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. |
gen2Z
: Transforms a gen
data set into a Z
matrix that is the data matrix in the regression. The function actually calls sequencially gen2genZ
and genZ2Z
.
gen2genZ
: Transforms a gen
matrix into a genZ
matrix.
genZ2freq
: Provides a vector representing the frequency of each genotypic form at each locus. The sum of the frequency is 1 for each locus.
genZ2S
: Provides the S
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.
genZ2Z
: Computes the Z
matrix from the genotypic probabilities. See Alvarez-Castro and Carlborg 2007 for more details.
genZ2ZS
: Computes Z
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"
.
genZ2X
: Computes the product of Z
and S
matrices without building them. This is very efficient when considering only low-level interactions.
checkgenZ
: Checks the structure of the genZ
matrix.
Z2freq
: Computes the multi-locus genotypic frequency over all genotypic combinations.
partialX
: Computes the product of Z
and S
matrices, keeping Z
and S
as small as possible considering a given effect effect
.
Sloc
: Provides a 3x3 S
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)