Gates Open Res Gates Open Research 2572-4754 F1000 Research Limited London, UK 10.12688/gatesopenres.12832.1 Software Tool Article Articles DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis

[version 1; peer review: 1 approved with reservations]

 Finak Greg Conceptualization Funding Acquisition Investigation Methodology Software Supervision Writing – Original Draft Preparation Writing – Review & Editing a 1 2 3 Mayer Bryan Data Curation Methodology Supervision Validation Writing – Review & Editing 1 2 3 Fulp William Data Curation Formal Analysis Writing – Review & Editing 1 2 3 Obrecht Paul Data Curation Software Validation 1 2 3 Sato Alicia Project Administration Resources Supervision 1 2 3 Chung Eva Project Administration 1 2 3 Holman Drienna Data Curation Supervision Validation 1 2 3 Gottardo Raphael Conceptualization Funding Acquisition Investigation Project Administration Supervision Writing – Review & Editing b 1 2 3 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA Vaccine Immunology Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA Statistical Center For HIV AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA gfinak@fredhutch.org rgottard@fredhutch.org

No competing interests were disclosed.

 22 6 2018 2018 2 31 19 6 2018 Copyright: © 2018 Finak G et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large ’omics’ or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high "startup" costs and poor adherence to procedures when they deviate substantially from an analyst’s usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individual’s existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically R’s package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

 Assay data Bioconductor Collaboration Data science Package Reproducibility Rmarkdown Rstats Version control Gates Foundation OPP1151646 National Institute of General Medical Sciences R01GM118417-01A1 This work was supported by the Gates Foundation [OPP1032317; to RG]; and the National Institute of General Medical Sciences [R01 GM118417-01A1; to GF]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

A central idea of reproducible research is that results are published along with underlying data and software code necessary to reproduce and verify the findings. Termed a research compendium, this idea has received significant attention in the literature 1– 5 .

Many software tools have since been developed to facilitate reproducible data analytics, and scientific journals have increasingly demanded that code and primary data be made publicly available with scientific publications 2, 6– 27 . Tools like git and Github, figshare, and Rmarkdown are increasingly used by researchers to make code, figures, and data open, accessible and reproducible. Nonetheless, in the life sciences, practicing reproducible research with large data sets and complex processing pipelines continues to be challenging.

Data preprocessing, quality control (QC), data standardization, analysis, and reporting are tightly coupled in most discussions of reproducible research, and indeed, literate programming frameworks such as Sweave and Rmarkdown are designed around the idea that code, data, and research results are tightly integrated 2, 25 . Tools like Docker, a software container that virtualizes an operating system environment for distribution, have been used to ensure consistent versions of software and other dependencies are used for reproducible data analysis 16 . The use of R in combination with other publicly available tools has been proposed in the past to build reproducible research compendia 3, 28, 29 . Many existing tools already implement such ideas. The workflowr package provides mechanisms to turn a data analysis project into a version-controlled, documented, website presenting the results. The drake package 30 is a general purpose work-flow manager that implements analytic "plans", caching of intermediate data objects, and provides scalability, and provides tangible evidence of reproducibility by detecting when code, data and results are in sync.

However, tight coupling of preprocessing and analysis can be challenging for teams analyzing and integrating large volumes of diverse data, where different individuals in the team have different areas of expertise and may be responsible for processing different data sets from a larger study. These challenges are compounded when a processing pipeline is split across multiple teams. A primary problem in data science is the programmatic integration of software tools with dynamic data sources.

Here, we argue that data processing, QC, and analysis can be treated as modular components in a reproducible research pipeline. For some data types, it is already common practice to factor out the processing and QC from the data analysis. For rnaseq data, for example, it is clearly impractical and time consuming to re-run monolithic code that performs alignment, QC, gene expression quantification, and analysis each time the downstream analysis is changed. Our goal is to ensure that downstream data analysis maintains dependencies on upstream raw data and processing but that the processed data can be efficiently distributed to users in an independent manner and updated when there are changes.

Here, we present how the Vaccine Immunology Statistical Center (VISC) at the Fred Hutchinson Cancer Research Center has addressed this problem and implemented a reproducible research work-flow that scales to medium-sized collaborative teams by leveraging free and open source tools, including R, Bioconductor and git 22, 31 .

 Methods Operation

In order to use DataPackageR an R installation (≥3.5.0) is required. Associated dependencies are listed in the package’s DESCRIPTION file and are automatically installed when using the install_github() API from the devtools package. There are no minimum memory, CPU, or storage requirements apart from what is necessary to perform data processing, which varies on a case–by-case basis.

 Implementation

Our work-flow is built around the DataPackageR R package, which provides a framework for decoupling data preprocessing from data analysis, while maintaining traceability and data provenance 18 .

DataPackageR builds upon the features already provided by the R package system. R packages provide a convenient mechanism for including documentation as part of the built-in help system, as well as long-form vignettes, and version information and distribution of the entire package. Importantly, R packages often include data stored as R objects, and some packages, particularly under BioConductor, are devoted solely to the distribution of data sets 22 . The accepted mechanism for such distribution is to store R objects as rda files in the data directory of the package source tree and to store the source code used to produce those data sets in data-raw. The devtools package provides some mechanisms to process the source code into stored data objects 32 .

Data processing code provided by the user (in the form of Rmd files preferably, and R files optionally) is run and the results are automatically included as package vignettes, with output data sets (specified by the user) included as data objects in the package. Notably, this process, while apparently mirroring much of the existing R package build process, is disjointed from it, thereby allowing the decoupling of computationally long or expensive data processing from routine package building and installation. This allows DataPackageR to decouple data munging and tidying from data analysis while maintaining data provenance in the form of a vignette in the final package where the end-user can view and track how individual data sets have been processed. This is particularly useful for large or complex data that involve extensive preprocessing of primary or raw data (e.g. alignment of fastq files for rnaseq or gating of fcm data), and where computation may be prohibitively long or involve software dependencies not immediately available to the end-user.

DataPackageR implements these features on top of a variety of tidyverse tools including devtools, roxygen2, rmarkdown, utils, yaml, purrr. The complete list of package dependencies is in the package DESCRIPTION file.

 Package structure

To construct a data package using DataPackageR, the user invokes the datapackage.skeleton() API, which behaves like R’s package.skeleton() , creating the necessary directory structure with some modifications. A listing of the structure of DataPackageR skeleton package directory, with other associated files is shown below:

package root |--- datapackager.yml # Configuration file controlling | # the package build process. |--- DESCRIPTION # Adds a DataVersion | # string to version the | # data set. |--- NAMESPACE |--- DATADIGEST # Stores an MD5 hash of each | # data object in the package. |--- R |--- Read-and-delete-me.txt # Further instructions | # on building the package. |--- data # Holds processed, analysis-ready data objects. |--- data-raw # User code for data | # processing is placed here by | # datapackage.skeleton(). |--- documentation.R # Auto generated roxygen documentation | # for data set objects. |--- inst | |___ extdata # (small) raw data files. | |___ doc # Processed vignettes are moved here. | # Data processing code is accessible in the | # final package via the vignette() API. |--- vignettes # Scripts in data-raw | # are processed into vignettes. |___ man # Autogenerated documentation is processed # into rd files.

The datapackage.skeletion API takes several new arguments apart from the package name. First, code_files takes a vector of paths to Rmd or R scripts that perform the data processing. These are moved into the data-raw directory by package.skeleton() . The argument r_object_names takes a vector of quoted R object names. These are objects that are to be stored in the final package and it is expected that they are created by the code in the R or Rmd files. These can be tidy data tables, or arbitrary R objects and data structures (e.g. S4 objects) that will be consumed by the package end-user. Information about the processing scripts and data objects is stored in a configuration file named datapackager.yml in the package root directory and only used by the package build process. The scripts may read raw data from any location, but generally the package maintainer should place it in inst/extdata if file size is not prohibitive for distribution.

 The build_package API

Once code and data are in place, the build_package() API invokes the build process. This API is the only way to invoke the execution of code in data-raw to produce data sets stored in data. It is not invoked through R’s standard R CMD build or R CMD INSTALL APIs, thereby decoupling long and computationally intensive processing from the standard build process invoked by end-users. Upon invocation of build_package() the R and Rmd files specified in datapackager.yml will be compiled into package vignettes and moved into the inst/doc directory, data objects will be created and moved into data, data objects will be version tagged with their checksum and recorded in the DATADIGEST file in the package root, and a roxygen markup skeleton will be created for each data object in the package.

 YAML configuration

The datapackager.yml configuration file in the package root controls the build process by specifying which R and Rmd files should be processed and which named R objects are expected to be included as data sets in the package. The listing below shows the structure of the YAML configuration file used by DataPackageR to control compilation and inclusion of data objects in the package:

configuration: files: # files property lists process_dataset_one.Rmd: # R or Rmd code files name: process_dataset_one.Rmd # Each file has a name enabled: yes # The enabled property specifies # if the file should be processed process_dataset_two.Rmd: name: process_dataset_two.Rmd enabled: yes objects: # A list of the data objects created - dataset_one # by processing the files. - dataset_two - dataset_three

The API for interacting with this file is outlined in Table 1.

The API for interacting with a YAML config file used by DataPackageR allows the user to add and remove data objects and code files, toggle compilation of files, and read and write the configuration to the data package.
API call Property return value
yml_add_files() file config object
yml_remove_files() file config object
yml_add_objects() objects config object
yml_remove_objects() objects config object
yml_find() config file config object
yml_write() config file null
yml_enable_compile() enable config object
yml_disable_compile() enable config object
Dataset versioning

During the build, the DATADIGEST file is auto-generated. This file contains an md5 hash of each data object stored in the package as well as an overall data set version string. These hashes are checked when the package is rebuilt; if they do not match, it indicates the format of the processed data has changed (either because the primary data has changed, or because the processing code has changed to update the data set). In these cases, the DATADIGEST for the changed object is updated and the minor version of the DataVersion string in the DESCRIPTION file is automatically incremented. The DataVersion for a package can be checked by the dataVersion() API, allowing end-users to produce reports based on the expected version of a data set ( Figure 1).

A schematic overview of the components in our reproducible data packaging work-flow that decouples data processing from data analysis. Data documentation

DataPackageR ensures that documentation is available for each data object included in a package by automatically creating a roxygen markup stub for each object that can then be filled in by the user. Undocumented objects are explicitly excluded from the final package.

Packages can be readily distributed in source or tarball form (together with the processed data sets under /data/ and raw data sets under /inst/extdata). Within VISC we leverage git and github to provide version control of data package source code. By leveraging DataPackageR, the data processing is decoupled from the usual build process and does not need to be run by the end-user each time a package is downloaded and installed. Documentation in the form of Rd files, one for each data object in the package, as well as html vignettes describing the data processing, are included in the final package. These describe the data sets as well as how data was transformed, filtered, and otherwise processed from its raw state.

 Use cases

DataPackageR was developed as a lightweight alternative to existing reproducible work-flow tools (e.g. Galaxy 33 ), or to fully fledged database solutions that are often beyond the scope of most short-term projects. DataPackageR plugs easily into any existing R-based data analysis work-flow, since existing data processing code needs only to be formatted into Rmarkdown (ideally). It is particularly suited for long-running or complex data processing tasks, or tasks that depend on large data sets that may not be available to the end user (e.g. FASTQ alignment or raw flow cytometry data processing). Such tasks do not fit well into the standard R CMD build paradigm, for example either as vignettes or .R files under /data since these would be invoked each time an end user builds a package from source. We desire, however, to maintain a link between the processed data sets and the processing code that generates them. We note that DataPackageR is distinct from other reproducible research frameworks such as workflowr or drake 30 , in that it is designed to reproducibly prepare data for analysis, using an existing code base, with little additional effort. The product of DataPackageR is nothing more than an R package that can be used by anyone. The resulting data packages are meant to be shared, to serve as the basis for further analysis ( Figure 1) and distributed as part of publications. These downstream analyses may leverage any of the existing work-flow management tools. Our goal is that data sets forming the basis of scientific findings can be confidently shared in their processed form which is often much smaller and easier to distribute.

Within the VISC, a team of analysts, statistical programmers, and data managers work collaboratively to analyze pre-clinical data arising from multiple trials. There are multiple assays per trial. The challenges associated with ensuring the entire team works from the same version of a frequently changing and dynamic data set, motivated the development of DataPackageR. The tool is routinely used to process and standardize trial data for submission to The Collaboration for AIDS Vaccine Discovery (CAVD) Data Space.

We demonstrate how DataPackageR is used to process and package multiple types of assay data from an animal trial of an experimental HIV vaccine.

Data

We demonstrate the use of DataPackageR for processing data from a vaccine study, named MX1, designed to examine the antibody responses to heterologous N7 Env prime-boost immunization in macaques. The study had four treatment groups plus a control arm, with six animals per group. Samples were collected at three time points: t1: baseline, post-prime 2, post-boost 1, t2: post-boost 2, t3: post-boost 3. Six assays were run at each time point, using either serum samples or peripheral blood mononuclear cells (PBMCs). The assays were: 1) enzyme-linked immunosorbent assay (ELISA), an immunological assay that enables detection of antibodies, antigens, proteins and/or glycoproteins (serum); 2) a neutralizing antibody (Ab) assay (serum); 3) a binding antibody multiplex assay (BAMA) to assess antibody response breadth (serum); 4) a BAMA assay to permit epitope mapping (serum); 5) an antibody dependent cellular cytotoxicity (ADCC) assay (serum); and 6) an intracellular cytokine staining assay to assess cellular responses (PBMCs).

The raw data and environment to reproduce the processing with DataPackageR are distributed as a Docker image on hub.docker.com as gfinak/datapackager:latest. We have restricted the number of FCS files distributed in the container to limit the size of the image and speed up processing of FCM data for demonstration purposes.

 Flow cytometry and other assay data

Flow cytometry (FCM) is a high content, high throughput assay for which VISC leverages specialized data processing and analytics tools. Raw FCS files and manual gate information in the form of FlowJo (FlowJo LLC, Ashland, OR) workspace files are uploaded directly to VISC by the labs. The raw data are processed with open source BioConductor software ( flowWorkspace) to import and reproduce the manual gating, extract cell subpopulation statistics, and access the single-cell event-level data required for downstream modeling of T-cell polyfunctionality and immunogenicity 34– 36 . Tables of extracted cell populations, cell counts, proportions, and fluorescence intensities are included in study packages, together with an Rmarkdown vignette describing the data processing. Due to the size of the raw FCS files, they are imported for processing from a location external to the data package source tree, so that the raw files are not part of the final package, but vignettes outlining the data processing are automatically included.

Remaining assay data are of reasonable size and are provided raw data in tabular (csv) form, imported into the package, processed and standardized from the inst/extdata package directory. Users can run and connect to an Rstudio instance in the container, where code and data to build the MX1 data package reside.

 Summary

Reproducibility is increasingly emphasized for scientific publications. We describe a new utility R package, DataPackageR that serves to help automate and track the processing and standardization of diverse data sets into analysis-ready data packages that can be easily distributed for analysis and publication. DataPackageR, when paired with a version control system such as git, decouples data processing from data analysis while tracking changes to data sets, ensuring data objects are documented, and keeping a record of data processing pipelines as vignettes within the data package. The principle behind the tool is that it remains a lightweight and non-intrusive framework that easily plugs into most R-based data analytic work-flows. It places few restrictions on the user code therefore most existing scripts can be ported to use the package. The VISC has been using DataPackageR for a number of years to perform reproducible end-to-end analysis of animal trial data, and the package has been used to publicly share sets for a number of published manuscripts 35, 37, 38 .

 Software and data availability

Source code available from: http://github.com/RGLab/DataPackageR

Archived source code as at time of publication: https://doi.org/10.5281/zenodo.1292095 39

Reproducible examples: http://hub.docker.com/r/gfinak/datapackager/

License: MIT license

Partial data for the MX1 study to demonstrate processing using DataPackageR are available as a docker container from gfinak/DataPackageR:latest. The processed MX1 data are available on the CAVD DataSpace data sharing and discovery tool at http://dataspace.cavd.org under study identifier, CAVD 451. At the time of publication the complete data set is available to CAVD members only.

 Acknowledgements

The authors wish to acknowledge the contributions of the members of the VISC and the CAVD Data Space (CDS) for contributions to testing and feedback on the software. We also acknowledge the Collaboration for AIDS Vaccine Discovery (CAVD), the Comprehensive Antibody Vaccine Immune Monitoring Consortium (CAVIMC) and the Comprehensive Cellular Vaccine Immune Monitoring Consortium (CCVIMC), as well as Dr. Shiu-Lok Hu.

 Baggerly KA Coombes KR : What information should be required to support clinical "omics" publications? Clin Chem. 2011;57(5):688–690. 21364027 10.1373/clinchem.2010.158618 Gentleman R Lang DT : Statistical analyses and reproducible research. Bioconductor Project Working Papers. 2004. Reference Source Marwick B Boettiger C Mullen L : Packaging data analytical work reproducibly using R (and friends). PeerJ Preprints ; PeerJ Inc.,2018. 10.7287/peerj.preprints.3192v2 Stodden V : Enabling reproducible research: Open licensing for scientific innovation. International Journal of Communications Law and Policy. 2009. Reference Source Stodden V Borwein J Bailey DH : Publishing standards for computational science: "Setting the default to reproducible".2013. Reference Source Lortie CJ : A review of R for data science: Key elements and a critical analysis. PeerJ Preprints ; PeerJ Inc.,2017. 10.7287/peerj.preprints.2873v1 Wickham H Grolemund G : R for data science: Import, tidy, transform, visualize, and model data. ‘O’Reilly Media, Inc.’,2016. Reference Source Huang Y Gottardo R : Comparability and reproducibility of biomedical data. Brief Bioinform. 2013;14(4):391–401. 23193203 10.1093/bib/bbs078 3713713 Buck S : Solving reproducibility. Science. 2015;348(6242):1403. 26113692 10.1126/science.aac8041 Peng R : The reproducibility crisis in science: A statistical counterattack. Significance. 2015;12(3):30–32. 10.1111/j.1740-9713.2015.00827.x Morrison SJ : Time to do something about reproducibility. eLife. 2014;3:e03981. 25493617 10.7554/eLife.03981 4260475 Yaffe MB : Reproducibility in science. Sci Signal. 2015;8(371):eg5. 25852185 10.1126/scisignal.aaa5764 Begley CG Ioannidis JP : Reproducibility in science: Improving the standard for basic and preclinical research. Circ Res. 2015;116(1):116–126. 25552691 10.1161/CIRCRESAHA.114.303819 Stodden V Leisch F Peng RD : Implementing reproducible research. CRC Press,2014. Reference Source Freedman LP Inglese J : The increasing urgency for standards in basic biologic research. Cancer Res. 2014;74(15):4024–4029. 25035389 10.1158/0008-5472.CAN-14-0925 4975040 Boettiger C : An introduction to docker for reproducible research. Oper Syst Rev. 2015;49(1):71–79. 10.1145/2723872.2723882 McNutt M : Journals unite for reproducibility. Science. 2014;346(6210):679. 25383411 10.1126/science.aaa1724 Peng RD : Reproducible research in computational science. Science. 2011;334(6060):1226–1227. 22144613 10.1126/science.1213847 3383002 Gentleman R : Reproducible research: A bioinformatics case study. Stat Appl Genet Mol Biol. 2005;4(1): Article2. 16646837 10.2202/1544-6115.1034 Peng RD : Reproducible research and Biostatistics. Biostatistics. 2009;10(3):405–408. 19535325 10.1093/biostatistics/kxp014 Mesirov JP : Computer science. Accessible reproducible research. Science. 2010;327(5964):415–6. 20093459 10.1126/science.1179653 3878063 Gentleman RC Carey VJ Bates DM : Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5(10):R80. 15461798 10.1186/gb-2004-5-10-r80 545600 Finak G Gottardo R : Promises and Pitfalls of High-Throughput Biological Assays. Methods Mol Biol. 2016;1415:225–243. 27115636 10.1007/978-1-4939-3572-7_12 Allaire J : Rmarkdown: Dynamic documents for R.2015. Xie Y : Knitr: A comprehensive tool for reproducible research in R. Implement Reprod Res. 2014;1:20. Reference Source Baumer B Udwin D : R markdown. WIREs Comput Stat. 2015;7(3):167–177. 10.1002/wics.1348 Ram K : Git can facilitate greater reproducibility and increased transparency in science. Source Code Biol Med. 2013;8(1):7. 23448176 10.1186/1751-0473-8-7 3639880 Project, T: rOpenSci. Use of an r package to facilitate reproducible research.2015. Project, T: rOpenSci. A guide to reproducible research.2015. Michael Landau W : The drake R package: A pipeline toolkit for reproducibility and high-performance computing. JOSS. 2018;3(21):550. 10.21105/joss.00550 Ihaka R Gentleman R : R: A language for data analysis and graphics. J Comput Graph Stat. 1996;5(3):299–314. 10.2307/1390807 Wickham H Chang W : Devtools: Tools to make developing r packages easier.2018. Reference Source Goecks J Nekrutenko A Taylor J : Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11(8):R86. 20738864 10.1186/gb-2010-11-8-r86 2945788 Finak G Jiang M Andre M : FlowWorkspace: A new R package for importing flow cytometry data into bioconductor from flowJo. Fred Hutchinson Cancer Research Center; Poster B232, CYTO 2011 XXVI Congress of the International Society for Advancement of Cytometry Baltimore Convention Center, Baltimore, Maryland, USA May 21 Ð 25,2010. Lin L Finak G Ushey K : COMPASS identifies t-cell subsets correlated with clinical outcomes. Nat Biotechnol. 2015;33(6):610–616. 26006008 10.1038/nbt.3187 4569006 Finak G McDavid A Chattopadhyay P : Mixture models for single-cell assays with applications to vaccine studies. Biostatistics. 2014;15(1):87–101. 23887981 10.1093/biostatistics/kxt024 3862207 Finak G McDavid A Yajima M : MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16:278. 26653891 10.1186/s13059-015-0844-5 4676162 Bolton DL McGinnis K Finak G : Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells. PLoS Pathog. 2017;13(6):e1006445. 28654687 10.1371/journal.ppat.1006445 5507340 Finak G Obrecht P : RGLab/DataPackageR v0.13.2 (Version v0.13.2). Zenodo. 2018. Data Source 10.21956/gatesopenres.13908.r26541 Reviewer response for version 1 Lun Aaron T.L. 1 Referee https://orcid.org/0000-0002-3564-4813 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

Competing interests: No competing interests were disclosed.

 25 6 2018 Copyright: © 2018 Lun ATL 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

In this article, Finak et al. describe a new package for pre-processing and sharing of data within the R programming language. Their DataPackageR package provides a standardized framework for traceable pre-processing and version control of R data objects, facilitating reproducible research across a diverse team of collaborators. The manuscript is clear and concise, and the function and benefits of the software are clear. Nonetheless, I have some comments (listed below) that the authors might consider to improve both the software and the manuscript.

The processed data are stored as RData files in the data/ directory and distributed as part of the constructed data package. These files can become somewhat large (> 100 MB), which in and of itself is not a problem; indeed, having to deal with large data files may not be avoidable in some contexts. However, the text on page 5 and Figure 1 suggest that the user should place the generated data files under version control via Git prior to distribution. With Git, every clone of the package will contain every version of all data files, inflating the size of the repository for download and on disk. This may become prohibitive for practical use (e.g., GitHub forbids uploads of files above a certain size), and feels unnecessary given that only one version of the data will be active at any one time. Perhaps the authors would consider supporting the versioning and acquisition of RData files from a separate location, mimicking the behaviour of (or directly using) Bioconductor's ExperimentHub where large data files are downloaded and cached locally as needed?

The YAML header seems to be limited to R scripts or Rmd files for data pre-processing. However, as the authors would appreciate, a lot of pre-processing is performed on the command line, e.g., with aligners and related software. It would be awkward (and involve extra work) to have to wrap these scripts in R/ Rmd files in order for them to be executed by DataPackageR. To provide a concrete example: I would like DataPackageR to directly call my existing Bash scripts for alignment of RNA-seq data, followed by execution of Rmd reports for assigning reads to genes to get a count table that is saved as an RData object.

Does the YAML header permit dependencies between scripts? Say I have a long pre-processing pipeline that is split across multiple scripts for convenience. Does the order of files in the header reflected in the order of execution? And are they executed in the same location, so that intermediate files produced by one script (that are not intended to be included in the final package) can be picked up as inputs to the following script?

The MX1 use case on pages 6-7 seems under-described - I don't get an intuition as to why DataPackageR was beneficial for this project. It would be demonstrative to have some specific examples of how the version control and change tracking were advantageous in a collaborative environment. For example, how many DataVersion bumps were performed throughout the course of the project, and why? Were there any issues arising from changes to the processed data, and how were these resolved?

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.