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targets workflow for GWASpoly Vignette
Raw
.Rprofile
source("renv/activate.R")
Raw
README.md
Raw
_targets.R
## load packages
suppressMessages(source("./packages.R"))
## Load your R files
lapply(list.files("./R", full.names = TRUE), source)
##---configuration for running on slurm -----####
library(RLinuxModules)
module('load Slurm')
template <- file.path(here::here(), "slurm.tmpl")
future::plan(
strategy = batchtools_slurm,
template = template,
registry = list("cluster.functions$fs.latency" = 100)
)
##----global target options ----#
## specify default to running on head node and 4 cores for worker
#--can override individual targets by providing a full resources list---#
tar_option_set(
deployment = 'main',
error = "abridge",
storage = "main",
resources = tar_resources(future =
tar_resources_future(resources =
list(ncpus = 4,
walltime = 100,
memory=1000)))
)
##-----------------------------------------------####
list(
tar_target(
genofile,
system.file("extdata", "new_potato_geno.csv", package = "GWASpoly"),
format = 'file'
),
tar_target(
phenofile,
system.file("extdata", "new_potato_pheno.csv", package = "GWASpoly"),
format = 'file'
),
tar_target(
data,
read.GWASpoly(
ploidy = 4,
pheno.file = phenofile,
geno.file = genofile,
format = "numeric",
n.traits = 1,
delim = ","
)
),
tar_target(data.loco ,
set.K(data, LOCO = TRUE, n.core = 4),
deployment = 'worker'),
tar_target(data.original,
set.K(data, LOCO = FALSE, n.core = 4),
deployment = 'worker'),
tar_target(
params,
set.params(
geno.freq = 1 - 5 / nrow(data@geno),
fixed = "env",
fixed.type = "factor"
)
),
tar_target(
data.loco.scan,
GWASpoly(
data = data.loco,
models = c("additive", "1-dom"),
traits = c("vine.maturity"),
params = params,
n.core = 16
),
deployment = 'worker',
resources = tar_resources(# run this with 16 cores
future = tar_resources_future(
resources = list(ncpus = 16,
walltime = 100,
memory = 10000)))
),
tar_target(
data.original.scan,
GWASpoly(
data.original,
models = c("additive", "1-dom"),
traits = c("vine.maturity"),
params = params,
n.core = 16
),
deployment = 'worker',
resources = tar_resources(# run this with 16 cores
future = tar_resources_future(
resources = list(ncpus = 16,
walltime = 100,
memory = 10000)))
),
tar_render(
report,
'./GWAS_poly_with_Targets_Vignette.Rmd',
deployment = 'worker'
)
)
Raw
GWAS_poly_with_Targets_Vignette.Rmd
---
title: "Targets GWAS Poly Demo"
author: "John McCallum"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir =here::here())
```
## Load Up Objects
```{r}
tar_load(c(data,
data.original.scan,
data.loco.scan))
```
### Inspect the Phenotype
```{r plot.vine.maturity}
data@pheno %>%
ggplot(aes(x=vine.maturity)) + geom_histogram()
```
```{r qqplot.original}
qq.plot(data.original.scan,trait="vine.maturity") + ggtitle(label="Original")
```
```{r qqplot.loco}
qq.plot(data.loco.scan,trait="vine.maturity") + ggtitle(label="LOCO")
```
```{r}
data2 <- set.threshold(data.loco.scan,method="Bonferroni",level=0.05)
```
```{r manhattan.plot.all}
p <- manhattan.plot(data2,traits="vine.maturity")
p + theme(axis.text.x = element_text(angle=90,vjust=0.5))
```
```{r manhattan.plot.chr05}
manhattan.plot(data2,traits="vine.maturity",chrom="chr05")
```
```{r}
p <- LD.plot(data2)
p + xlim(0,30)
```
```{r}
qtl <- get.QTL(data=data2,traits="vine.maturity",models="additive",bp.window=5e6)
knitr::kable(qtl)
```
```{r}
fit.ans <- fit.QTL(data=data2,trait="vine.maturity",
qtl=qtl[,c("Marker","Model")],
fixed=data.frame(Effect="env",Type="factor"))
knitr::kable(fit.ans,digits=3)
```
Raw
packages.R
library(targets)
library(tarchetypes)
library(future)
library(future.batchtools)
library(here)
library(GWASpoly)
library(ggplot2)
library(dplyr)
Raw
renv.lock
{
"R": {
"Version": "4.0.0",
"Repositories": [
{
"Name": "CRAN",
"URL": "https://cran.stat.auckland.ac.nz"
},
{
"Name": "CRANextra",
"URL": "https://www.stats.ox.ac.uk/pub/RWin"
}
]
},
"Packages": {
"GWASpoly": {
"Package": "GWASpoly",
"Version": "2.10",
"Source": "GitHub",
"RemoteType": "github",
"RemoteHost": "api.github.com",
"RemoteUsername": "jendelman",
"RemoteRepo": "GWASpoly",
"RemoteRef": "master",
"RemoteSha": "0beaa165287278f866764d3278fec85c9bbc87d2",
"Hash": "17a1136a0fc26e50d13cfda4ad111c99"
},
"MASS": {
"Package": "MASS",
"Version": "7.3-54",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "0e59129db205112e3963904db67fd0dc"
},
"Matrix": {
"Package": "Matrix",
"Version": "1.3-4",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "4ed05e9c9726267e4a5872e09c04587c"
},
"R6": {
"Package": "R6",
"Version": "2.5.1",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "470851b6d5d0ac559e9d01bb352b4021"
},
"RColorBrewer": {
"Package": "RColorBrewer",
"Version": "1.1-2",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "e031418365a7f7a766181ab5a41a5716"
},
"RLinuxModules": {
"Package": "RLinuxModules",
"Version": "0.4",
"Source": "GitHub",
"RemoteType": "github",
"RemoteHost": "api.github.com",
"RemoteRepo": "RLinuxModules",
"RemoteUsername": "kiwiroy",
"RemoteRef": "main",
"RemoteSha": "d158522e4af50d171013430fbc892513ed6872fe",
"Hash": "3c1d6f5ea5cea2ea84ca75045c8fecf2"
},
"Rcpp": {
"Package": "Rcpp",
"Version": "1.0.7",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "dab19adae4440ae55aa8a9d238b246bb"
},
"backports": {
"Package": "backports",
"Version": "1.2.1",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "644043219fc24e190c2f620c1a380a69"
},
"base64url": {
"Package": "base64url",
"Version": "1.4",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "0c54cf3a08cc0e550fbd64ad33166143"
},
"batchtools": {
"Package": "batchtools",
"Version": "0.9.16",
"Source": "GitHub",
"RemoteType": "github",
"RemoteHost": "api.github.com",
"RemoteUsername": "mllg",
"RemoteRepo": "batchtools",
"RemoteRef": "master",
"RemoteSha": "63a6f812927f6dc78595abbffac0f6bc9651f67a",
"Hash": "e51eaa383f84199add5b827f1cc9be7f"
},
"brew": {
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"Hash": "92a5f887f9ae3035ac7afde22ba73ee9"
},
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},
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"Hash": "019388fc48e48b3da0d3a76ff94608a8"
},
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"Hash": "bb4341986bc8b914f0f0acf2e4a3f2f7"
},
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}
}
}
Raw
slurm.tmpl
#!/bin/bash
## Job Resource Interface Definition
##
## ntasks [integer(1)]: Number of required tasks,
## Set larger than 1 if you want to further parallelize
## with MPI within your job.
## ncpus [integer(1)]: Number of required cpus per task,
## Set larger than 1 if you want to further parallelize
## with multicore/parallel within each task.
## walltime [integer(1)]: Walltime for this job, in seconds.
## Must be at least 60 seconds for Slurm to work properly.
## memory [integer(1)]: Memory in megabytes for each cpu.
## Must be at least 100 (when I tried lower values my
## jobs did not start at all).
##
## Default resources can be set in your .batchtools.conf.R by defining the variable
## 'default.resources' as a named list.
<%
# relative paths are not handled well by Slurm
log.file = fs::path_expand(log.file)
-%>
#SBATCH --job-name=<%= job.name %>
#SBATCH --output=<%= log.file %>
#SBATCH --error=<%= log.file %>
#SBATCH --time=<%= as.integer(resources$walltime) %>
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=<%= resources$ncpus %>
#SBATCH --mem=<%= as.numeric(resources$memory) %>
<%= if (!is.null(resources$partition)) sprintf(paste0("#SBATCH --partition='", resources$partition, "'")) %>
<%= if (array.jobs) sprintf("#SBATCH --array=1-%i", nrow(jobs)) else "" %>
## Initialize work environment like
## source /etc/profile
## module add ...
## Export value of DEBUGME environemnt var to slave
export DEBUGME=<%= Sys.getenv("DEBUGME") %>
<%= sprintf("export OMP_NUM_THREADS=%i", resources$omp.threads) -%>
<%= sprintf("export OPENBLAS_NUM_THREADS=%i", resources$blas.threads) -%>
<%= sprintf("export MKL_NUM_THREADS=%i", resources$blas.threads) -%>
## Run R:
module load R/4.0.0
## we merge R output with stdout from SLURM, which gets then logged via --output option
Rscript -e 'batchtools::doJobCollection("<%= uri %>")'
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