estPhylo, plotPhyloTree, estNetwork, and plotHaploNetwork functions to fix some errors.calcGRM function so that it can compute the genomic relationship matrix with the input of marker genotype scored with {0, 1} or {-1, 0}.calcGRM function so that it can compute the genomic relationship matrix with the input of marker genotype scored with {0, 1, 2}.See function so that it can show the shorter results for list objects.method = "Sidak" option in the CalcThreshold function.EMM_functions.cpp by explicitly extracting each element from the matrix by using .coeff.nCores to n.core and added the argument parallel.method in the estPhylo and estNetwork functions.estPhylo and estNetwork functions so that they can perform parallel computing even in Windows OS by using the parallel.compute function.estPhylo and estNetwork functions so that they can return a n x h matrix where n is the number of genotypes and h is the number of haplotypes for each block of interest. Also, they were extended so that they could perform k-medoids clustering to define the haplotype when the number of haplotypes is predefined.parallel.compute function so that parallel.method = 'foreach' option can return the same values as parallel.method = 'mclapply'.max.HE argument to the MAF.cut function so that it can also remove markers with a large heterozygous rate.score.calc and score.calc.MC functions.gene.set argument beforehand inside the RGWAS.multisnp, RGWAS.epistasis, and RGWAS.multisnp.interaction functions.score.calc.LR, score.calc.LR.MC, and EM3.general functions.See function when showing array objects.We added subpop argument to calcGRM function. By utilizing subpop argument, you can consider the difference of allele frequencies between sub-populations when computing the genomic relationship matrix. This argument is only valid when NOIA methods are selected.
We added how to compute the marker effects from the GBLUP results in the example of EMM.cpp function.
We implemented new functions for testing the interaction between each SNP-set (haplotype block) and the genetic background (or epistasis with polygenes). The functions to compute p-values for those effects are score.calc.LR.int and score.calc.LR.int.MC, and the function to perform haplotype-block based GWAS including such interaction effects is RGWAS.multisnp.interaction. We also added the function to NAMESPACE file.
We implemented a new function adjustGRM, which adjusts genomic relationship matrices when there is population structure. The function utilizes the true/estimated sub-population information (population membership) to estimated each variance component corresponding to each sub-population. We added the function to NAMESPACE file.
We implemented a new function convertBlockList, which converts a list of haplotype blocks estimaed by PLINK to the format which can be inputted as a gene.set argument in RGWAS.multisnp, RGWAS.multisnp.interaction, and RGWAS.epistasis functions. We added the function to NAMESPACE file. We also added the data.table package to the Suggests list in the DESCRIPTION file.
We removed the RGWAS.normal function from the imported functions in NAMESPACE file because the function did not catch up with the latest version of the RAINBOWR package.
We added a new argument map.gene.set to RGWAS.multisnp, RGWAS.multisnp.interaction, RGWAS.epstasis, RGWAS.twostep, and RGWAS.twostep.epi functions. If this argument is NULL, the map will be constructed by genesetmap function after the SNP-set GWAS. It will take some time, so you can reduce the computational time by assigning this argument beforehand.
We also updated the README.md and RAINBOWR.md for vignettes.
RGWAS.normal, RGWAS.normal.interaction, RGWAS.multisnp, RGWAS.epstasis, RGWAS.twostep, and RGWAS.twostep.epi functions because there were some modifications with the function arguments.ggtree package from Imports list, and moved it to Suggests list in the DESCRIPTION file. By doing this, errors caused by the installation of ggtree package are now eliminated.We implemented new functions for testing the interaction between each SNP and the genetic background. The functions to compute p-values for those effects are score.calc.int and score.calc.int.MC, and the function to perform SNP-based GWAS including such interaction effects is RGWAS.normal.interaction. We also added the to NAMESPACE file.
We implemented a new function is.diag, which judges a matrix is diagonal or not.
We implemented a new function parallel.compute, which enables us to perform parallel computation easily with the three different methods: mclapply, furrr, and foreach. This function is utilized in score.calc.MC, score.calc.LR.MC, score.calc.score.MC, score.calc.epistasis.LR.MC, score.calc.epistasis.score.MC, and score.calc.int.MC functions.
We implemented a new function EM3.general, which enables us to solve mixed-effects model with the three different packages: RAINBOWR, gaston, and MM4LMM.
We also added the four functions above to the NAMESPACE file to be exported correctly.
We fixed some mistakes in EM3.cpp and EM3.linker.cpp functions when computing Vinv. We also added new arguments of return.u.always, return.u.each, and return.Hinv, and a new return of u.each as a u in the older version. We also modified a return of u to the summation of genotypic values.
We rewrote the codes in score.calc, score.calc.MC, and GWAS_F_test (C++) functions so that it can test multiple fixed effects simultaneously.
We fixed some parts in RGWAS.normal, RGWAS.multisnp, and RGWAS.epstasis functions when using covariate, covariate.factor, and structure.matrix arguments.
We added a new argument skip.check in RGWAS.normal, RGWAS.multisnp, RGWAS.epstasis, RGWAS.twostep, and RGWAS.twostep.epi functions. As default, RAINBOWR checks the type of input data and modifies it into the correct format. However, it will take some time, so if you prepare the correct format of input data, you can skip this procedure by setting skip.check = TRUE.
We introduced a new argument package.MM in RGWAS.normal, RGWAS.multisnp, RGWAS.epstasis, RGWAS.twostep, RGWAS.twostep.epi, score.calc, score.calc.MC, score.calc.LR, score.calc.LR.MC, score.calc.epistasis.LR, score.calc.epistasis.LR.MC, score.calc.int, and score.calc.int.MC functions. By changing this argument, you can choose which package is used to solve the mixed-effects model in GWAS from the following three packages: RAINBOWR, gaston, and MM4LMM.
We introduced a new argument parallel.method in RGWAS.normal, RGWAS.multisnp, RGWAS.epstasis, RGWAS.twostep, RGWAS.twostep.epi, score.calc.MC, score.calc.LR.MC, score.calc.score.MC, score.calc.epistasis.LR.MC, score.calc.epistasis.score.MC, and score.calc.int.MC functions. In the older version, you can just use the pbmcapply::pbmclapply function for parallel computation, but now you can choose the parallel computation method from the following three methods: mclapply, furrr, and foreach.
We largely corrected the functions related to epstatic tests; RGWAS.epistasis, RGWAS.twostep.epi, score.calc.epistasis.LR, score.calc.epistasis.score, score.calc.epistasis.LR.MC, and score.calc.epistasis.score.MC functions. This is because how to compute the interaction term between two haplotype blocks was wrong in the older version. We also added a new argument skip.self.int to choose whether skipping the computation for the epistatsis of self-interaction.
We added gaston and MM4LMM packages to Imports list in the DESCRIPTION file because these packages are required when you use these packages with package.MM argument. We also added lmm.aireml, lmm.diago, and MMEst functions to be imported in the NAMESPACE file.
We added adegenet, furrr, future, progressr, foreach, and doParallel packages to Suggests list in the DESCRIPTION file according to the implementation of new functions and arguments.We also removed ggplot2, ggtree, scatterpie, and phylobase packages from Imports list, and moved it to Suggests list in the DESCRIPTION file.
haplotypes package from Imports list, and moved it to Suggests list in the DESCRIPTION file. This is because the package phangorn, which is used in the haplotypes package, is now scheduled for archival on 2021-04-19.We added a dependency on Rfast package to perform the faster computation of distance matrix. We also added an option of pamonce = 5 in cluster::pam function to save the cost of performing k-medoids analysis.
We removed a dependency on pblapply package since the package is actually not needed in the RAINBOWR package. We also removed parallel package from Imports list in DESCRIPTION file because parallel package is initially installed in R. We also removed plotly package from Imports list, and moved it to Suggests list in the DESCRIPTION file.
rgl package since the future of the rgl package is unstable. Instead, we used plot_ly function in plotly package to draw 3d plots for results of epistasis GWAS. We added dependency on plotly, here, and htmlwidgets packages.estPhylo function.genesetmap function related to the case where the markers in haplotype block list are not included in the marker genotype.EMM1.cpp function). We also added the argument n.core for the functions that includes the mixed-effects model by EMM1.cpp.We modified the estPhylo and estNetwork functions. Now, these functions can output the ggplot version of phylogenetic trees or haplotype networks. We also added ggtree, ggplot2, scatterpie, phylobase, haplotypes, and ggimage packages in the NAMESPACE and DESCRIPTION files because these packages are used in the estPhylo and estNetwork functions.
We also added the plotPhyloTree and plotHaploNetwork functions to plot phylogenetic tree / haplotype network from the estimated results.
We added the estNetwork function to estimate and plot haplotype network. We also added pegas package in the NAMESPACE and DESCRIPTION files because this package is used in the estNetwork function.
We fixed some parts related to the estimation of haplotype effects in the estPhylo function. We also added the optimization part both in the estPhylo & estNetwork functions.
We added the calcGRM function to calculate genomic relationship matrix (GRM). The parts that calculate GRMs in other functions and examples were replaced to use calcGRM function. We also added stringr package in the NAMESPACE and DESCRIPTION files because these packages are used in the calcGRM function.
We fixed some parts related to estimate the siginificance of dominance effects in score.calc.LR, score.calc.LR.MC, score.calc.score, score.calc.score.MC, score.epistasis.LR, score.epistasis.score functions.
spectralG.cpp function was fixed thanks to Dr. Ishimori.class(obj) == "try-error" was modified to try-error %in% class(obj) in order to deal with the cases where the obj has more than one class (to avoid warnings).Found the following (possibly) invalid URLs:
URL: https://cran.r-project.org/web/packages/RAINBOWR/index.html
From: inst/doc/RAINBOWR.html
README.md
Status: 200
Message: OK
CRAN URL not in canonical form
The canonical URL of the CRAN page for a package is
https://CRAN.R-project.org/package=pkgname
Then, we fixed this by using the canonical URL of the CRAN package "https://cran.r-project.org/package=RAINBOWR" in inst/doc/RAINBOWR.html and README.md files.
We added the citation file for RAINBOWR becuase our paper about RAINBOWR had been published in PLOS Computational Biology (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663). We also changed the Description in the DESCRIPTION file to update the reference information.
We added the estPhylo function to estimate and plot the phylogenetic tree for the block of interest. This function will also estimate the genotypic values for the block of interest. We also added ape and cluster packages in the NAMESPACE and DESCRIPTION files because these packages are used in the estPhylo function.
We fixed some parts related to the treatment of the missing in marker genotypes.
As the previous version, if the marker genotype has missing values, errors will be occurred in RGWAS.normal, RGWAS.multisnp, RGWAS.epistasis, RGWAS.twostep, and RGWAS.twostep.epi functions.
Then, we changed the R code in the RGWAS.normal, RGWAS.multisnp, and RGWAS.epistasis functions as follows.
Before
M.now <- Z.A[not.NA, ] %*% M
if (sum(is.na(M)) == 0) {
M.now <- Z.A[not.NA, ] %*% M
} else {
M.now <- M[apply(Z.A[not.NA, ], 1, function(x) which(x == 1)), ]
}