Fixes (as saw build failing on r-universe)

.gitattributes

@@ -0,0 +1 @@
+* text=auto

.gitignore

@@ -6,3 +6,5 @@ mrbase.oauth
 *.rdx
 *.rdb
 
+*_cache/
+.DS_Store

DESCRIPTION

@@ -1,21 +1,44 @@
 Package: tryx
 Title: MR-TRYX (treasure your exceptions)
-Version: 0.2.0
-Authors@R: c(person("Gibran", "Hemani", email = "g.hemani@bristol.ac.uk", role = c("aut", "cre")), person("Yoonsu", "Cho", email = "yoonsu.cho@bristol.ac.uk", role = c("aut")))
-Description: Heterogeneity in MR analyses can arise due to horizontal pleiotropy. This package uses MR-Base to identify possible traits that can explain the heterogeneity, with a view to identifying novel putative associations, and adjusting for their influences to reduce heterogeneity and improve power.
-Depends: R (>= 3.6.0), 
-         TwoSampleMR, 
-         dplyr,
-         RadialMR,
-         magrittr,
-         tidyr,
-         ggplot2,
-         glmnet,
-         ggrepel
+Version: 0.2.1
+Authors@R: c(
+    person("Gibran", "Hemani", , "g.hemani@bristol.ac.uk", role = c("aut", "cre")),
+    person("Yoonsu", "Cho", , "yoonsu.cho@bristol.ac.uk", role = "aut")
+  )
+Description: Heterogeneity in MR analyses can arise due to horizontal
+    pleiotropy. This package uses MR-Base to identify possible traits that
+    can explain the heterogeneity, with a view to identifying novel
+    putative associations, and adjusting for their influences to reduce
+    heterogeneity and improve power.
+License: MIT + file LICENSE
+URL: https://mrcieu.r-universe.dev/tryx,
+    https://explodecomputer.github.io/tryx/,
+    https://github.com/explodecomputer/tryx
+BugReports: https://github.com/explodecomputer/tryx/issues
+Depends: 
+    R (>= 4.1.0)
+Imports:
+    dplyr,
+    ggplot2,
+    ggrepel,
+    glmnet,
+    magrittr,
+    R6,
+    RadialMR,
+    tibble,
+    tidyr,
+    TwoSampleMR
 Suggests: 
     igraph,
+    knitr,
+    rmarkdown,
+    simulateGP,
     testthat
-License: MIT + file LICENSE
+VignetteBuilder: 
+    knitr
+Remotes:
+    explodecomputer/simulateGP,
+    MRCIEU/TwoSampleMR,
+    WSpiller/RadialMR
+Config/roxygen2/version: 8.1.0
 Encoding: UTF-8
-LazyData: true
-RoxygenNote: 7.0.2

NAMESPACE

@@ -10,3 +10,63 @@ export(tryx.scan)
 export(tryx.sig)
 export(tryx.simulate)
 export(volcano_plot)
+importFrom(R6,R6Class)
+importFrom(TwoSampleMR,
+  available_outcomes,
+  extract_instruments,
+  extract_outcome_data,
+  harmonise_data,
+  mr,
+  mr_heterogeneity,
+  mr_method_list,
+  mv_extract_exposures,
+  mv_harmonise_data,
+  mv_multiple
+)
+importFrom(dplyr,
+  arrange,
+  bind_rows,
+  desc,
+  do,
+  filter,
+  group_by,
+  mutate,
+  n,
+  summarise
+)
+importFrom(ggplot2,
+  aes,
+  arrow,
+  element_blank,
+  element_text,
+  facet_grid,
+  geom_abline,
+  geom_errorbarh,
+  geom_point,
+  geom_segment,
+  geom_vline,
+  ggplot,
+  labs,
+  scale_colour_brewer,
+  theme,
+  theme_bw,
+  unit,
+  xlim,
+  ylim
+)
+importFrom(ggrepel,geom_label_repel)
+importFrom(magrittr,
+  "%$%",
+  "%>%"
+)
+importFrom(stats,
+  as.formula,
+  coef,
+  coefficients,
+  lm,
+  p.adjust,
+  rnorm,
+  sd
+)
+importFrom(tibble,tibble)
+importFrom(utils,combn)

R/adjustment.r

@@ -207,7 +207,7 @@ tryx.adjustment.mv <- function(tryxscan, lasso=TRUE, id_remove=NULL, proxies=FAL
 #' @param tryxscan Output from \code{tryx.scan}
 #' @param plot Whether to plot or not. Default is TRUE
 #' @param id_remove List of IDs to exclude from the adjustment analysis. It is possible that in the outlier search a candidate trait will come up which is essentially just a surrogate for the outcome trait (e.g. if you are analysing coronary heart disease as the outcome then a variable related to heart disease medication might come up as a candidate trait). Adjusting for a trait which is essentially the same as the outcome will erroneously nullify the result, so visually inspect the candidate trait list and remove those that are inappropriate.
-#' @param duplicate_outliers_method Sometimes more than one trait will associate with a particular outlier. TRUE = only keep the trait that has the biggest influence on heterogeneity
+#' @param filter_duplicate_outliers Sometimes more than one trait will associate with a particular outlier. TRUE = only keep the trait that has the biggest influence on heterogeneity
 #' 
 #' @export
 #' @return List of 
@@ -246,11 +246,6 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 	# 	analysis$detection <- detection
 	# }
 
-	cpg <- require(ggrepel)
-	if(!cpg)
-	{
-		stop("Please install the ggrepel package\ninstall.packages('ggrepel')")
-	}
 
 	dat <- subset(tryxscan$dat, mr_keep, select=c(SNP, beta.exposure, beta.outcome, se.exposure, se.outcome))
 	dat$ratio <- dat$beta.outcome / dat$beta.exposure
@@ -310,7 +305,7 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 	# Outliers removed (all)
 	tt <- subset(dat, !SNP %in% tryxscan$outliers)
 	mod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))
-	if(class(mod) != "try-error")
+	if(!inherits(mod, "try-error"))
 	{
 		estimates <- bind_rows(estimates, 
 			tibble(
@@ -328,7 +323,7 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 	# Outliers removed (candidates)
 	tt <- subset(dat, !SNP %in% temp$SNP)
 	mod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))
-	if(class(mod) != "try-error")
+	if(!inherits(mod, "try-error"))
 	{
 		estimates <- bind_rows(estimates, 
 			tibble(
@@ -348,7 +343,7 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 	tt$qi <- cochrans_q(tt$beta.outcome / tt$beta.exposure, tt$se.outcome / abs(tt$beta.exposure))
 	analysis$Q$adj_Q <- sum(tt$qi)
 	mod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))
-	if(class(mod) != "try-error")
+	if(!inherits(mod, "try-error"))
 	{
 		estimates <- bind_rows(estimates, 
 			tibble(
@@ -382,7 +377,7 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 	{
 		tt <- subset(dat, !SNP %in% tryxscan$true_outliers)
 		mod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))
-		if(class(mod) != "try-error")
+		if(!inherits(mod, "try-error"))
 		{
 			estimates <- bind_rows(estimates,
 				tibble(
@@ -451,26 +446,6 @@ tryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_o
 
 
 
-#' Analyse tryx results
-#' 
-#' This returns various heterogeneity statistics, IVW estimates for raw, 
-#' adjusted and outlier removed datasets, and summary of peripheral 
-#' traits detected etc.
-#' 
-#' @param tryxscan Output from \code{tryx.scan}
-#' @param plot Whether to plot or not. Default is TRUE
-#' @param filter_duplicate_outliers Whether to only allow each putative outlier to be adjusted by a single trait (in order of largest divergence). Default is TRUE.
-#' 
-#' @export
-#' @return List of 
-#' - adj_full: data frame of SNP adjustments for all candidate traits
-#' - adj: The results from adj_full selected to adjust the exposure-outcome model
-#' - Q: Heterogeneity stats
-#' - estimates: Adjusted and unadjested exposure-outcome effects
-#' - plot: Radial plot showing the comparison of different methods and the changes in SNP effects ater adjustment
-
-
-
 #' Adjust and analyse the tryx results
 #'
 #' Similar to tryx.analyse, but when there are multiple traits associated with a single variant then we use a LASSO-based multivariable approach 
@@ -597,7 +572,6 @@ bootstrap_path <- function(gx, gx.se, gp, gp.se, px, px.se, nboot=1000)
 
 radialmr <- function(dat, outlier=NULL)
 {
-	library(ggplot2)
 	beta.exposure <- dat$beta.exposure
 	beta.outcome <- dat$beta.outcome
 	se.outcome <- dat$se.outcome

R/plots.r

@@ -6,17 +6,6 @@
 #' @return ggplot of volcano plots
 volcano_plot <- function(res, what="exposure")
 {
-	cpg <- require(ggplot2)
-	if(!cpg)
-	{
-		stop("Please install the ggplot2 package")
-	}
-	cpg <- require(ggrepel)
-	if(!cpg)
-	{
-		stop("Please install the ggrepel package")
-	}
-
 	stopifnot(all(c("outcome", "exposure", "b", "se", "pval") %in% names(res)))
 	if(!"sig" %in% names(res))
 	{
@@ -44,7 +33,7 @@ volcano_plot <- function(res, what="exposure")
 	geom_vline(xintercept=0, linetype="dotted") +
 	geom_errorbarh(aes(xmin=b-1.96*se, xmax=b+1.96*se)) +
 	geom_point(aes(colour=sig)) +
-	facet_grid(form, scale="free") +
+	facet_grid(form, scales="free") +
 	geom_label_repel(data=subset(res, sig), aes(label=exposure), colour="black", segment.colour="black", point.padding = unit(0.7, "lines"), box.padding = unit(0.7, "lines"), segment.size=0.5, force=2, max.iter=3e3) +
 	# geom_text_repel(data=subset(res, sig), aes(label=outcome, colour=category)) +
 	geom_point(aes(colour=sig)) +
@@ -69,16 +58,10 @@ volcano_plot <- function(res, what="exposure")
 tryx.network <- function(tryxscan)
 {
 
-	a <- require(igraph)
-	if(!a)
+	if(!requireNamespace("igraph", quietly=TRUE))
 	{
 		stop("Please install the igraph R package")
 	}
-	a <- require(dplyr)
-	if(!a)
-	{
-		stop("Please install the dplyr R package")
-	}
 
 	stopifnot("candidate_outcome_mr" %in% names(tryxscan))
 	stopifnot("candidate_exposure_mr" %in% names(tryxscan))
@@ -174,14 +157,14 @@ tryx.network <- function(tryxscan)
 
 
 	nodes <- rbind(
-		data_frame(
+		tibble(
 			name=c(
 				ao$trait[ao$id %in% tryxscan$dat$id.exposure[1]],
 				ao$trait[ao$id %in% tryxscan$dat$id.outcome[1]]),
 			id=c(tryxscan$dat$id.exposure[1], tryxscan$dat$id.outcome[1]),
 			what=c("original")
 		),
-		data_frame(
+		tibble(
 			name=unique(tryxscan$outliers),
 			id=NA,
 			what="Outlier instruments"
@@ -230,20 +213,20 @@ tryx.network <- function(tryxscan)
 	grp$size <- 0.1
 	grp$size[grp$what == "Main hypothesis"] <- 0.5
 
-	layoutg <- graph_from_data_frame(layoutd2, vertices=nodes)
-	l <- layout_with_fr(layoutg)
-	grl <- graph_from_data_frame(grp, directed=TRUE, vertices=nodes)
+	layoutg <- igraph::graph_from_data_frame(layoutd2, vertices=nodes)
+	l <- igraph::layout_with_fr(layoutg)
+	grl <- igraph::graph_from_data_frame(grp, directed=TRUE, vertices=nodes)
 	plot(grl, 
 		layout=l, 
-		vertex.size=V(grl)$size, 
-		vertex.label=V(grl)$label,
-		# edge.arrow.size=E(grl)$size, 
+		vertex.size=igraph::V(grl)$size, 
+		vertex.label=igraph::V(grl)$label,
+		# edge.arrow.size=igraph::E(grl)$size, 
 		edge.arrow.size=0.3,
-		edge.color=E(grl)$colour,
+		edge.color=igraph::E(grl)$colour,
 		vertex.label.cex=0.5, 
 		vertex.label.family="sans", 
 		vertex.label.color="black", 
-		vertex.color=V(grl)$colour,
+		vertex.color=igraph::V(grl)$colour,
 		edge.color="red"
 	)
 }

R/scan.r

@@ -56,16 +56,6 @@ tryx.scan <- function(dat, outliers="RadialMR", outlier_correction="none", outli
 	if(outliers[1] == "RadialMR")
 	{
 		message("Using RadialMR package to detect outliers")
-		cpg <- require(RadialMR)
-		if(!cpg)
-		{
-			stop("Please install the RadialMR package\ndevtools::install_github('WSpiller/RadialMR')")
-		}
-		cpg <- require(dplyr)
-		if(!cpg)
-		{
-			stop("Please install the RadialMR package\ndevtools::install_github('WSpiller/RadialMR')")
-		}
 
 
 		# radial <- RadialMR::ivw_radial(RadialMR::format_radial(dat$beta.exposure, dat$beta.outcome, dat$se.exposure, dat$se.outcome, dat$SNP), alpha=0.05/nrow(dat), weights=3)
@@ -82,7 +72,7 @@ tryx.scan <- function(dat, outliers="RadialMR", outlier_correction="none", outli
 		# apply outlier_correction method with outlier_threshold to radial SNP-Q statistics
 
 
-		if(radial$outliers[1] == "No significant outliers")
+		if(is.character(radial$outliers) && radial$outliers[1] == "No significant outliers")
 		{
 			message("No outliers found")
 			message("Try changing the outlier_threshold parameter")
@@ -318,6 +308,7 @@ strategy1 <- function(dat, het_threshold=0.05, ivw_max_snp=1)
 
 #' Identify putatively significant associations in the outlier scan
 #' 
+#' @param tryxscan Output from \code{tryx.scan}
 #' @param mr_threshold_method This is the argument to be passed to \code{p.adjust}. Default is "fdr". If no p-value adjustment is to be applied then specify "unadjusted"
 #' @param mr_threshold Threshold to declare significance
 #' @export