mstmap.data.frame {ASMap}  R Documentation 
Extremely fast linkage map construction for data frame objects using MSTmap.
Description
Extremely fast linkage map construction for data frame objects utilizing the source code for MSTmap (see Wu et al., 2008). The construction includes linkage group clustering, marker ordering and genetic distance calculations.
Usage
## S3 method for class 'data.frame'
mstmap(object, pop.type = "DH", dist.fun = "kosambi",
objective.fun = "COUNT", p.value = 1e06, noMap.dist = 15,
noMap.size = 0, miss.thresh = 1, mvest.bc = FALSE,
detectBadData = FALSE, as.cross = TRUE, return.imputed = FALSE,
trace = FALSE, ...)
Arguments
object 
A 
pop.type 
Character string specifying the population type of the data frame

dist.fun 
Character string defining the distance function used for calculation of
genetic distances. Options are 
objective.fun 
Character string defining the objective function to be used when
constructing the map. Options are 
p.value 
Numerical value to specify the threshold to use when clustering
markers. Defaults to 
noMap.dist 
Numerical value to specify the smallest genetic distance a set of
isolated markers can appear distinct from other linked markers. Isolated
markers will appear in their own linkage groups ad will be of size
specified by 
noMap.size 
Numerical value to specify the maximum size of isolated marker linkage
groups that have been identified using 
miss.thresh 
Numerical value to specify the threshold proportion of missing marker scores allowable in each of the markers. Markers above this threshold will not be included in the linkage map. Default is 1. 
mvest.bc 
Logical value. If 
detectBadData 
Logical value. If 
as.cross 
Logical value. If 
return.imputed 
Logical value. If 
trace 
An automatic tracing facility. If 
... 
Currently ignored. 
Details
The data frame object
must have an explicit format with markers
in rows and genotypes in columns. The marker names are required to be in
the rownames
component and the genotype names are
required to be in the names
component of the object
. In
each set of names there must be no spaces. If spaces are detected they
are exchanged for a "". Each of the columns of the data frame must be of class
"character"
(not factors). If converting from a matrix, this can
easily be achieved by using the stringAsFactors = FALSE
argument
for any data.frame
method.
It is important to know what population type the data frame
object
is and to correctly input this into pop.type
. If
pop.type = "ARIL"
then it is assumed that the minimal number of heterozygotes have been
set to missing before proceeding. The advanced RIL population is then
treated like a backcross population for the purpose of linkage map
construction. Genetic distances are adjusted post construction.
For nonadvanced RIL populations pop.type =
"RILn"
, the number of generations of selfing is limited to 20 to
ensure sensible input.
The content of the markers in object
can either be all numeric
(see below) or all character. If markers are of type character then
the following allelic content must be explicitly adhered to. For pop.type
"BC"
,
"DH"
or "ARIL"
the two allele types should
be represented as ("A"
or "a"
) and ("B"
or
"b"
). For nonadvanced RIL populations (pop.type = "RILn"
)
phase unknown heterozygotes should be represented as
"X"
. For all populations, missing marker scores should be represented
as ("U"
or ""
).
This function also extends the functionality of the MSTmap
algorithm by allowing users to input a complete numeric data frame of
marker probabilities for pop.type
"BC"
, "DH"
or
"ARIL"
. The values must be inclusively between 1 (A) and 0 (B) and be
representative of the probability that the A allele is present. No
missing values are allowed.
The algorithm allows an adjustment of the p.value
threshold for
clustering of markers to distinct linkage groups (see Wu et al.,
2008) and is highly dependent on the number of individuals in
the population. As the number of individuals increases the
p.value
threshold should be decreased accordingly. This may
require some trial and error to achieve desired results.
If mvest.bc = TRUE
and the population type is "BC","DH","ARIL"
then missing values are imputed before markers are clustered into
linkage groups. This is only a simple imputation that places a 0.5
probability of the missing observation being one allele or the other and
is used to assist the clustering algorithm when there is known to be high numbers of
missing observations between pairs of markers.
It should be highlighted that for population types
"BC","DH","ARIL"
, imputation of missing values occurs
regardless of the value of mvest.bc
. This is achieved using an EM algorithm that is
tightly coupled with marker ordering (see Wu et al., 2008). Initially
a marker order is obtained omitting missing marker scores and then
imputation is performed based on the underlying recombinant probabilities
of the flanking markers with the markers containing the missing
value. The recombinant probabilities are then recomputed and an update of
the pairwise distances are calculated. The ordering algorithm is then
run again and the complete process is repeated until
convergence. Note, the imputed probability matrix for the linkage map
being constructed is returned if return.imputed = TRUE
.
For populations "BC","DH","ARIL"
, if detectBadData = TRUE
,
the marker ordering algorithm also
includes the detection of genotyping errors. For any individual
genotype, the detection method is based on a weighted Euclidean metric
(see Wu et al., 2008) that is a function of the
recombination probabilities of all the markers with the marker containing
the suspicious observation. Any genotyping errors detected are set to
missing and the missing values are then imputed if mv.est =
TRUE
. Note, the detection of these errors and their
amendment is returned in the imputed probability matrix if
return.imputed = TRUE
If as.cross = TRUE
then the constructed object is returned as a
qtl cross object with the appropriate class structure. For "RILn"
populations the constructed object is given the class "bcsft"
by
using the qtl package conversion function convert2bcsft
with arguments F.gen = n
and BC.gen =
0
. For "ARIL"
populations the constructed object is given the
class "riself"
.
If return.imputed = TRUE
and pop.type
is one of
"BC","DH","ARIL"
, then the marker probability matrix is
returned for the linkage groups that have been constructed using the
algorithm. Each linkage group is named identically to the linkage groups
of the map and, if as.cross = TRUE
, contains an ordered
"map"
element and a "data"
element consisting of marker probabilities of the A allele being present
(i.e. P(A) = 1, P(B) = 0). Both elements contain a
possibly reduced version of the marker set that includes all
noncolocating markers as well as the first marker of any set of
colocating markers. If as.cross = FALSE
then an ordered data frame of matrix
probabilities is returned.
Value
If as.cross = TRUE
the function returns an R/qtl cross object with the appropriate
class structure. The object is a list with usual components
"pheno"
and "geno"
. If as.cross = FALSE
the
function returns an ordered data frame object
with additional columns that indicate the linkage group, the position
and marker names and genetic distance of the markers within in each
linkage group. If markers were omitted for any reason during the
construction, the object will have an "omit"
component with
all omitted markers in a collated matrix. If return.imputed =
TRUE
then the object will also contain an "imputed.geno"
element.
Author(s)
Julian Taylor, Dave Butler, Timothy Close, Yonghui Wu, Stefano Lonardi
References
Wu, Y., Bhat, P., Close, T.J, Lonardi, S. (2008) Efficient and Accurate Construction of Genetic Linkage Maps from Minimum Spanning Tree of a Graph. Plos Genetics, 4, Issue 10.
Taylor, J., Butler, D. (2017) R Package ASMap: Efficient Genetic Linkage Map Construction and Diagnosis. Journal of Statistical Software, 79(6), 1–29.
See Also
Examples
data(mapDH, package = "ASMap")
## forming data frame object from R/qtl object
dfg < t(do.call("cbind", lapply(mapDH$geno, function(el) el$data)))
dimnames(dfg)[[2]] < as.character(mapDH$pheno[["Genotype"]])
dfg < dfg[sample(1:nrow(dfg), nrow(dfg), replace = FALSE),]
dfg[dfg == 1] < "A"
dfg[dfg == 2] < "B"
dfg[is.na(dfg)] < "U"
dfg < cbind.data.frame(dfg, stringsAsFactors = FALSE)
## construct map
testd < mstmap(dfg, dist.fun = "kosambi", trace = FALSE)
pull.map(testd)
## let's get a timing on that ...
system.time(testd < mstmap(dfg, dist.fun = "kosambi", trace = FALSE))