mqmpermutation {qtl} | R Documentation |
Two randomization approaches to obtain estimates of QTL significance:
mqmpermutation(cross, scanfunction=scanone, pheno.col=1, multicore=TRUE, n.perm=10, batchsize=10, file="MQM_output.txt", n.cluster=1, method=c("permutation","simulation"),cofactors=NULL, plot=FALSE, verbose=FALSE, ...)
cross |
An object of class cross . See read.cross for details.
|
scanfunction |
Function to use when mappingQTL's (either scanone,cim or mqm) |
pheno.col |
Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers. |
multicore |
Use multicore (if available) |
n.perm |
Number of permutations to perform (DEFAULT=10, should be 1000, or higher, for publications) |
batchsize |
Batch size. The entire set is split in jobs. Each job contains b.size number of traits per job |
file |
Name of the intermediate output file used |
n.cluster |
Number of child processes to split the job into |
method |
What kind permutation should occur: permutation or simulation |
cofactors |
cofactors, only used when scanfunction is mqm.
List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors |
plot |
If TRUE, make a plot |
verbose |
If TRUE, print tracing information |
... |
Parameters passed through to the
scanone , cim or
mqmscan functions |
Analysis of scanone
, cim
or
mqmscan
to scan for QTL in shuffled/randomized data. It is recommended to also install the snow
library.
The snow
library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation.
Returns a mqmmulti object. this object is a list of scanone objects that can be plotted using plot.scanone(result[[trait]])
Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman kbroman@biostat.wisc.edu
MQM
- MQM description and references
mqmscan
- Main MQM single trait analysis
mqmscanall
- Parallellized traits analysis
mqmaugment
- Augmentation routine for estimating missing data
mqmautocofactors
- Set cofactors using marker density
mqmsetcofactors
- Set cofactors at fixed locations
mqmpermutation
- Estimate significance levels
scanone
- Single QTL scanning
data(multitrait) #Use the multitrait dataset multitrait <- calc.genoprob(multitrait) result <- mqmpermutation(multitrait,pheno.col=7, n.perm=2, batchsize=2) cof <- mqmautocofactors(multitrait,50) #Set 50 cofactors multitrait <- fill.geno(multitrait) result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof,pheno.col=7, n.perm=2,batchsize=2,verbose=FALSE) #EXTRA f2perm <- mqmprocesspermutation(result) #Create a permutation object summary(f2perm) #Get Significant LOD thresholds