Error checks and plotting speedups for dive_phe2mash function

DESCRIPTION

@@ -15,10 +15,13 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
 Imports: 
     ashr,
+    bigassertr,
     bigsnpr,
     bigstatsr,
+    cluster,
     cowplot,
     dplyr,
+    GGally,
     ggplot2,
     mashr,
     magrittr,
@@ -26,6 +29,8 @@ Imports:
     purrr,
     readr,
     rlang (>= 0.1.2),
+    rlist,
+    stringr,
     tibble,
     tidyr,
     tidyselect

NAMESPACE

@@ -11,25 +11,44 @@ export(dive_phe2mash)
 export(enquo)
 export(enquos)
 export(expr)
+export(get_GxE)
+export(get_U_by_mass)
 export(get_lambdagc)
+export(get_pairwise_sharing)
 export(get_qqplot)
+export(get_significant_results)
+export(mash_plot_Ulist)
+export(mash_plot_covar)
+export(mash_plot_manhattan_by_condition)
+export(mash_plot_marker_effect)
+export(mash_plot_pairwise_sharing)
+export(mash_plot_sig_by_condition)
 export(sym)
 export(syms)
 import(bigsnpr)
 import(bigstatsr)
 import(ggplot2)
 import(mashr)
+importFrom(GGally,ggcorr)
 importFrom(ashr,get_fitted_g)
+importFrom(ashr,get_lfsr)
+importFrom(ashr,get_pm)
+importFrom(ashr,get_psd)
+importFrom(bigassertr,printf)
 importFrom(bigsnpr,snp_autoSVD)
+importFrom(cluster,daisy)
 importFrom(cowplot,save_plot)
 importFrom(dplyr,arrange)
+importFrom(dplyr,between)
 importFrom(dplyr,case_when)
+importFrom(dplyr,desc)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,mutate_if)
+importFrom(dplyr,n)
 importFrom(dplyr,rename)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
@@ -52,11 +71,15 @@ importFrom(rlang,enquos)
 importFrom(rlang,expr)
 importFrom(rlang,sym)
 importFrom(rlang,syms)
+importFrom(rlist,list.append)
+importFrom(stats,hclust)
 importFrom(stats,median)
 importFrom(stats,ppoints)
 importFrom(stats,predict)
 importFrom(stats,qbeta)
 importFrom(stats,uniroot)
+importFrom(stringr,str_replace)
+importFrom(stringr,str_replace_all)
 importFrom(tibble,add_column)
 importFrom(tibble,add_row)
 importFrom(tibble,as_tibble)
@@ -64,6 +87,8 @@ importFrom(tibble,enframe)
 importFrom(tibble,rownames_to_column)
 importFrom(tibble,tibble)
 importFrom(tidyr,gather)
+importFrom(tidyr,pivot_longer)
 importFrom(tidyr,replace_na)
+importFrom(tidyr,separate)
 importFrom(tidyselect,all_of)
 importFrom(utils,tail)

R/wrapper.R

@@ -42,6 +42,7 @@
 #' @param U.hyp Other covariance matrices for mash. Specify these as a list. These
 #'     matrices must have dimensions that match the number of phenotypes where
 #'     univariate GWAS ran successfully.
+#' @param verbose Output some information on the iterations? Default is `TRUE`.
 #'
 #' @return A mash object made up of all phenotypes where univariate GWAS ran
 #'     successfully.
@@ -56,6 +57,7 @@
 #' @importFrom tidyr replace_na
 #' @importFrom matrixStats colMaxs rowMaxs
 #' @importFrom stats predict
+#' @importFrom bigassertr printf
 #'
 #' @export
 dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
@@ -64,7 +66,7 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
                           thr.m = c("sum", "max"), num.strong = 1000,
                           num.random = NA,
                           scale.phe = TRUE, roll.size = 50, U.ed = NA,
-                          U.hyp = NA){
+                          U.hyp = NA, verbose = TRUE){
   # 1. Stop if not functions. ----
   if (attr(snp, "class") != "bigSNP") {
     stop("snp needs to be a bigSNP object, produced by the bigsnpr package.")
@@ -80,6 +82,9 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
   } else {
     type <- rep(type, ncol(df) - 1)
   }
+  if (!dir.exists(outputdir)) {
+    dir.create(outputdir)
+  }
 
   ## 1a. Generate useful values ----
   G <- snp$genotypes
@@ -94,12 +99,13 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
   bonferroni <- -log10(0.05/length(snp$map$physical.pos))
   markers <- tibble(CHR = snp$map$chromosome, POS = snp$map$physical.pos,
                     marker.ID = snp$map$marker.ID) %>%
-    mutate(CHRN = as.numeric(as.factor(.data$CHR)))
+    mutate(CHRN = as.numeric(as.factor(.data$CHR)),
+           CHR = as.factor(.data$CHR))
 
   # 2. Pop Structure Correction  ----
   if (is.null(svd)) {
-    message(paste0("Covariance matrix (svd) was not supplied - this will be",
+    printf2(verbose = verbose, "\nCovariance matrix (svd) was not supplied - ")
-                   " generated using snp_autoSVD()."))
+    printf2(verbose = verbose, "\nthis will be generated using snp_autoSVD()")
     svd <- snp_autoSVD(G = G, infos.chr = markers$CHRN, infos.pos = markers$POS,
                        k = 10, thr.r2 = thr.r2, roll.size = roll.size)
     } else {
@@ -107,6 +113,7 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
       }
   pc_max <- ncol(svd$u)
   gwas_ok <- c()
+  first_gwas_ok <- FALSE
 
   for (i in 2:ncol(df)) {
     df1 <- df %>%
@@ -127,8 +134,10 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
     gwas_ok[i-1] <- check_gwas(df1 = df1, phename = phename, type = type[i-1],
                                nPhe = nPhe, minphe = min.phe, nLev = nLev)
 
+
     # Find best # PCs to correct for population structure for each phenotype.
     if(gwas_ok[i-1]){
+
       lambdagc_df <- div_lambda_GC(df = df1, type = type[i-1], snp = snp,
                                    svd = svd, npcs = c(0:pc_max))
       PC_df <- get_best_PC_df(lambdagc_df)
@@ -145,29 +154,22 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
                         nsnp = nSNP, npcs = PC_df$NumPCs, nphe = nPhe,
                         nlev = nLev, lambda_GC = PC_df$lambda_GC,
                         bonferroni = bonferroni)
-    # plot Manhattan and QQ if save.plots == TRUE
+
-    if(save.plots == TRUE){
+    # plot  QQ if save.plots == TRUE
+    if (save.plots == TRUE) {
       qqplot <- get_qqplot(ps = gwas$pvalue, lambdaGC = TRUE)
-      manhattan <- get_manhattan(log10p = gwas$log10p, snp = snp,
+      }
-                                 thresh = bonferroni)  # could round these too
-      plotname <- paste0(gwas_data$phe, "_", gwas_data$type, "_model_",
-                         gwas_data$nphe, "g_", gwas_data$nsnp, "_SNPs_",
-                         gwas_data$npcs, "_PCs.png")
-      save_plot(filename = file.path(outputdir, paste0("QQplot_", plotname)),
-                plot = qqplot, base_asp = 1, base_height = 4)
-      save_plot(filename = file.path(outputdir, paste0("Manhattan_", plotname)),
-                plot = manhattan, base_asp = 2.1, base_height = 3.5)
-    }
 
     # save gwas outputs together in a fbm
     gwas <- gwas %>%
       select(.data[["estim"]], .data[["std.err"]], .data[["log10p"]])
 
-    if(i == 2){     # save .bk and .rds file the first time through the loop.
+    if(!first_gwas_ok){     # save .bk and .rds file the first time through the loop.
       if (!grepl("_$", suffix) & suffix != ""){
         suffix <- paste0("_", suffix)
       }
-      fbm.name <- paste0(outputdir, "gwas_effects", suffix)
+      first_gwas_ok <- TRUE
+      fbm.name <- file.path(outputdir, paste0("gwas_effects", suffix))
 
       colnames_fbm <- c(paste0(phename, "_Effect"), paste0(phename, "_SE"),
                        paste0(phename, "_log10p"))
@@ -179,18 +181,43 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
       colnames_fbm <- c(colnames_fbm, paste0(phename, "_Effect"),
                         paste0(phename, "_SE"), paste0(phename, "_log10p"))
       gwas2$add_columns(ncol_add = 3)
-      gwas2[,c(i*3-5, i*3-4, i*3-3)] <- gwas
+      gwas2[, c(sum(gwas_ok)*3 - 2, sum(gwas_ok)*3 - 1,
+                sum(gwas_ok)*3)] <- gwas
       gwas2$save()
-      gwas_metadata <- add_row(gwas_metadata, phe = phename, type = type[i-1],
+      gwas_metadata <- add_row(gwas_metadata, phe = phename, type = type[i - 1],
                            nsnp = nSNP, npcs = PC_df$NumPCs, nphe = nPhe,
                            nlev = nLev, lambda_GC = PC_df$lambda_GC,
                            bonferroni = bonferroni)
     }
+    # plot Manhattan and QQ if save.plots == TRUE
+    if (save.plots == TRUE) {
+      # set aspect ratio based on number of SNPs in snp file
+      asp <- log10(snp$genotypes$ncol)/2
+      if(asp < 1.1){
+        asp <- 1.1
+      }
+
+      manhattan <- get_manhattan(X = gwas2, ind = sum(gwas_ok)*3, snp = snp,
+                                 thresh = bonferroni)
+      plotname <- paste0(gwas_data$phe, "_", gwas_data$type, "_model_",
+                         gwas_data$nphe, "g_", gwas_data$nsnp, "_SNPs_",
+                         gwas_data$npcs, "_PCs.png")
+      save_plot(filename = file.path(outputdir, paste0("QQplot_", plotname)),
+                plot = qqplot, base_asp = 1, base_height = 4)
+      save_plot(filename = file.path(outputdir, paste0("Manhattan_", plotname)),
+                plot = manhattan, base_asp = asp, base_height = 3.75)
+
+    }
   rm(gwas)
-  message(paste0("Finished phenotype ", i-1, ": ", names(df)[i]))
+  printf2(verbose = verbose, "\nFinished GWAS on phenotype %s. ",
+          names(df)[i])
+    } else {
+      printf2(verbose = verbose, "\nSkipping GWAS on phenotype %s. ",
+              names(df)[i])
     }
   }
 
+  printf2(verbose = verbose, "\nNow preparing gwas effects for use in mash.\n")
   # 4. mash input ----
       ## prioritize effects with max(log10p) or max(sum(log10p))
       ## make a random set of relatively unlinked SNPs
@@ -198,7 +225,7 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
   ind_se <- (1:sum(gwas_ok))*3 - 1
   ind_p <- (1:sum(gwas_ok))*3
 
-  if(thr.m == "sum"){
+  if (thr.m == "sum") {
   thr_log10p <- big_apply(gwas2,
                          a.FUN = function(X, ind) rowSums(X[, ind]),
                          ind = ind_p,
@@ -211,21 +238,21 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
   }
   gwas2$add_columns(ncol_add = 1)
   colnames_fbm <- c(colnames_fbm, paste0(thr.m, "_thr_log10p"))
-  gwas2[,(sum(gwas_ok)*3+1)] <- thr_log10p
+  gwas2[,(sum(gwas_ok)*3 + 1)] <- thr_log10p
   gwas2$save()
 
     ## replace NA or Nan values
   # Replace SE with 1's, estimates and p values with 0's.
   replace_na_1 <- function(X, ind) replace_na(X[, ind], 1)
   replace_na_0 <- function(X, ind) replace_na(X[, ind], 0)
-  gwas2[,ind_se] <- big_apply(gwas2, a.FUN = replace_na_1, ind = ind_se,
+  gwas2[, ind_se] <- big_apply(gwas2, a.FUN = replace_na_1, ind = ind_se,
                               a.combine = 'plus')
-  gwas2[,ind_estim] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_estim,
+  gwas2[, ind_estim] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_estim,
                                  a.combine = 'plus')
-  gwas2[,ind_p] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_p,
+  gwas2[, ind_p] <- big_apply(gwas2, a.FUN = replace_na_0, ind = ind_p,
                                  a.combine = 'plus')
-  gwas2[,(sum(gwas_ok)*3+1)] <- big_apply(gwas2, a.FUN = replace_na_0,
+  gwas2[, (sum(gwas_ok)*3+1)] <- big_apply(gwas2, a.FUN = replace_na_0,
-                                          ind = (sum(gwas_ok)*3+1),
+                                          ind = (sum(gwas_ok)*3 + 1),
                                           a.combine = 'plus')
   gwas2$save()
 
@@ -301,31 +328,26 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
   # 5. mash ----
 
   data_r <- mashr::mash_set_data(Bhat_random, Shat_random)
-  message(paste0("Estimating the correlation structure in the null tests from ",
+  printf2(verbose = verbose, "\nEstimating correlation structure in the null tests from a random sample of clumped data.")
-                 "the random data.
-                 (not the strong data because it will not necessarily contain
-                 any null tests)."))
   Vhat <- mashr::estimate_null_correlation_simple(data = data_r)
 
-  message(paste0("Setting up the main data objects with this correlation ",
-                 "structure in place."))
   data_strong <- mashr::mash_set_data(Bhat_strong, Shat_strong, V = Vhat)
   data_random <- mashr::mash_set_data(Bhat_random, Shat_random, V = Vhat)
   data_full <- mashr::mash_set_data(Bhat_full, Shat_full, V = Vhat)
   U_c <- mashr::cov_canonical(data_random)
 
-  if (!is.na(U.ed[1])) {
+  if (is.na(U.ed[1])) {
-  message(paste0("Now estimating data-driven covariances using the strong",
+    printf2(verbose = verbose, "\nNow estimating data-driven covariances using
-                 " tests.
+    the strong tests. NB: This step may take some time to complete.\n")
-                 NB: This step may take some time to complete."))
+    if (length(ind_p) < 6) {
-  if (length(ind_p) < 6) {
+      cov_npc <- ind_p - 1
-    cov_npc <- ind_p - 1
+    } else {
-  } else {
+      cov_npc <- 5
-    cov_npc <- 5
+    }
-  }
+    U_pca = mashr::cov_pca(data_strong, npc = cov_npc)
-  U_pca = mashr::cov_pca(data_strong, npc = cov_npc)
+    U_ed = mashr::cov_ed(data_strong, U_pca)
-  U_ed = mashr::cov_ed(data_strong, U_pca)
+    saveRDS(U_ed, file = file.path(outputdir, paste0("Mash_U_ed", suffix,
-  saveRDS(U_ed, file = paste0(outputdir, "Mash_U_ed", suffix, ".rds"))
+                                                     ".rds")))
   } else if (typeof(U.ed) == "list") {
     U_ed <- U.ed
   } else if (typeof(U.ed) == "character") {
@@ -337,12 +359,15 @@ dive_phe2mash <- function(df, snp, type = "linear", svd = NULL, suffix = "",
 
   if (typeof(U.hyp) == "list") {
     m = mashr::mash(data_random, Ulist = c(U_ed, U_c, U.hyp), outputlevel = 1)
-  } else {
+    } else if (typeof(U.hyp) == "character") {
+    U_hyp <- readRDS(file = U.hyp)
+    m = mashr::mash(data_random, Ulist = c(U_ed, U_c, U_hyp), outputlevel = 1)
+    } else {
     m = mashr::mash(data_random, Ulist = c(U_ed, U_c), outputlevel = 1)
+    printf2(verbose = verbose, "\nNo user-specified covariance matrices were included in the mash fit.")
   }
-  message(paste0("Compute posterior matrices for all effects",
+  printf2(verbose = verbose, "\nComputing posterior weights for all effects
-                 " using the mash fit from the
+  using the mash fit from the random tests.")
-               random tests."))
   m2 = mashr::mash(data_full, g = ashr::get_fitted_g(m), fixg = TRUE)
 
   return(m2)
@@ -429,6 +454,9 @@ div_gwas <- function(df, snp, type, svd, npcs){
   return(gwaspc)
 }
 
+#' Verbose?
+#' @importFrom bigassertr printf
+printf2 <- function(verbose, ...) if (verbose) { printf(...) }
 
 #' Create a quantile-quantile plot with ggplot2.
 #'
@@ -544,17 +572,27 @@ round_xy <- function(x, y, cl = NA, cu = NA, roundby = 0.001){
   }
 }
 
-get_manhattan <- function(log10p, snp, thresh){
+get_manhattan <- function(X, ind, snp, thresh){
+  roundFBM <- function(X, ind, at) ceiling(X[, ind] / at) * at
+  observed <- big_apply(X, ind = ind, a.FUN = roundFBM, at = 0.01,
+                        a.combine = 'plus')
+
   plot_data <- tibble(CHR = snp$map$chromosome, POS = snp$map$physical.pos,
-                    marker.ID = snp$map$marker.ID, log10p = log10p) %>%
+                    marker.ID = snp$map$marker.ID, observed = observed)
-    mutate(observed = round2(.data$log10p, at = 0.001)) %>%
+
+  if (length(unique(snp$map$physical.pos)) >= 500000) {
+    plot_data <- plot_data %>%
+      mutate(POS = round2(.data$POS, at = 250000))
+  }
+  plot_data <- plot_data %>%
     group_by(.data$CHR, .data$POS, .data$observed) %>%
-    slice(1)
+    slice(1) %>%
+    mutate(CHR = as.factor(.data$CHR))
 
   nchr <- length(unique(plot_data$CHR))
 
   p1 <- plot_data %>%
-    ggplot(aes(x = .data$POS, y = .data$log10p)) +
+    ggplot(aes(x = .data$POS, y = .data$observed)) +
     geom_point(aes(color = .data$CHR, fill = .data$CHR)) +
     geom_hline(yintercept = thresh, color = "black", linetype = 2,
                size = 1) +
@@ -583,7 +621,7 @@ get_manhattan <- function(log10p, snp, thresh){
           strip.text = element_text(hjust = 0.5, size = 10 ,vjust = 0),
           strip.placement = 'outside', panel.spacing.x = unit(-0.1, 'cm')) +
     labs(x = "Chromosome", y = "-log10(p value)") +
-    scale_x_continuous(expand = c(0.2, 0.2))
+    scale_x_continuous(expand = c(0.15, 0.15))
   return(p1)
 }
 
@@ -737,10 +775,9 @@ get_lambdagc <- function(ps, tol = 1e-8){
 }
 
 
-#' Return best number of PCs in terms of lambda_GC for Panicum virgatum.
+#' Return best number of PCs in terms of lambda_GC
-#' Return best number of PCs in terms of lambda_GC for the CDBN.
 #'
-#' @description Given a dataframe created using pvdiv_lambda_GC, this function
+#' @description Given a dataframe created using div_lambda_GC, this function
 #'     returns the first lambda_GC less than 1.05, or the smallest lambda_GC,
 #'     for each column in the dataframe.
 #'
@@ -812,3 +849,35 @@ check_gwas <- function(df1, phename, type, nPhe, minphe, nLev){
   }
   return(gwas_ok)
 }
+
+
+## @title Basic sanity check for covariance matrices
+## @param X input matrix
+check_covmat_basics = function(x) {
+  label = substitute(x)
+  if (!is.matrix(x))
+    labelled_stop(label, "is not a matrix")
+  if (!is.numeric(x))
+    labelled_stop(label, "is not a numeric matrix")
+  if (any(is.na(x)))
+    labelled_stop(label, "cannot contain NA values")
+  if (any(is.infinite(x)))
+    labelled_stop(label, "cannot contain Inf values")
+  if (any(is.nan(x)))
+    labelled_stop(label, "cannot contain NaN values")
+  if (nrow(x) != ncol(x))
+    labelled_stop(label, "is not a square matrix")
+  if (!isSymmetric(x, check.attributes = FALSE))
+    labelled_stop(label, "is not a symmetric matrix")
+  return(TRUE)
+}
+
+## @title check matrix for positive definitness
+## @param X input matrix
+check_positive_definite = function(x) {
+  check_covmat_basics(x)
+  tryCatch(chol(x),
+           error = function(e) labelled_stop(substitute(x),
+                                             "must be positive definite"))
+  return(TRUE)
+}

man/dive_phe2mash.Rd

@@ -20,7 +20,8 @@ dive_phe2mash(
   scale.phe = TRUE,
   roll.size = 50,
   U.ed = NA,
-  U.hyp = NA
+  U.hyp = NA,
+  verbose = TRUE
 )
 }
 \arguments{
@@ -73,6 +74,8 @@ generating these once and reusing them for multiple mash runs can save time.}
 \item{U.hyp}{Other covariance matrices for mash. Specify these as a list. These
 matrices must have dimensions that match the number of phenotypes where
 univariate GWAS ran successfully.}
+
+\item{verbose}{Output some information on the iterations? Default is \code{TRUE}.}
 }
 \value{
 A mash object made up of all phenotypes where univariate GWAS ran

man/expand_cov.Rd

@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{expand_cov}
+\alias{expand_cov}
+\title{Create expanded list of covariance matrices expanded by
+grid, Sigma_{lk} = omega_l U_k}
+\usage{
+expand_cov(Ulist, grid, usepointmass = TRUE)
+}
+\arguments{
+\item{Ulist}{a list of covarance matrices}
+
+\item{grid}{a grid of scalar values by which the covariance
+matrices are to be sc}
+
+\item{usepointmass}{if TRUE adds a point mass at 0 (null component)
+to the list}
+}
+\value{
+This takes the covariance matrices in Ulist and multiplies
+them by the grid values If usepointmass is TRUE then it adds a null
+component.
+}
+\description{
+This is an internal (non-exported) function. This help
+page provides additional documentation mainly intended for
+developers and expert users.
+}
+\keyword{internal}

man/get_GxE.Rd

@@ -0,0 +1,39 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_GxE}
+\alias{get_GxE}
+\title{Get data frames of types of GxE from a mash object}
+\usage{
+get_GxE(m, factor = 0.4, thresh = 0.05)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{factor}{a number between 0 and 1. The factor within which effects are
+considered to be shared.}
+
+\item{thresh}{Numeric. The threshold for including an effect in the assessment}
+}
+\value{
+A list containing eight data frames. Those with names that start
+"S_" contain significant effects of different types between pairs of
+named rows and columns. S_all_pairwise contains all significant effects;
+NS_pairwise contains all non-significant effects. S_CN contains effects
+significant in only one condition, and effects with a significantly
+different magnitude (differential sensitivity). This dataframe is not
+conservative using the local false sign rate test - we can't determine
+the sign of one of the effects for effects significant in only one
+condition - so it's not recommended to use this, but included. S_2_no
+contains effects significant in both conditions that do not differ
+significantly in magnitude. These effects do not have GxE. S_AP contains
+effects significant in both conditions that differ in their sign - and
+have antagonistic pleiotropy. S_DS contains effects significant in both
+conditions that differ in the magnitude of their effect, but not their
+sign - differentially sensitive alleles. S_1_row and S_1_col contain
+effects that are significant in just one of the two conditions - the row
+or the column, respectively.
+}
+\description{
+Performs set operations to determine pairwise GxE for effects
+from a mash object.
+}

man/get_U_by_mass.Rd

@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_U_by_mass}
+\alias{get_U_by_mass}
+\title{Get the positions of objects in a mash object Ulist that are above
+some mass threshold.}
+\usage{
+get_U_by_mass(m, thresh = 0.05)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{thresh}{Numeric. The mass threshold for including a covariance matrix}
+}
+\description{
+Get the positions of objects in a mash object Ulist that are
+above some mass threshold.
+}

man/get_best_PC_df.Rd

@@ -2,8 +2,7 @@
 % Please edit documentation in R/wrapper.R
 \name{get_best_PC_df}
 \alias{get_best_PC_df}
-\title{Return best number of PCs in terms of lambda_GC for Panicum virgatum.
+\title{Return best number of PCs in terms of lambda_GC}
-Return best number of PCs in terms of lambda_GC for the CDBN.}
 \usage{
 get_best_PC_df(df)
 }
@@ -16,7 +15,7 @@ A dataframe containing the best lambda_GC value and number of PCs
 for each phenotype in the data frame.
 }
 \description{
-Given a dataframe created using pvdiv_lambda_GC, this function
+Given a dataframe created using div_lambda_GC, this function
 returns the first lambda_GC less than 1.05, or the smallest lambda_GC,
 for each column in the dataframe.
 }

man/get_colnames.Rd

@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_colnames}
+\alias{get_colnames}
+\title{Get column names from a mash object}
+\usage{
+get_colnames(m)
+}
+\arguments{
+\item{m}{An object of type mash}
+}
+\value{
+A vector of phenotype names
+}
+\description{
+This function extracts the column names from the local false
+sign rate table of a mash object's results. This can tell you the condition
+names or phenotype names used in the mash object. That can be useful for
+looking at a subset of these columns, say.
+}
+\examples{
+    \dontrun{get_colnames(m = mash_obj)}
+
+}

man/get_date_filename.Rd

@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_date_filename}
+\alias{get_date_filename}
+\title{Get current date-time in a filename-appropriate format.}
+\usage{
+get_date_filename()
+}
+\value{
+A string containing the current date-time with spaces and colons
+replaced with underscores and periods, respectively.
+}
+\description{
+Converts the current \code{Sys.time()} system time to a format
+that is acceptable to include in a filename. Changes punctuation that
+won't work in a filename.
+}

man/get_estimated_pi.Rd

@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_estimated_pi}
+\alias{get_estimated_pi}
+\title{Return the estimated mixture proportions. Use get_estimated_pi to
+extract the estimates of the mixture proportions for different types of
+covariance matrix. This tells you which covariance matrices have most of
+the mass.}
+\usage{
+get_estimated_pi(m, dimension = c("cov", "grid", "all"))
+}
+\arguments{
+\item{m}{the mash result}
+
+\item{dimension}{indicates whether you want the mixture proportions for the
+covariances, grid, or all}
+}
+\value{
+a named vector containing the estimated mixture proportions.
+}
+\description{
+Return the estimated mixture proportions. Use get_estimated_pi to
+extract the estimates of the mixture proportions for different types of
+covariance matrix. This tells you which covariance matrices have most of
+the mass.
+}
+\details{
+If the fit was done with \code{usepointmass=TRUE} then the first
+element of the returned vector will correspond to the null, and the
+remaining elements to the non-null covariance matrices. Suppose the fit
+was done with $K$ covariances and a grid of length $L$. If
+\code{dimension=cov} then the returned vector will be of length $K$
+(or $K+1$ if \code{usepointmass=TRUE}).  If \code{dimension=grid} then
+the returned vector will be of length $L$ (or $L+1$).  If
+\code{dimension=all} then the returned vector will be of length $LK$ (or
+$LK+1$). The names of the vector will be informative for which
+combination each element corresponds to.
+}

man/get_log10bf.Rd

@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_log10bf}
+\alias{get_log10bf}
+\title{Return the Bayes Factor for each effect}
+\usage{
+get_log10bf(m)
+}
+\arguments{
+\item{m}{the mash result (from joint or 1by1 analysis); must have been
+computed using usepointmass = TRUE}
+}
+\value{
+if m was fitted using usepointmass=TRUE then returns a vector of
+the log10(bf) values for each effect. That is, the jth element
+lbf_j is log10(Pr(Bj | g = ghat-nonnull)/Pr(Bj | g = 0)) where gha
+t-nonnull is the non-null part of ghat.  Otherwise returns NULL.
+}
+\description{
+Return the Bayes Factor for each effect
+}

man/get_marker_df.Rd

@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_marker_df}
+\alias{get_marker_df}
+\title{Get mash marker_df}
+\usage{
+get_marker_df(m, snp)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{snp}{A bigSNP object, produced by the bigsnpr package. Here, the WAMI
+SNP information.}
+}
+\description{
+Pulls SNP markers information in the mash object from a bigsnp
+object.
+}

man/get_n_significant_conditions.Rd

@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_n_significant_conditions}
+\alias{get_n_significant_conditions}
+\title{Count number of conditions each effect is significant in}
+\usage{
+get_n_significant_conditions(
+  m,
+  thresh = 0.05,
+  conditions = NULL,
+  sig_fn = get_lfsr
+)
+}
+\arguments{
+\item{m}{the mash result (from joint or 1by1 analysis)}
+
+\item{thresh}{indicates the threshold below which to call signals significant}
+
+\item{conditions}{which conditions to include in check (default to all)}
+
+\item{sig_fn}{the significance function used to extract significance from mash object; eg could be ashr::get_lfsr or ashr::get_lfdr}
+}
+\value{
+a vector containing the number of significant conditions
+}
+\description{
+Count number of conditions each effect is significant in
+}

man/get_ncond.Rd

@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_ncond}
+\alias{get_ncond}
+\title{Get number of conditions}
+\usage{
+get_ncond(m)
+}
+\arguments{
+\item{m}{The mash result}
+}
+\description{
+Get number of conditions
+}

man/get_pairwise_sharing.Rd

@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_pairwise_sharing}
+\alias{get_pairwise_sharing}
+\title{Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean}
+\usage{
+get_pairwise_sharing(m, factor = 0.5, lfsr_thresh = 0.05, FUN = identity)
+}
+\arguments{
+\item{m}{the mash fit}
+
+\item{factor}{a number between 0 and 1 - the factor within which effects are
+considered to be shared.}
+
+\item{lfsr_thresh}{the lfsr threshold for including an effect in the
+assessment}
+
+\item{FUN}{a function to be applied to the estimated effect sizes before
+assessing sharing. The most obvious choice beside the default
+'FUN=identity' would be 'FUN=abs' if you want to ignore the sign of the
+effects when assesing sharing.}
+}
+\description{
+Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
+}
+\details{
+For each pair of tissues, first identify the effects that are
+significant (by lfsr<lfsr_thresh) in at least one of the two tissues.
+Then compute what fraction of these have an estimated (posterior mean)
+effect size within a factor \code{factor} of one another. The results are
+returned as an R by R matrix.
+}
+\examples{
+\dontrun{
+get_pairwise_sharing(m) # sharing by magnitude (same sign)
+get_pairwise_sharing(m, factor=0) # sharing by sign
+get_pairwise_sharing(m, FUN=abs) # sharing by magnitude when sign is ignored
+}
+
+}

man/get_significant_results.Rd

@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{get_significant_results}
+\alias{get_significant_results}
+\title{From a mash result, get effects that are significant in at least
+one condition.}
+\usage{
+get_significant_results(
+  m,
+  thresh = 0.05,
+  conditions = NULL,
+  sig_fn = ashr::get_lfsr
+)
+
+get_significant_results(
+  m,
+  thresh = 0.05,
+  conditions = NULL,
+  sig_fn = ashr::get_lfsr
+)
+}
+\arguments{
+\item{m}{the mash result (from joint or 1by1 analysis)}
+
+\item{thresh}{indicates the threshold below which to call signals significant}
+
+\item{conditions}{which conditions to include in check (default to all)}
+
+\item{sig_fn}{the significance function used to extract significance from mash object; eg could be ashr::get_lfsr or ashr::get_lfdr. (Small values must indicate significant.)}
+}
+\value{
+a vector containing the indices of the significant effects, by
+order of most significant to least
+
+a vector containing the indices of the significant effects, by order of most significant to least
+}
+\description{
+From a mash result, get effects that are significant in at least
+one condition.
+
+From a mash result, get effects that are significant in at least one condition
+}

man/mash_plot_Ulist.Rd

@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_Ulist}
+\alias{mash_plot_Ulist}
+\title{ggplot of specific covariance matrix patterns}
+\usage{
+mash_plot_Ulist(
+  m,
+  range = NA,
+  saveoutput = FALSE,
+  suffix = "",
+  limits = TRUE,
+  labels = TRUE
+)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{range}{Numeric vector. Which covariance matrices should be plotted?}
+
+\item{saveoutput}{Logical. Should the output be saved to the path?}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+
+\item{limits}{should there be plot limits of -1 and 1? Default is true.}
+}
+\value{
+A list of dataframes used to make the tile plots and the plots
+themselves.
+}
+\description{
+Creates a tile plot using ggplot of the covariance matrices
+specified in the mash model.
+}

man/mash_plot_covar.Rd

@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_covar}
+\alias{mash_plot_covar}
+\title{ggplot of covariance matrix masses}
+\usage{
+mash_plot_covar(m, saveoutput = FALSE, suffix = "")
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{saveoutput}{Logical. Should the output be saved to the path?}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+}
+\description{
+Creates a bar plot using ggplot of the masses that are on each
+covariance matrix specified in the mash model.
+}
+\note{
+This plot can be useful for seeing the overall patterns of effects in
+the data used in mash. Non-significant effects will add mass to the
+"no_effects" covariance matrix, while significant effects will add mass
+to one of the other covariance matrices. You can use mash_plot_Ulist()
+to plot the covariance matrix patterns themselves.
+}

man/mash_plot_manhattan_by_condition.Rd

@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_manhattan_by_condition}
+\alias{mash_plot_manhattan_by_condition}
+\title{Manhattan plot in ggplot colored by significant conditions}
+\usage{
+mash_plot_manhattan_by_condition(
+  m,
+  snp,
+  cond = NA,
+  saveoutput = FALSE,
+  suffix = "",
+  thresh = 0.05
+)
+}
+\arguments{
+\item{m}{A mash object (outputted by mash).}
+
+\item{snp}{A bigSNP object, produced by the bigsnpr package. Here,
+the WAMI SNP information.}
+
+\item{cond}{A vector of phenotypes. Defaults to the names of each
+column in the mash object.}
+
+\item{saveoutput}{Logical. Should the output be saved to the path?}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+
+\item{thresh}{Numeric. The threshold used for the local false sign rate to
+call significance in a condition.}
+}
+\value{
+A \code{tbl_df()} of the data used to make the Manhattan plot, and a
+ggplot object containing the Manhattan.
+}
+\description{
+Takes a mash object and, for some vector of phenotypes, returns
+a Manhattan plot ggplot object (and its dataframe). Each SNP in the plot
+is colored by the number of phenotypes it is significant for. Even and
+odd chromosomes have different shapes for their SNPs, so that
+chromosome identity can be determined.
+}
+\examples{
+\dontrun{manhattan_out <- mash_ggman_by_condition(m = m, saveoutput = TRUE)}
+
+}

man/mash_plot_marker_effect.Rd

@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_marker_effect}
+\alias{mash_plot_marker_effect}
+\title{ggplot of single mash effect}
+\usage{
+mash_plot_marker_effect(
+  m,
+  snp = snp,
+  n = NA,
+  i = NA,
+  marker = TRUE,
+  saveoutput = FALSE,
+  suffix = ""
+)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{snp}{A bigSNP object, produced by the bigsnpr package. Here,
+the WAMI SNP information.}
+
+\item{n}{Optional. Integer or integer vector. The result number to plot, in
+order of significance. 1 would be the top result, for example. Find
+these with \code{\link{get_significant_results}}.}
+
+\item{i}{Optional. Integer or integer vector. The result number to plot, in
+the order of the mash object. 1 would be the first marker in the mash
+object, for example. Find these with \code{\link{get_marker_df}}.}
+
+\item{marker}{Optional. Print the marker name on the plot?}
+
+\item{saveoutput}{Logical. Should the output be saved to the path?}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+}
+\description{
+Creates a plot with point estimates and standard errors for
+effects of a single SNP in multiple conditions.
+}
+\note{
+Specify only one of n or i.
+}

man/mash_plot_pairwise_sharing.Rd

@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_pairwise_sharing}
+\alias{mash_plot_pairwise_sharing}
+\title{Create a ggplot of pairwise sharing of mash effects}
+\usage{
+mash_plot_pairwise_sharing(
+  m = NULL,
+  effectRDS = NULL,
+  corrmatrix = NULL,
+  reorder = TRUE,
+  saveoutput = FALSE,
+  filename = NA,
+  suffix = "",
+  ...
+)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{effectRDS}{An RDS containing a correlation matrix.}
+
+\item{corrmatrix}{A correlation matrix}
+
+\item{reorder}{Logical. Should the columns be reordered by similarity?}
+
+\item{saveoutput}{Logical. Should the output be saved to the path?}
+
+\item{filename}{Character string with an output filename. Optional.}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+
+\item{...}{Other arguments to \code{\link{get_pairwise_sharing}} or
+\code{\link{ggcorr}}.}
+}
+\value{
+A list containing a dataframe containing the correlations and a
+ggplot2 object containing the correlation plot.
+}
+\description{
+Given a correlation matrix, an RDS with a correlation matrix, or
+a mash object, create a ggplot of pairwise sharing of mash effects using
+\code{\link{get_pairwise_sharing}} and \code{\link{ggcorr}}.
+}

man/mash_plot_sig_by_condition.Rd

@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{mash_plot_sig_by_condition}
+\alias{mash_plot_sig_by_condition}
+\title{Significant SNPs per number of conditions}
+\usage{
+mash_plot_sig_by_condition(
+  m,
+  conditions = NA,
+  saveoutput = FALSE,
+  suffix = "",
+  thresh = 0.05
+)
+}
+\arguments{
+\item{m}{An object of type mash}
+
+\item{conditions}{A vector of conditions. Get these with get_colnames(m).}
+
+\item{saveoutput}{Logical. Save plot output to a file? Default is FALSE.}
+
+\item{suffix}{Character. Optional. A unique suffix used to save the files,
+instead of the current date & time.}
+
+\item{thresh}{What is the threshold to call an effect significant? Default
+is 0.05.}
+}
+\value{
+A list containing a dataframe of the number of SNPs significant per
+number of conditions, and a ggplot object using that dataframe.
+}
+\description{
+For some number of columns in a mash object that correspond to
+conditions, find the number of SNPs that are significant for that number
+of conditions.
+}
+\examples{
+  \dontrun{mash_plot_sig_by_condition(m = mash_obj, saveoutput = TRUE)}
+
+}

man/printf2.Rd

@@ -0,0 +1,11 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/wrapper.R
+\name{printf2}
+\alias{printf2}
+\title{Verbose?}
+\usage{
+printf2(verbose, ...)
+}
+\description{
+Verbose?
+}

man/reorder_cormat.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{reorder_cormat}
+\alias{reorder_cormat}
+\title{Reorder correlation matrix}
+\usage{
+reorder_cormat(cormat)
+}
+\arguments{
+\item{cormat}{A correlation matrix}
+}
+\description{
+Reorder correlation coefficients from a matrix of things
+(including NA's) and hierarchically cluster them
+}

man/scale_cov.Rd

@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mash-evaluation.R
+\name{scale_cov}
+\alias{scale_cov}
+\title{Scale each covariance matrix in list Ulist by a scalar in
+vector grid}
+\usage{
+scale_cov(Ulist, grid)
+}
+\arguments{
+\item{Ulist}{a list of matrices}
+
+\item{grid}{a vector of scaling factors (standard deviaions)}
+}
+\value{
+a list with length length(Ulist)*length(grid)
+}
+\description{
+This is an internal (non-exported) function. This help
+page provides additional documentation mainly intended for
+developers and expert users.
+}
+\keyword{internal}