Reproducible

Common Workflow Language (CWL)

• https://www.commonwl.org/
• Workflow systems turn raw data into scientific knowledge. Pipeline, Snakemake, Docker, Galaxy, Python, Conda, Workflow Definition Language (WDL), Nextflow. The best is to embed the workflow in a container; see Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics by Baichoo 2018.

R

• Rcwl package
  • Connecting Bioconductor to other bioinformatics tools using Rcwl from Bioc2020
• Reproducible Research: What to do When Your Results Can’t Be Reproduced. 3 danger zones.
  • R session context
    • R version
    • Packages versions
    • Using set.seed() for a reproducible randomization
    • Floating point accuracy
  • Operating System (OS) context
    • System packages versions
    • System locale
    • Environment variables
  • Data versioning

Rmarkdown

Rmarkdown package

packrat and renv

R packages → packrat/renv

checkpoint

R → Reproducible Research

dockr package

'dockr': easy containerization for R

Docker & Singularity

Docker

targets package

targets: Democratizing Reproducible Analysis Pipelines Will Landau

Snakemake

• Hypercluster: a flexible tool for parallelized unsupervised clustering optimization
• https://snakemake.readthedocs.io/en/stable/tutorial/setup.html#run-tutorial-for-free-in-the-cloud-via-gitpod
• https://hpc.nih.gov/apps/snakemake.html

Papers

High-throughput analysis suggests differences in journal false discovery rate by subject area and impact factor but not open access status

Misc

• 4 great free tools that can make your R work more efficient, reproducible and robust
• digest: Create Compact Hash Digests of R Objects
• memoise: Memoisation of Functions. Great for shiny applications. Need to understand how it works in order to take advantage. I modify the example from Efficient R by moving the data out of the function. The cache works in the 2nd call. I don't use benchmark() function since it performs the same operation each time (so favor memoise and mask some detail).

  library(ggplot2) # mpg 
  library(memoise) 
  plot_mpg2 <- function(mpgdf, row_to_remove) {
    mpgdf = mpgdf[-row_to_remove,]
    plot(mpgdf$cty, mpgdf$hwy)
    lines(lowess(mpgdf$cty, mpgdf$hwy), col=2)
  }
  m_plot_mpg2 = memoise(plot_mpg2)
  system.time(m_plot_mpg2(mpg, 12))
  #   user  system elapsed
  #  0.019   0.003   0.025
  system.time(plot_mpg2(mpg, 12))
  #   user  system elapsed
  #  0.018   0.003   0.024
  system.time(m_plot_mpg2(mpg, 12))
  #   user  system elapsed
  #  0.000   0.000   0.001
  system.time(plot_mpg2(mpg, 12))
  #   user  system elapsed
  #  0.032   0.008   0.047

And be careful when it is used in simulation.

f <- function(n=1e5) { 
  a <- rnorm(n)
  a
} 
system.time(f1 <- f())
mf <- memoise::memoise(f)
system.time(f2 <- mf())
system.time(f3 <- mf())
all.equal(f2, f3) # TRUE

• reproducible: A Set of Tools that Enhance Reproducibility Beyond Package Management
• Improving reproducibility in computational biology research 2020