross |
missgeno |
a character string determining how missing values in
the linkage map should be imputed. If "Broman ", then missing values are
imputed according to Bromans rules. If "MartinezCurnow " then
missing values are imputed according to the rules of Martinez &
Curnow (1994) (see reference list). The default is "MartinezCurnow " |
rem.mark |
logical value. If TRUE redundant markers are deleted
and placed in the component of the object (see details). Defaults to
TRUE . |
id |
the name of the unique identifier for each row of genotype
data (see details). Defaults to "id " |
subset |
A possible character vector naming the subset of
chromosomes to be returned. Defaults to NULL implying return
all chromosomes. |
... |
Any other arguments to be passed to read.cross for
the appropriate reading in of the data from a file (see
read.cross for more details). |
This function uses a *modified* verison of Bromans' read.cross
function to read in the genotypic data and estimate the linkage
map. This *strict* modification only allows two genotypes which are the
defaults "A" and "B". Attempting more than this will cause
an error.
Bromans' read.cross
and this function by default read genotypic information as
well as phenotypic information if the data contains any. In general,
this phenotypic component of the file *should* contain, at least, a column
to identify each row of the data uniquely. The argument id
is
used for the purpose of identifying this column. An error will be
induced otherwise.
The output of this function differs slightly from read.cross
by
the inclusion of additional components to the returned object. To ensure
that functions of the qtl package could still be used with the returned
object from this function the class structure from read.cross
has
been maintained. The returned object also inherits the class
"interval
" for forwards compatibility with functions available in
the wgaim
package.
a list of class "cross
" that also inherits the class
"interval
". The list contains the following components
geno |
This is a list with elements named by the corresponding names of the
chromosomes. Each chromosome is itself a list with six
elements: "data " is the actual estimated map matrix with rows
as individuals named by "id " and markers as columns; "map " is a vector of marker positions on the
corresponding chromosome; "argmax " is identical to "data "
matrix but with all NA's replaced by imputed values according to the
rules of "missgeno "; "dist " contains the genetic
distance between adjacent markers or the genetic distances of the
intervals; "theta " contains the recombination fractions for
each interval; "intval " contains the recalculated intervals
based on the recombination fractions and the missing marker information. |
cor.markers |
If rem.mark = TRUE , a three column matrix with each row
describing which pairwise markers are correlated and what chromosome
they are from. |
pheno |
A data.frame of phenotypic information with rows as individuals read
in from read.cross . A copy of the column named by the "id " argument
can be found here (see read.cross ) |
Julian Taylor, Simon Diffey, Ari Verblya and Brian Cullis
Martinez, O., Curnow. R. N. (1994) Missing markers when estimating quantitative trait loci using regression mapping. Heredity, 73, 198-206.
Verbyla, A. P., Cullis, B. R., Thompson, R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theoretical And Applied Genetics, 116, 95-111.
## Not run: # read in linkage map from a rotated .CSV file with "id" as the # identifier for each unique row racca <- read.interval("csvr", file="raccasgroups.csv", genotypes=c("A","B"), missgeno="MartinezCurnow", id = "id", na.strings = c("-", "NA")) # plot linkage map link.map(racca, cex = 0.5) ## End(Not run)