R/RGWAS.menu.R

In RAINBOWR: Genome-Wide Association Study with SNP-Set Methods

#' Print the R code which you should perform for RAINBOWR GWAS
#' @description Print the R code which you should perform for RAINBOWR (Reliable Association INference By Optimizing Weights with R).
#' @return The R code which you should perform for RAINBOWR GWAS
#'
#'
RGWAS.menu <- function() {
  test.methods <- c("'LR'", "'score'")
  gene.sets <- c("gene.set", "NULL")
  kernel.methods <- c("'linear'", "'gaussian'", "'exponential'")
  test.effects <-
    c("'additive'", "'dominance'", "'additive+dominance'")
  weighting.centers <- c("TRUE", "FALSE")
  dominance.effs <- c("FALSE", "TRUE")

  Q1 <- menu(
    c(
      "Normal GWAS; testing each single SNP",
      "Kernel-based GWAS; testing multiple SNPs simultaneously",
      "Check epistatic effects"
    ),
    title = "Which GWAS do you want to perform?"
  )

  if (Q1 != 1) {
    Q2 <- menu(
      c(
        "One step method; perform kerenel method for all SNPs",
        "Two step method; perform kerenel method for SNPs which were detected by normal GWAS"
      ),
      title = "Which method do you want to perform?"
    )

    Q3 <-
      menu(
        c(
          "LR; likelihood-ratio test, relatively slow, but accurate (default)",
          "score; score test, much faster than LR, but sometimes overestimate -log10(p)"
        ),
        title = "Kernel-based GWAS have two methods to test SNPs. Which do you want to use?"
      )

    Q4 <-
      menu(
        c(
          "Yes; when you input your gene information, see Arguments of 'gene.set' for details of its format",
          "No; perform tests for each SNP-set containing a fixed number of SNPs"
        ),
        title = "Do you have some gene information? RGWAS can perform gene-set based GWAS."
      )

    Q5 <- menu(
      c(
        "Yes; RGWAS will only test for additive effects",
        "No; you can choose which effects to test"
      ),
      title = "Do you use inbred lines?"
    )

    if (Q1 == 2) {
      Q6 <-
        menu(
          c(
            "linear; fastest especially when the number of SNPs in SNP-set is small (default)",
            "gaussian; slower than linear (especially with LR test), but it may catch non-linear effects in SNP-set",
            "exponential; slower than linear (especially with LR test), but it may have high detection sensitivity"
          ),
          title = "Which kernel do you want to use?"
        )

      if (Q5 == 2) {
        Q7 <- menu(
          c(
            "additive; test additive effects (default)",
            "dominance; test dominance effects, however score test sometimes overestimates this effect",
            "additive+dominance; test both additive and dominance effects simultaneouly"
          ),
          title = "Which genetic effects do you want to test?"
        )
      } else{
        Q7 <- 1
      }

      Q8 <-
        menu(
          c(
            "Yes; weighting is recommended when you perform kernel-based GWAS by sliding windows (default)",
            "No; weighting is not recommended when you perform kernel based GWAS by separated bins"
          ),
          title = "RGWAS can weight the SNP at the center of the SNP-set. Do you want to weight?"
        )
    }
  }



  if (Q1 == 1) {
    func.name <- "RGWAS.normal"
    output <-
      paste0(
        func.name,
        "(pheno, geno, ZETA = ZETA, covariate =  NULL, covariate.factor = NULL, ",
        "structure.matrix = NULL, n.PC = 0, min.MAF = 0.02, P3D = TRUE, n.core = 1, ",
        "sig.level = 0.05, plot.qq = TRUE, plot.Manhattan = TRUE, plot.method = 1, ",
        "plot.col1 = c('dark blue', 'cornflowerblue'), plot.col2 = 1, ",
        "plot.type = 'p', plot.pch = 16, saveName = NULL, main.qq = NULL, ",
        "main.man = NULL, plot.add.last = FALSE, return.EMM.res = FALSE, ",
        "thres = TRUE, verbose = FALSE, count = TRUE, time = TRUE)"
      )
  } else{
    if (Q1 == 2) {
      if (Q2 == 1) {
        func.name <- "RGWAS.multisnp"
        output <-
          paste0(
            func.name,
            "(pheno, geno, ZETA = ZETA, covariate =  NULL, covariate.factor = NULL, ",
            "structure.matrix = NULL, n.PC = 0, min.MAF = 0.02, test.method = ",
            test.methods[Q3],
            ", n.core = 1, kernel.method = ",
            kernel.methods[Q6],
            ", kernel.h = 'tuned', ",
            "haplotype = TRUE, num.hap = NULL, test.effect = ",
            test.effects[Q7],
            ", window.size.half = 5, window.slide = 1, chi0.mixture = 0.5, ",
            "gene.set = ",
            gene.sets[Q4],
            ", weighting.center = ",
            weighting.centers[Q8],
            ", weighting.other = NULL, ",
            "sig.level = 0.05, plot.qq = TRUE, plot.Manhattan = TRUE, plot.method = 1, ",
            "plot.col1 = c('dark blue', 'cornflowerblue'), plot.col2 = 1, ",
            "plot.type = 'p', plot.pch = 16, saveName = NULL, main.qq = NULL, ",
            "main.man = NULL, plot.add.last = FALSE, return.EMM.res = FALSE, ",
            "thres = TRUE, verbose = FALSE, count = TRUE, time = TRUE)"
          )
      } else{
        func.name <- "RGWAS.twostep"
        output <-
          paste0(
            func.name,
            "(pheno, geno, ZETA = ZETA, covariate =  NULL, covariate.factor = NULL, ",
            "structure.matrix = NULL, n.PC = 0, min.MAF = 0.02, n.core = 1, ",
            "check.size = 40, check.gene.size = 4, kernel.percent = 0.1, GWAS.res.first = NULL, ",
            "P3D = TRUE, test.method.1 = 'normal', test.method.2 = ",
            test.methods[Q3],
            ", kernel.method = ",
            kernel.methods[Q6],
            ", kernel.h = 'tuned', ",
            "haplotype = TRUE, num.hap = NULL, test.effect.1 = ",
            test.effects[Q7],
            ", test.effect.2 = ",
            test.effects[Q7],
            ", window.size.half = 5, window.slide = 1, chi0.mixture = 0.5, ",
            "gene.set = ",
            gene.sets[Q4],
            ", weighting.center = ",
            weighting.centers[Q8],
            ", weighting.other = NULL, ",
            "sig.level = 0.05, plot.qq.1 = TRUE, plot.Manhattan.1 = TRUE, ",
            "plot.qq.2 = TRUE, plot.Manhattan.2 = TRUE, plot.method = 1, ",
            "plot.col1 = c('dark blue', 'cornflowerblue'), plot.col2 = 1, ",
            "plot.col3 = c('red3', 'orange3'), plot.type = 'p', plot.pch = 16, saveName = NULL, ",
            "main.qq.1 = NULL, main.man.1 = NULL, main.qq.2 = NULL, main.man.2 = NULL, ",
            "plot.add.last = FALSE, return.EMM.res = FALSE, ",
            "thres = TRUE, verbose = FALSE, count = TRUE, time = TRUE)"
          )
      }
    } else{
      if (Q2 == 1) {
        func.name <- "RGWAS.epistasis"
        output <-
          paste0(
            func.name,
            "(pheno, geno, ZETA = ZETA, covariate =  NULL, covariate.factor = NULL, ",
            "structure.matrix = NULL, n.PC = 0, min.MAF = 0.02, n.core = 1, test.method = ",
            test.methods[Q3],
            ", haplotype = TRUE, dominance.eff = ",
            dominance.effs[Q5],
            ", num.hap = NULL, window.size.half = 5, window.slide = 1, ",
            "chi0.mixture = 0.5, ",
            "gene.set = ",
            gene.sets[Q4],
            ", plot.epi.3d = TRUE, plot.epi.2d = TRUE, main.epi.3d = NULL, ",
            "main.epi.2d = NULL, saveName = NULL, ",
            "verbose = FALSE, count = TRUE, time = TRUE)"
          )
      } else{
        func.name <- "RGWAS.twostep.epi"
        output <-
          paste0(
            func.name,
            "(pheno, geno, ZETA = ZETA, covariate =  NULL, covariate.factor = NULL, ",
            "structure.matrix = NULL, n.PC = 0, min.MAF = 0.02, n.core = 1, ",
            "check.size.epi = 4, epistasis.percent = 0.1, check.epi.max = 200, ",
            "your.check = NULL, GWAS.res.first = NULL, ",
            "P3D = TRUE, test.method = ",
            test.methods[Q3],
            ", haplotype = TRUE, ",
            "dominance.eff = ",
            dominance.effs[Q5],
            ", num.hap = NULL, window.size.half = 5, ",
            "window.slide = 1, chi0.mixture = 0.5, gene.set = ",
            gene.sets[Q4],
            ", sig.level = 0.05, plot.qq.1 = TRUE, plot.Manhattan.1 = TRUE, ",
            "plot.epi.3d = TRUE, plot.epi.2d = TRUE, plot.method = 1, ",
            "plot.col1 = c('dark blue', 'cornflowerblue'), plot.col2 = 1, ",
            "plot.type = 'p', plot.pch = 16, saveName = NULL, ",
            "main.qq.1 = NULL, main.man.1 = NULL, main.epi.3d = NULL, main.epi.2d = NULL, ",
            "verbose = FALSE, count = TRUE, time = TRUE)"
          )
      }
    }
  }


  return(output)
}