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

• CRAN Task View: Reproducible Research
• 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

• A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker, Slides, Talks & Video. The whole idea is written in an R package repro package. The package create an R project Template where we can use it by RStudio -> New Project -> Create Example Repro Template. Note that the Makefile and Dockerfile can be inferred from the markdown.Rmd file. Four elements
  • Git folder of source code for version control (R project)
  • Makefile. Make is a “recipe” language that describes how files depend on each other and how to resolve these dependencies.
  • Docker software environment (Containerization)
  • RMarkdown (dynamic document generation)

  automake() # Create and open <Makefile>
  # Modify <Makefile> by following the console output
  rerun() # and follow the console output
  # by opening a terminal
  make docker && make -B DOCKER=TRUE
  # The above will generate the output
  

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

Share your code and data

• zenodo.org which has been used by Demystifying "drop-outs" in single-cell UMI data
• OSF which has been used by Methods for correcting inference based on outcomes predicted by machine learning
• codeocean.
  • A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. The R code can be downloaded by git (Capsule -> Export -> Clone via Git). The data (3.4G zip file) has to be downloaded manually. The environment panel shows what packages have to be installed (apt-get, Bioconductor, R-CRAN, R-Github). It seems "Export" is more complete than "Clone via Git". It even include a Dockerfile.
  • Consensus Non-negative Matrix factorization (cNMF) v1.2

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