Generate workflow environment

Load workflow environment with sample data into your current working directory. The sample data are described here.

library(systemPipeRdata)
genWorkenvir(workflow = "varseq")
setwd("varseq")

Alternatively, this can be done from the command-line as follows:

Rscript -e "systemPipeRdata::genWorkenvir(workflow='varseq')"

In the workflow environments generated by genWorkenvir all data inputs are stored in a data/ directory and all analysis results will be written to a separate results/ directory, while the systemPipeVARseq.Rmd script and the targets file are expected to be located in the parent directory. The R session is expected to run from this parent directory. Additional parameter files are stored under param/.

To work with real data, users want to organize their own data similarly and substitute all test data for their own data. To rerun an established workflow on new data, the initial targets file along with the corresponding FASTQ files are usually the only inputs the user needs to provide.

Run workflow

Now open the R markdown script systemPipeVARseq.Rmdin your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.

Here pair-end workflow example is provided. Please refer to the main vignette systemPipeR.Rmd for running the workflow with single-end data.

Run R session on computer node

In a computer cluster enviornment. Typically, after opening the Rmd file of this workflow in Vim and attaching a connected R session via the F2 ( vim-r plugin installed) key, following command sequence can be used to run your R session on a computer node.

q("no")  # closes R session on head node
srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l
module load R/3.6.0
R

Now check your R session running environment.

system("hostname")  # should return the computer name or cluster name
getwd()  # checks current working directory of R session
dir()  # returns content of current working directory

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman et al., 2016).

library(systemPipeR)
## Loading required package: Rsamtools
## Loading required package: GenomeInfoDb
## Loading required package: BiocGenerics
## Loading required package: parallel
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall,
##     clusterEvalQ, clusterExport, clusterMap,
##     parApply, parCapply, parLapply, parLapplyLB,
##     parRapply, parSapply, parSapplyLB
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, basename,
##     cbind, colnames, dirname, do.call, duplicated,
##     eval, evalq, Filter, Find, get, grep, grepl,
##     intersect, is.unsorted, lapply, Map, mapply,
##     match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, Position, rank, rbind, Reduce,
##     rownames, sapply, setdiff, sort, table, tapply,
##     union, unique, unsplit, which, which.max,
##     which.min
## Loading required package: S4Vectors
## Loading required package: stats4
## 
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:base':
## 
##     expand.grid
## Loading required package: IRanges
## Loading required package: GenomicRanges
## Loading required package: Biostrings
## Loading required package: XVector
## 
## Attaching package: 'Biostrings'
## The following object is masked from 'package:base':
## 
##     strsplit
## Loading required package: ShortRead
## Loading required package: BiocParallel
## Loading required package: GenomicAlignments
## Loading required package: SummarizedExperiment
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view
##     with 'browseVignettes()'. To cite Bioconductor,
##     see 'citation("Biobase")', and for packages
##     'citation("pkgname")'.
## Loading required package: DelayedArray
## Loading required package: matrixStats
## 
## Attaching package: 'matrixStats'
## The following objects are masked from 'package:Biobase':
## 
##     anyMissing, rowMedians
## 
## Attaching package: 'DelayedArray'
## The following objects are masked from 'package:matrixStats':
## 
##     colMaxs, colMins, colRanges, rowMaxs, rowMins,
##     rowRanges
## The following objects are masked from 'package:base':
## 
##     aperm, apply, rowsum
## 
## 
## Attaching package: 'systemPipeR'
## The following object is masked from 'package:BiocStyle':
## 
##     output

If applicable users can load custom functions not provided by systemPipeR. Skip this step if this is not the case.

source("systemPipeVARseq_Fct.R")

If you are running on a single machine, use following code as an exmaple to check if some tools used in this workflow are in your environment PATH. No warning message should be shown if all tools are installed.



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