refs.bib

@Manual{wickham_et_al,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain Fran\c{c}ois and Lionel Henry and Kirill M\"{u}ller},
    year = {2019},
    note = {R package version 0.8.3},
    url = {https://CRAN.R-project.org/package=dplyr},
  }

@Manual{xaringan,
    title = {xaringan: Presentation Ninja},
    author = {Yihui Xie},
    year = {2019},
    note = {R package version 0.12},
    url = {https://CRAN.R-project.org/package=xaringan},
  }

@book{Wickham:2015:RP:2904414,
 author = {Wickham, Hadley},
 title = {R Packages},
 year = {2015},
 isbn = {1491910593, 9781491910597},
 edition = {1st},
 publisher = {O'Reilly Media, Inc.},
}

@book{Wickhamr4ds,
 author = {Wickham, Hadley and Çetinkaya-Rundeland, Mine and Grolemund, Garrett},
 title = {R for Data Science (2e)},
 year = {2023},
 edition = {2nd},
 publisher = {O'Reilly Media, Inc.},
 url = {https://r4ds.hadley.nz/},
}

@MISC{Bryan_Happy_Git,
  title        = "Happy Git and {GitHub} for the useR",
  author       = "Bryan, Jenny and Hester, Jim",
  abstract     = "Using Git and GitHub with R, Rstudio, and R Markdown",
  howpublished = "\url{https://happygitwithr.com/}",
  note         = "Accessed: 2019-11-22"
}

@Book{ggplot2,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }



@ARTICLE{Wickham2014-nl,
  title    = "Tidy Data",
  author   = "Wickham, Hadley",
  abstract = "A huge amount of effort is spent cleaning data to get it ready
              for analysis, but there has been little research on how to make
              data cleaning as easy and effective as possible. This paper
              tackles a small, but important, component of data cleaning: data
              tidying. Tidy datasets are easy to manipulate, model and
              visualize, and have a specific structure: each variable is a
              column, each observation is a row, and each type of observational
              unit is a table. This framework makes it easy to tidy messy
              datasets because only a small set of tools are needed to deal
              with a wide range of un-tidy datasets. This structure also makes
              it easier to develop tidy tools for data analysis, tools that
              both input and output tidy datasets. The advantages of a
              consistent data structure and matching tools are demonstrated
              with a case study free from mundane data manipulation chores.",
  journal  = "Journal of Statistical Software, Articles",
  volume   =  59,
  number   =  10,
  pages    = "1--23",
  year     =  2014
}


@Manual{R-core,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2022},
    url = {https://www.R-project.org/},
  }


  @Article{R-tidyverse,
    title = {Welcome to the {tidyverse}},
    author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain Françoi and  Garrett Grolemun and  Alex Haye and  Lionel Henr and  Jim Heste and  Max Kuh and  Thomas Lin Pederse and  Evan Mille and  Stephan Milton Bach and  Kirill Müll and , Jeroen Oo and , David Robins and , Dana Paige Seid and , Vitalie Spi and , Kohske Takahas and , Davis Vaugh and , Claus Wil and , Kara W and , Hiroaki Yutani},
    year = {2019},
    journal = {Journal of Open Source Software},
    volume = {4},
    number = {43},
    pages = {1686},
    doi = {10.21105/joss.01686},
  }

@Manual{R-here,
    title = {here: A Simpler Way to Find Your Files},
    author = {Kirill Müller},
    year = {2017},
    note = {R package version 0.1},
    url = {https://CRAN.R-project.org/package=here},
  }
  

@MISC{Bryan-Hester,
  title        = "Chapter 2 Project-oriented workflow | What They Forgot to
                  Teach You About {R}",
  author       = "Jennifer Bryan and Jim Hester",
  abstract     = "The stuff you need to know about R, besides statistical
                  analysis.",
  howpublished = "\url{https://whattheyforgot.org/project-oriented-workflow.html}",
  note         = "Accessed: 2019-9-26"
}



@BOOK{Codd1990-th,
  title     = "The Relational Model for Database Management: Version 2",
  author    = "Codd, E F",
  publisher = "Addison-Wesley Longman Publishing Co., Inc.",
  year      =  1990,
  address   = "Boston, MA, USA"
}

  @Manual{magrittr,
    title = {magrittr: A Forward-Pipe Operator for R},
    author = {Stefan Milton Bache and Hadley Wickham},
    year = {2014},
    note = {R package version 1.5},
    url = {https://CRAN.R-project.org/package=magrittr},
  }
  
 @Manual{haven,
    title = {haven: Import and Export 'SPSS', 'Stata' and 'SAS' Files},
    author = {Hadley Wickham and Evan Miller},
    year = {2018},
    note = {R package version 1.1.2},
    url = {https://CRAN.R-project.org/package=haven},
  }
  
@Manual{googlesheets4,
    title = {googlesheets4: Access Google Sheets using the Sheets API V4},
    author = {Jennifer Bryan},
    year = {2021},
    note = {R package version 0.3.0},
    url = {https://CRAN.R-project.org/package=googlesheets4},
  }



@ARTICLE{Wilson2014-ij,
  title    = "Best practices for scientific computing",
  author   = "Wilson, Greg and Aruliah, D A and Brown, C Titus and Chue Hong,
              Neil P and Davis, Matt and Guy, Richard T and Haddock, Steven H D
              and Huff, Kathryn D and Mitchell, Ian M and Plumbley, Mark D and
              Waugh, Ben and White, Ethan P and Wilson, Paul",
  journal  = "PLoS Biol.",
  volume   =  12,
  number   =  1,
  pages    = "e1001745",
  month    =  jan,
  year     =  2014,
  language = "en"
}


@ARTICLE{Wilson2017-cp,
  title    = "Good enough practices in scientific computing",
  author   = "Wilson, Greg and Bryan, Jennifer and Cranston, Karen and Kitzes,
              Justin and Nederbragt, Lex and Teal, Tracy K",
  abstract = "Computers are now essential in all branches of science, but most
              researchers are never taught the equivalent of basic lab skills
              for research computing. As a result, data can get lost, analyses
              can take much longer than necessary, and researchers are limited
              in how effectively they can work with software and data.
              Computing workflows need to follow the same practices as lab
              projects and notebooks, with organized data, documented steps,
              and the project structured for reproducibility, but researchers
              new to computing often don't know where to start. This paper
              presents a set of good computing practices that every researcher
              can adopt, regardless of their current level of computational
              skill. These practices, which encompass data management,
              programming, collaborating with colleagues, organizing projects,
              tracking work, and writing manuscripts, are drawn from a wide
              variety of published sources from our daily lives and from our
              work with volunteer organizations that have delivered workshops
              to over 11,000 people since 2010.",
  journal  = "PLoS Comput. Biol.",
  volume   =  13,
  number   =  6,
  pages    = "e1005510",
  month    =  jun,
  year     =  2017,
  language = "en"
}

@ARTICLE{Bryan2018-hl,
  title     = "Excuse Me, Do You Have a Moment to Talk About Version Control?",
  author    = "Bryan, Jennifer",
  abstract  = "ABSTRACTData analysis, statistical research, and teaching
               statistics have at least one thing in common: these activities
               all produce many files! There are data files, source code,
               figures, tables, prepared reports, and much more. Most of these
               files evolve over the course of a project and often need to be
               shared with others, for reading or edits, as a project unfolds.
               Without explicit and structured management, project organization
               can easily descend into chaos, taking time away from the primary
               work and reducing the quality of the final product. This unhappy
               result can be avoided by repurposing tools and workflows from
               the software development world, namely, distributed version
               control. This article describes the use of the version control
               system Git and the hosting site GitHub for statistical and data
               scientific workflows. Special attention is given to projects
               that use the statistical language R and, optionally, R Markdown
               documents. Supplementary materials include an annotated set of
               links to step-by-step tutorials, real world examples, and other
               useful learning resources. Supplementary materials for this
               article are available online.",
  journal   = "Am. Stat.",
  publisher = "Taylor \& Francis",
  volume    =  72,
  number    =  1,
  pages     = "20--27",
  month     =  jan,
  year      =  2018
}


@MISC{Wickham_undated-jy,
  title        = "Advanced {R}",
  author       = "Wickham, Hadley",
  abstract     = "The book is designed primarily for R users who want to
                  improve their programming skills and understanding of the
                  language. It should also be useful for programmers coming to
                  R from other languages, as help you to understand why R works
                  the way it does.",
  howpublished = "\url{https://adv-r.hadley.nz/}",
  year = "2019"
}

 @Manual{markdown1,
    title = {rmarkdown: Dynamic Documents for R},
    author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
    year = {2019},
    note = {R package version 1.16},
    url = {https://github.com/rstudio/rmarkdown},
  }
  
@Book{markdown2,
    title = {R Markdown: The Definitive Guide},
    author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2018},
    note = {ISBN 9781138359338},
    url = {https://bookdown.org/yihui/rmarkdown},
  }
  
  @Manual{kableExtra,
    title = {kableExtra: Construct Complex Table with 'kable' and Pipe Syntax},
    author = {Hao Zhu},
    year = {2019},
    note = {R package version 1.1.0},
    url = {https://CRAN.R-project.org/package=kableExtra},
  }
  
  @Manual{flextable,
    title = {flextable: Functions for Tabular Reporting},
    author = {David Gohel},
    year = {2019},
    note = {R package version 0.5.5},
    url = {https://CRAN.R-project.org/package=flextable},
  }

@ARTICLE{Sandve2013-nd,
  title    = "Ten simple rules for reproducible computational research",
  author   = "Sandve, Geir Kjetil and Nekrutenko, Anton and Taylor, James and
              Hovig, Eivind",
  journal  = "PLoS Comput. Biol.",
  volume   =  9,
  number   =  10,
  pages    = "e1003285",
  month    =  oct,
  year     =  2013,
  language = "en"
}

 @Manual{Dowle_Srinivasan_2019,
    title = {data.table: Extension of `data.frame`},
    author = {Matt Dowle and Arun Srinivasan},
    year = {2019},
    note = {R package version 1.12.6},
    url = {https://CRAN.R-project.org/package=data.table},
  }
  
  @Manual{readxl,
    title = {readxl: Read Excel Files},
    author = {Hadley Wickham and Jennifer Bryan},
    year = {2019},
    note = {R package version 1.3.1},
    url = {https://CRAN.R-project.org/package=readxl},
  }
  @Manual{rvest,
    title = {rvest: Easily Harvest (Scrape) Web Pages},
    author = {Hadley Wickham},
    year = {2019},
    note = {R package version 0.3.5},
    url = {https://CRAN.R-project.org/package=rvest},
  }
  
  @Book{bookdownbook,
    title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
    author = {Yihui Xie},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2016},
    note = {ISBN 978-1138700109},
    url = {https://bookdown.org/yihui/bookdown},
  }
  @Manual{bookdown,
    title = {bookdown: Authoring Books and Technical Documents with R Markdown},
    author = {Yihui Xie},
    year = {2023},
    note = {R package version 0.32},
    url = {https://github.com/rstudio/bookdown},
  }
  
  @ARTICLE{Wilkinson2016-pu,
  title    = "The {FAIR} Guiding Principles for scientific data management and
              stewardship",
  author   = "Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, I
              Jsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak,
              Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva
              Santos, Luiz Bonino and Bourne, Philip E and Bouwman, Jildau and
              Brookes, Anthony J and Clark, Tim and Crosas, Merc{\`e} and
              Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo,
              Chris T and Finkers, Richard and Gonzalez-Beltran, Alejandra and
              Gray, Alasdair J G and Groth, Paul and Goble, Carole and Grethe,
              Jeffrey S and Heringa, Jaap and 't Hoen, Peter A C and Hooft, Rob
              and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott
              J and Martone, Maryann E and Mons, Albert and Packer, Abel L and
              Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van
              Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and
              Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz,
              Morris A and Thompson, Mark and van der Lei, Johan and van
              Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and
              Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and
              Mons, Barend",
  abstract = "There is an urgent need to improve the infrastructure supporting
              the reuse of scholarly data. A diverse set of
              stakeholders-representing academia, industry, funding agencies,
              and scholarly publishers-have come together to design and jointly
              endorse a concise and measureable set of principles that we refer
              to as the FAIR Data Principles. The intent is that these may act
              as a guideline for those wishing to enhance the reusability of
              their data holdings. Distinct from peer initiatives that focus on
              the human scholar, the FAIR Principles put specific emphasis on
              enhancing the ability of machines to automatically find and use
              the data, in addition to supporting its reuse by individuals.
              This Comment is the first formal publication of the FAIR
              Principles, and includes the rationale behind them, and some
              exemplar implementations in the community.",
  journal  = "Sci Data",
  volume   =  3,
  pages    = "160018",
  month    =  mar,
  year     =  2016,
  language = "en"
}
@ARTICLE{Goodman2016-tl,
  title    = "What does research reproducibility mean?",
  author   = "Goodman, Steven N and Fanelli, Daniele and Ioannidis, John P A",
  abstract = "The language and conceptual framework of ``research
              reproducibility'' are nonstandard and unsettled across the
              sciences. In this Perspective, we review an array of explicit and
              implicit definitions of reproducibility and related terminology,
              and discuss how to avoid potential misunderstandings when these
              terms are used as a surrogate for ``truth.''",
  journal  = "Sci. Transl. Med.",
  volume   =  8,
  number   =  341,
  pages    = "341ps12",
  month    =  jun,
  year     =  2016,
  language = "en"
}

@TECHREPORT{K_Bollen2015-un,
  title       = "Social, Behavioral, and Economic Sciences Perspectives on
                 Robust and Reliable Science",
  author      = "Bollen, K and Cacioppo, J T and Kaplan, J and Krosnick, J
                 and Olds, J L",
  institution = "National Science Foundation",
  year        =  2015
}


@TECHREPORT{OECD_Global_Science_Forum2020-cp,
  title       = "Building digital workforce capacity and skills for
                 data-intensive science",
  author      = "{OECD Global Science Forum}",
  institution = "OECD",
  month       =  jun,
  year        =  2020
}

@ARTICLE{Markowetz2015,
  title    = "Five selfish reasons to work reproducibly",
  author   = "Markowetz, Florian",
  abstract = "And so, my fellow scientists: ask not what you can do for
              reproducibility; ask what reproducibility can do for you! Here, I
              present five reasons why working reproducibly pays off in the
              long run and is in the self-interest of every ambitious,
              career-oriented scientist.",
  journal  = "Genome Biol.",
  volume   =  16,
  pages    = "274",
  month    =  dec,
  year     =  2015,
  language = "en"
}


@BOOK{National_Academies_of_Sciences_Engineering2019-sb,
  title     = "Understanding Reproducibility and Replicability",
  author    = "{National Academies of Sciences, Engineering} and {Medicine} and
               {Policy} and Affairs, Global and {Committee on Science,
               Engineering, Medicine} and {Public Policy} and {Board on
               Research Data} and {Information} and {Division on Engineering}
               and {Physical Sciences} and {Committee on Applied} and
               Statistics, Theoretical and {Board on Mathematical Sciences} and
               {Analytics} and {Division on Earth} and {Life Studies} and
               {Nuclear} and {Radiation Studies Board} and {Division of
               Behavioral} and {Social Sciences} and {Education} and {Committee
               on National Statistics} and {Board on Behavioral, Cognitive} and
               {Sensory Sciences} and {Committee on Reproducibility} and
               {Replicability}",
  abstract  = "Scientific research has evolved from an activity mainly
               undertaken by individuals operating in a few locations to many
               teams, large communities, and complex organizations involving
               hundreds to thousands of individuals worldwide. In the 17th
               century, scientists would communicate through letters and were
               able to understand and assimilate major developments across all
               the emerging major disciplines. In 2016---the most recent year
               for which data are available---more than 2,295,000 scientific
               and engineering research articles were published worldwide
               (National Science Foundation, 2018e). In addition, the number of
               scientific and engineering fields and subfields of research is
               large and has greatly expanded in recent years, especially in
               fields that intersect disciplines (e.g., biophysics); more than
               230 distinct fields and subfields can now be identified. The
               published literature is so voluminous and specialized that some
               researchers look to information retrieval, machine learning, and
               artificial intelligence techniques to track and apprehend the
               important work in their own fields.",
  publisher = "National Academies Press (US)",
  month     =  may,
  year      =  2019
}


@ARTICLE{Baggerly2009-pw,
  title     = "{DERIVING} {CHEMOSENSITIVITY} {FROM} {CELL} {LINES}: {FORENSIC}
               {BIOINFORMATICS} {AND} {REPRODUCIBLE} {RESEARCH} {IN}
               {HIGH-THROUGHPUT} {BIOLOGY}",
  author    = "Baggerly, Keith A and Coombes, Kevin R",
  abstract  = "[High-throughput biological assays such as microarrays let us
               ask very detailed questions about how diseases operate, and
               promise to let us personalize therapy. Data processing, however,
               is often not described well enough to allow for exact
               reproduction of the results, leading to exercises in ``forensic
               bioinformatics'' where aspects of raw data and reported results
               are used to infer what methods must have been employed.
               Unfortunately, poor documentation can shift from an
               inconvenience to an active danger when it obscures not just
               methods but errors. In this report we examine several related
               papers purporting to use microarray-based signatures of drug
               sensitivity derived from cell lines to predict patient response.
               Patients in clinical trials are currently being allocated to
               treatment arms on the basis of these results. However, we show
               in five case studies that the results incorporate several simple
               errors that may be putting patients at risk. One theme that
               emerges is that the most common errors are simple (e.g., row or
               column offsets); conversely, it is our experience that the most
               simple errors are common. We then discuss steps we are taking to
               avoid such errors in our own investigations.]",
  journal   = "Ann. Appl. Stat.",
  publisher = "Institute of Mathematical Statistics",
  volume    =  3,
  number    =  4,
  pages     = "1309--1334",
  year      =  2009
}

  @Manual{R-tidybulk,
    title = {tidybulk: Brings transcriptomics to the tidyverse},
    author = {Stefano Mangiola},
    year = {2020},
    url  = {https://bioconductor.org/packages/release/bioc/html/tidybulk.html},
    note = {R package version 1.0.2},
  }
    @Manual{R-biobroom,
    title = {biobroom: Turn Bioconductor objects into tidy data frames},
    author = {Andrew J. Bass and David G. Robinson and Steve Lianoglou and Emily Nelson and John D. Storey and with contributions from Laurent Gatto},
    year = {2020},
    note = {R package version 1.20.0},
    url = {https://github.com/StoreyLab/biobroom},
  }
  
@ARTICLE{Wickham-style,
  title    = "The tidyverse style guide",
  author   = "Wickham, Hadley",
  howpublished = "\url{https://style.tidyverse.org/}",
  note         = "Accessed: 2020-10-30"
}

@ARTICLE{Knuth1984-ly,
  title     = "Literate Programming",
  author    = "Knuth, D E",
  abstract  = "Abstract. The author and his associates have been experimenting
               for the past several years with a programming language and
               documentation system called WEB. Thi",
  journal   = "Comput. J.",
  publisher = "Oxford Academic",
  volume    =  27,
  number    =  2,
  pages     = "97--111",
  month     =  jan,
  year      =  1984
}
  @Manual{gt,
    title = {gt: Easily Create Presentation-Ready Display Tables},
    author = {Richard Iannone and Joe Cheng and Barret Schloerke},
    year = {2020},
    note = {R package version 0.2.2},
    url = {https://CRAN.R-project.org/package=gt},
  }
  
@Book{rmarkdown_cookbook,
    title = {R Markdown Cookbook},
    author = {Yihui Xie, Christophe Dervieux and Emily Riederer},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2020},
    note = {"ISBN 9780367563837"},
    url = {https://bookdown.org/yihui/rmarkdown-cookbook},
  }
  
@Manual{palmerpenguins,
    title = {palmerpenguins: Palmer Archipelago (Antarctica) penguin data},
    author = {Allison Marie Horst and Alison Presmanes Hill and Kristen B Gorman},
    year = {2020},
    note = {R package version 0.1.0},
    url = {https://allisonhorst.github.io/palmerpenguins/},
  }

@article{Gorman2014,
    author = {Kristen B Gorman and Tony D Williams and William R Fraser},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis)},
    year = {2014},
    month = {03},
    volume = {9},
    url = {https://doi.org/10.1371/journal.pone.0090081},
    pages = {1-14},
    abstract = {Background Sexual segregation in vertebrate foraging niche is often associated with sexual size dimorphism (SSD), i.e., ecological sexual dimorphism. Although foraging behavior of male and female seabirds can vary markedly, differences in isotopic (carbon, δ13C and nitrogen, δ15N) foraging niche are generally more pronounced within sexually dimorphic species and during phases when competition for food is greater. We examined ecological sexual dimorphism among sympatric nesting Pygoscelis penguins asking whether environmental variability is associated with differences in male and female pre-breeding foraging niche. We predicted that all Pygoscelis species would forage sex-specifically, and that higher quality winter habitat, i.e., higher or lower sea ice coverage for a given species, would be associated with a more similar foraging niche among the sexes.  Results P2/P8 primers reliably amplified DNA of all species. On average, male Pygoscelis penguins are structurally larger than female conspecifics. However, chinstrap penguins were more sexually dimorphic in culmen and flipper features than Adélie and gentoo penguins. Adélies and gentoos were more sexually dimorphic in body mass than chinstraps. Only male and female chinstraps and gentoos occupied separate δ15N foraging niches. Strong year effects in δ15N signatures were documented for all three species, however, only for Adélies, did yearly variation in δ15N signatures tightly correlate with winter sea ice conditions. There was no evidence that variation in sex-specific foraging niche interacted with yearly winter habitat quality.  Conclusion Chinstraps were most sexually size dimorphic followed by gentoos and Adélies. Pre-breeding sex-specific foraging niche was associated with overall SSD indices across species; male chinstrap and gentoo penguins were enriched in δ15N relative to females. Our results highlight previously unknown trophic pathways that link Pygoscelis penguins with variation in Southern Ocean sea ice suggesting that each sex within a species should respond similarly in pre-breeding trophic foraging to changes in future winter habitat.},
    number = {3},
    doi = {10.1371/journal.pone.0090081},
}


@ARTICLE{Andrews2018-cy,
  title    = "Identifying cell populations with {scRNASeq}",
  author   = "Andrews, Tallulah S and Hemberg, Martin",
  abstract = "Single-cell RNASeq (scRNASeq) has emerged as a powerful method
              for quantifying the transcriptome of individual cells. However,
              the data from scRNASeq experiments is often both noisy and high
              dimensional, making the computational analysis non-trivial. Here
              we provide an overview of different experimental protocols and
              the most popular methods for facilitating the computational
              analysis. We focus on approaches for identifying biologically
              important genes, projecting data into lower dimensions and
              clustering data into putative cell-populations. Finally we
              discuss approaches to validation and biological interpretation of
              the identified cell-types or cell-states.",
  journal  = "Mol. Aspects Med.",
  volume   =  59,
  pages    = "114--122",
  month    =  feb,
  year     =  2018,
  language = "en",
}

@Book{venables_ripley,
    title = {Modern Applied Statistics with S},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer},
    edition = {Fourth},
    address = {New York},
    year = {2002},
    note = {ISBN 0-387-95457-0},
    url = {http://www.stats.ox.ac.uk/pub/MASS4/},
  }
  
@Manual{GGally,
    title = {GGally: Extension to 'ggplot2'},
    author = {Barret Schloerke and Di Cook and Joseph Larmarange and Francois Briatte and Moritz Marbach and Edwin Thoen and Amos Elberg and Jason Crowley},
    year = {2020},
    note = {R package version 2.0.0},
    url = {https://CRAN.R-project.org/package=GGally},
  }

@Manual{Tidymodels,
    title = {Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles.},
    author = {Max Kuhn and Hadley Wickham},
    url = {https://www.tidymodels.org},
    year = {2020},
  }

@Manual{xaringanExtra,
    title = {xaringanExtra: Extras And Extensions for Xaringan Slides},
    author = {Garrick Aden-Buie},
    year = {2020},
    note = {R package version 0.2.3.9000},
    url = {https://github.com/gadenbuie/xaringanExtra},
  }

@Article{tidyHeatmap,
    title = {tidyHeatmap: an R package for modular heatmap production based on tidy principles},
    author = {Stefano Mangiola and Anthony Papenfuss},
    journal = {Journal of Open Source Software},
    year = {2020},
    volume = {5},
    number = {52},
    pages = {2472},
    url = {https://doi.org/10.21105/joss.02472},
    doi = {10.21105/joss.02472},
  }

@Manual{caret,
    title = {caret: Classification and Regression Training},
    author = {Max Kuhn},
    year = {2022},
    note = {R package version 6.0-93},
    url = {https://CRAN.R-project.org/package=caret},
  }