| NOIA matrix manipulation {noia} | R Documentation |
These functions perform the matrix computation required for the computation of genetic effects and Genotype-to-Phenotype mapping.
gen2Z(gen)
gen2genZ(gen)
genZ2freq(genZ)
genZ2S(genZ=NULL, reference="F2", nloc=NULL, max.level=NULL,
max.dom=NULL)
genZ2Z(genZ)
genZ2ZS(genZ, reference="F2", max.level=NULL, max.dom=NULL,
threshold=0)
genZ2X(genZ, reference="F2", max.level=NULL, max.dom=NULL)
checkgenZ(genZ, tol=0.0001)
Z2freq(Z)
partialX(genZ, reference="F2", effect)
Sloc(reference="F2", i=NULL, genZ=NULL)
gen |
The matrix of genotypes, one column per locus, the genotype is
coded 1, 2, 3. Missing data are allowed. |
genZ |
The matrix of genotypic probabilities, 3 columns per locus (one for the probability of each genotype). The sum of probabilities must be 1, and missing data are not allowed. |
reference |
The reference of the population. "F2", "F1",
"Finf". "P1", "P2, "G2A", "UWR" and
"noia" are possible. Default is "F2". |
nloc |
Number of loci. |
max.level |
Maximum level of interactions. |
max.dom |
Maximum level for dominance effects. |
threshold |
Frequency threshold from which a genotype is taken into account. Values other than 0 might bias the results. |
Z |
A matrix reflecting the genotype of the corresponding observed phenotypes, as defined in Alvarez-Castro and Carlborg 2007. |
tol |
A tolerance factor, featuring how much the sum of genotypic frequencies can be different from 1. |
effect |
The name of a genetic effect (such as ".ad"). |
i |
Index of the locus. |
gen2Zgen data set into a Z matrix that
is the data matrix in the regression. The function actually calls sequencially
gen2genZ and genZ2Z. gen2genZgen matrix into a genZ matrix. genZ2freqgenZ2SS matrix (see Alvarez-Castro and Carlborg
2007) for a given reference point. Some reference points are genotypic
frequency-dependent ("G2A" and "noia"), and the genZ
matrix must be provided. For the others, only the number of loci is
necessary. genZ2ZZ matrix from the genotypic
probabilities. See Alvarez-Castro and Carlborg 2007 for more details. genZ2ZSZ and S matrices at the same time. This is highly efficient when many genotypes are not represented in the dataset. The function returns a list of two elements "zmat" and "smat".genZ2XZ and S matrices without building them. This is very efficient when considering only low-level interactions. checkgenZgenZ matrix. Z2freqpartialXZ and S matrices, keeping Z and S as small as possible considering a given effect effect. SlocS matrix, corresponding to one locus.
Frequency-dependent reference points will require the genZ matrix
and the index of the locus. Arnaud Le Rouzic <a.p.s.lerouzic@bio.uio.no>
Alvarez-Castro JM, Carlborg O. (2007). A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 176(2):1151-1167.
Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics, 4.
linearRegression, multilinearRegression
set.seed(123456789) map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25) names(map) <- genNames(2) pop <- simulatePop(map, N=500, sigmaE=0.2, type="F2") gen <- pop[2:3] genZ <- gen2genZ(gen) Z <- genZ2Z(genZ)