CTLmapping {ctl} | R Documentation |
Scan for correlated trait loci (CTL)
CTLmapping(genotypes, phenotypes, phenocol = 1, nperm = 100, nthreads = 1,
strategy = c("Exact", "Full", "Pairwise"), adjust = TRUE, qtl = TRUE, verbose = FALSE)
genotypes |
Matrix of genotypes. (individuals x markers) |
phenotypes |
Matrix of phenotypes. (individuals x phenotypes) |
phenocol |
Which phenotype column(s) should we analyse. Default: Analyse a single phenotype. |
nperm |
Number of permutations to perform. This parameter is not used when method="Exact". |
nthreads |
Number of CPU cores to use during the analysis. |
strategy |
The permutation strategy to use, either
Note: Exact is the default and fastest option it uses a normal distribution for estimating p-values and uses bonferoni correction. It has however the least power to detect CTLs, the two other methods (Full and Pairwise) perform permutations to assign significance. |
adjust |
Adjust p-values for multiple testing (only used when strategy = Exact). |
qtl |
Use the internal slow QTL mapping method to map QTLs. |
verbose |
Be verbose. |
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NOTE: Main bottleneck of the algorithm is the RAM available to the system
CTLscan, a list of:
$dcor - Matrix of differential correlation scores for each trait at each marker
$perms - Vector of maximums per marker obtained during permutations
$ctls - Matrix of LOD scores for CTL likelihood
TODO
Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com
TODO
CTLscan
- Main function to scan for CTL
CTLscan.cross
- Use an R/qtl cross object with CTLscan
CTLsignificant
- Significant interactions from a CTLscan
plot.CTLscan
- Plot the CTL curve for a single trait
library(ctl)
data(ath.metabolites) # Arabidopsis Thaliana dataset
singlescan <- CTLmapping(ath.metab$genotypes, ath.metab$phenotypes, phenocol = 23)
plot(singlescan) # Plot the results of the CTL scan for the phenotype
summary <- CTLsignificant(singlescan)
summary # Get a list of significant CTLs