Merge pull request #524 from remlapmot/tsmr-0-6-4

TwoSampleMR 0.6.4

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MR Base Database
-Version: 0.6.3
+Version: 0.6.4
 Authors@R: c(
     person("Gibran", "Hemani", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),

NEWS.md

@@ -1,3 +1,11 @@
+# TwoSampleMR v0.6.4
+
+(Release date: 2024-06-05)
+
+* Update installation instructions in README.md
+
+* Fixed a bug in which the wrong indels recoding function was called (thanks @ruochiz)
+
 # TwoSampleMR v0.6.3
 
 (Release date: 2024-05-23)

R/harmonise.R

@@ -457,7 +457,7 @@ harmonise_12 <- function(SNP, A1, B1, B2, betaA, betaB, fA, fB, tolerance, actio
 	remove <- palindromic
 
 	indel_index <- nchar(B1) > 1 | nchar(B2) > 1 | B1 == "D" | B1 == "I"
-	temp <- recode_indels_21(A1[indel_index], B1[indel_index], B2[indel_index])
+	temp <- recode_indels_12(A1[indel_index], B1[indel_index], B2[indel_index])
 
 	A1[indel_index] <- temp$A1
 	A2[indel_index] <- temp$A2
@@ -660,4 +660,3 @@ check_required_columns <- function(dat, type="exposure")
 	}
 	return(NULL)
 }
-

README.md

@@ -11,6 +11,8 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 [![DOI](https://zenodo.org/badge/49515156.svg)](https://zenodo.org/badge/latestdoi/49515156)
 [![Codecov test
 coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
+[![TwoSampleMR status
+badge](https://mrcieu.r-universe.dev/badges/TwoSampleMR)](https://mrcieu.r-universe.dev/TwoSampleMR)
 <!-- badges: end -->
 
 A package for performing Mendelian randomization using GWAS summary
@@ -28,26 +30,28 @@ reference panels.** For full details of the changes, please visit
 
 ## Installation
 
-To install the latest version of TwoSampleMR, perform as normal:
+Users running Windows and macOS, to install the latest version of
+TwoSampleMR please install from our MRC IEU r-universe
 
 ``` r
-install.packages("remotes")
-remotes::install_github("MRCIEU/TwoSampleMR")
+install.packages("TwoSampleMR", repos = c("https://mrcieu.r-universe.dev", "https://cloud.r-project.org"))
 ```
 
-To update the package just run the
-`remotes::install_github("MRCIEU/TwoSampleMR")` command again.
+Users running Linux or WebR please see the [following
+instructions](https://github.com/MRCIEU/mrcieu.r-universe.dev#readme).
+
+To update the package run the same command again.
 
-We recommend using this new version going forwards but for a limited
-time we are enabling backwards compatibility, in case you are in the
-middle of analysis or need to reproduce old analysis. In order to use
-the legacy version of the package and the database, install using:
+### Installing from source
 
 ``` r
 install.packages("remotes")
-remotes::install_github("MRCIEU/TwoSampleMR@0.4.26")
+remotes::install_github("MRCIEU/TwoSampleMR")
 ```
 
+To update the package just run the
+`remotes::install_github("MRCIEU/TwoSampleMR")` command again.
+
 ## Docker
 
 A docker image containing R with the TwoSampleMR package pre-installed

index.md

@@ -4,6 +4,7 @@
 [![R-CMD-check](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml/badge.svg)](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml)
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![DOI](https://zenodo.org/badge/49515156.svg)](https://zenodo.org/badge/latestdoi/49515156)
 [![Codecov test coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
+[![TwoSampleMR status badge](https://mrcieu.r-universe.dev/badges/TwoSampleMR)](https://mrcieu.r-universe.dev/TwoSampleMR)
 <!-- badges: end -->
 
 A package for performing Mendelian randomization using GWAS summary data. It uses the [IEU GWAS database](https://gwas.mrcieu.ac.uk/) to obtain data automatically, and a wide range of methods to run the analysis. You can use the [MR-Base web app](https://www.mrbase.org/) to try out a limited range of the functionality in this package, but for any serious work we strongly recommend using this R package.
@@ -14,22 +15,25 @@ A package for performing Mendelian randomization using GWAS summary data. It use
 
 ## Installation
 
-To install the latest version of TwoSampleMR, perform as normal:
+Users running Windows and macOS, to install the latest version of TwoSampleMR please install from our MRC IEU r-universe 
 
-``` r
-install.packages("remotes")
-remotes::install_github("MRCIEU/TwoSampleMR")
+```r
+install.packages("TwoSampleMR", repos = c("https://mrcieu.r-universe.dev", "https://cloud.r-project.org"))
 ```
 
-To update the package just run the `remotes::install_github("MRCIEU/TwoSampleMR")` command again.
+Users running Linux or WebR please see the [following instructions](https://github.com/MRCIEU/mrcieu.r-universe.dev#readme).
 
-We recommend using this new version going forwards but for a limited time we are enabling backwards compatibility, in case you are in the middle of analysis or need to reproduce old analysis. In order to use the legacy version of the package and the database, install using:
+To update the package run the same command again.
+
+### Installing from source
 
 ``` r
 install.packages("remotes")
-remotes::install_github("MRCIEU/TwoSampleMR@0.4.26")
+remotes::install_github("MRCIEU/TwoSampleMR")
 ```
 
+To update the package just run the `remotes::install_github("MRCIEU/TwoSampleMR")` command again.
+
 ## Docker
 
 A docker image containing R with the TwoSampleMR package pre-installed is available here: https://hub.docker.com/r/mrcieu/twosamplemr

inst/sandpit/api_alive.r

@@ -115,4 +115,3 @@ toggle_api("release")
 pout2 <- extract_outcome_data(snplist[1:5000], 1001, proxies=TRUE)
 Sys.time()-t1
 # 40sec
-

inst/sandpit/api_comparisons.R

@@ -41,4 +41,4 @@ table(out1 == out2)
 
 
 dat1 <- harmonise_data(exposure_dat, out2)
-dat2 <- harmonise_data(inst2, out2)
+dat2 <- harmonise_data(inst2, out2)

inst/sandpit/forest_plots.R

@@ -17,4 +17,3 @@ forest_plot(mr_result, in_columns=TRUE, by_category=FALSE, exponentiate=TRUE, tr
 
 forest_plot(mr_result, in_columns=FALSE, by_category=TRUE)
 forest_plot(mr_result, in_columns=FALSE, by_category=FALSE)
-

inst/sandpit/harmonise.R

@@ -1,5 +1,3 @@
-
-
 # Assumptions - effect allele is present in exposure and outcome
 # Possibilities
 
@@ -29,7 +27,7 @@
 
 
 
-a <- read.table("inst/data/alleles.txt", he=T, stringsAsFactors=FALSE)
+a <- read.table("inst/extdata/alleles.txt", he=T, stringsAsFactors=FALSE)
 SNP <- a$SNP
 A1 <- a$A1
 A2 <- a$A2
@@ -62,9 +60,9 @@ outcome_dat <- data.frame(
 	stringsAsFactors=FALSE
 )
 
-dat <- harmonise_exposure_outcome(exposure_dat, outcome_dat)
+dat <- TwoSampleMR::harmonise_data(exposure_dat, outcome_dat)
 
-a <- read.table("inst/data/alleles.txt", he=T, stringsAsFactors=FALSE)
+a <- read.table("inst/extdata/alleles.txt", he=T, stringsAsFactors=FALSE)
 SNP <- a$SNP
 A1 <- a$A1
 A2 <- a$A2
@@ -76,5 +74,5 @@ fA <- a$fA
 fB <- a$fB
 
 fA[4] <- NA
-harmonise_21(SNP, A1, A2, B1, betaA, betaB, fA, rep(NA, length(SNP)), 0.08, 2)
-harmonise_22(SNP, A1, A2, B1, B2, betaA, betaB, fA, fB, 0.08, 3)
+TwoSampleMR:::harmonise_21(SNP, A1, A2, B1, betaA, betaB, fA, rep(NA, length(SNP)), 0.08, 2)
+TwoSampleMR:::harmonise_22(SNP, A1, A2, B1, B2, betaA, betaB, fA, fB, 0.08, 3)

inst/sandpit/multivariable_mr.R

@@ -89,5 +89,3 @@ kable(mv_ivw(dat)$result)
 ```{r}
 kable(mv_basic(dat)$result)
 ```
-
-

inst/sandpit/multivariable_mr.rmd

@@ -89,5 +89,3 @@ kable(mv_ivw(dat)$result)
 ```{r}
 kable(mv_basic(dat)$result)
 ```
-
-

inst/sandpit/mysql.R

@@ -67,7 +67,3 @@ WHERE a.snp=b.id AND a.study=c.id
 AND b.name IN ('rs10900000', 'rs10000010', 'rs10000092')
 AND c.filename IN ('cardiogramplusc4d_180814_update_data.txt.uniform.af.txt', 'All_ancestries_SNP_gwas_mc_merge_nogc.tbl.uniq.gz.uniform.af.txt', 'MAGIC_INSULIN_SECRETION_DI_for_release_HMrel27.txt.uniform.af.txt')
 ORDER BY filename;
-
-
-
-

inst/sandpit/plot_tests.R

@@ -51,4 +51,3 @@ mr_leaveoneout_plot(mrl)
 mr_leaveoneout_plot(mrl1)
 mr_leaveoneout_plot(mrl2)
 mr_leaveoneout_plot(mrl3)
-

inst/sandpit/test_indels.r

@@ -15,9 +15,9 @@ al <- matrix(c(
 "I", "D", "D", "I"
 ), nrow=4) %>% t
 
-recode_indels_22(al[,1], al[,2], al[,3], al[,4]) %>% cbind(al, .)
-recode_indels_21(al[,1], al[,2], al[,3]) %>% cbind(al, .)
-recode_indels_12(al[,1], al[,3], al[,4]) %>% cbind(al, .)
+TwoSampleMR:::recode_indels_22(al[,1], al[,2], al[,3], al[,4]) %>% cbind(al, .)
+TwoSampleMR:::recode_indels_21(al[,1], al[,2], al[,3]) %>% cbind(al, .)
+TwoSampleMR:::recode_indels_12(al[,1], al[,3], al[,4]) %>% cbind(al, .)
 
 
 
@@ -97,5 +97,3 @@ x <- data.frame(
 )
 
 format_data(x)
-
-

inst/sandpit/test_mr_sign.R

@@ -29,4 +29,3 @@ mr(dat, method_list=c("mr_ivw", "mr_sign"))
 mr_scatter_plot(mr(dat, method_list=c("mr_ivw", "mr_sign")), dat)
 
 # Could consider doing a parametric bootstrap type analysis
-

inst/sandpit/test_mrinput.r

@@ -10,6 +10,3 @@ o2 <- dat_to_MRInput(dat, get_correlations=TRUE)
 
 MendelianRandomization::mr_ivw(o1[[1]])
 MendelianRandomization::mr_ivw(o2[[1]])
-
-
-

inst/sandpit/test_selection.R

@@ -181,4 +181,3 @@ mu_g2x <- min(rbeta(1, 1, 300), 0.05) * sample(c(1,-1), 1)
 
 
 a <- run_sim(nid1 = nid1, nid2 = nid2, nidu = nidu, ninst1 = ninst1, ninst2 = ninst2, ninstu = ninstu, var_xy = var_xy, var_ux = var_ux, var_uy = var_uy, var_g1x = var_g1x, var_g2y = var_g2y, var_guu = var_guu, var_g1y = var_g1y, var_g2x = var_g2x, mu_g1y = mu_g1y, mu_g2x = mu_g2x)
-

inst/sandpit/vignette.rmd

@@ -188,4 +188,3 @@ mr_leaveoneout_plot(l)[[2]]
 ### Enrichment
 
 You can also perform a Fisher's combined test to see if there is an enrichment of p-values in the outcome data (e.g. are the SNPs that influence the exposure also likely to influence the outcome, this is not an MR analysis).
-