@Manual{Rstat,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2011},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org/},
  }

@Article{Gentleman:2004,
	Abstract = {The Bioconductor project is an initiative for the
		  collaborative creation of extensible software for
		  computational biology and bioinformatics. The goals
		  of the project include: fostering collaborative
		  development and widespread use of innovative
		  software, reducing barriers to entry into
		  interdisciplinary scientific research, and promoting
		  the achievement of remote reproducibility of
		  research results. We describe details of our aims
		  and methods, identify current challenges, compare
		  Bioconductor to other open bioinformatics projects,
		  and provide working examples.},
	Doi = {10.1186/gb-2004-5-10-r80},
	File = {Gentleman_et_al_GenomeBiology_2004.pdf:/home/lgatto/Biblio/Gentleman_et_al_GenomeBiology_2004.pdf:PDF},
	Keywords = {Computational Biology; Internet; Repro; Software;
		  ducibility of Results},
	Owner = {lgatto},
	Pii = {gb-2004-5-10-r80},
	Pmid = {15461798},
	Timestamp = {2006.09.27},
	Url = {https://doi.org/10.1186/gb-2004-5-10-r80},
	author = {Gentleman, Robert C. and Carey, Vincent J. and
		  Bates, Douglas M. and Bolstad, Ben and Dettling,
		  Marcel and Dudoit, Sandrine and Ellis, Byron and
		  Gautier, Laurent and Ge, Yongchao and Gentry, Jeff
		  and Hornik, Kurt and Hothorn, Torsten and Huber,
		  Wolfgang and Iacus, Stefano and Irizarry, Rafael and
		  Leisch, Friedrich and Li, Cheng and Maechler, Martin
		  and Rossini, Anthony J. and Sawitzki, Gunther and
		  Smith, Colin and Smyth, Gordon and Tierney, Luke and
		  Yang, Jean Y. H. and Zhang, Jianhua},
	title = {Bioconductor: open software development for computational biology and bioinformatics.},
	journal = {Genome Biol},
	volume = {5},
	number = {10},
	pages = {-80},
	year = {2004},
	tags = "reproducible research, software, R, Bioconductor, statistics"
}

@ARTICLE{Wickham2019-fz,
  title     = "Welcome to the tidyverse",
  author    = "Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang,
               Winston and McGowan, Lucy and Fran{\c c}ois, Romain and
               Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester,
               Jim and Kuhn, Max and Pedersen, Thomas and Miller, Evan and
               Bache, Stephan and M{\"u}ller, Kirill and Ooms, Jeroen and
               Robinson, David and Seidel, Dana and Spinu, Vitalie and
               Takahashi, Kohske and Vaughan, Davis and Wilke, Claus and Woo,
               Kara and Yutani, Hiroaki",
  journal   = "J. Open Source Softw.",
  publisher = "The Open Journal",
  volume    =  4,
  number    =  43,
  pages     = "1686",
  month     =  nov,
  year      =  2019,
  copyright = "http://creativecommons.org/licenses/by/4.0/"
}



@Book{Sievert2020,
    author = {Carson Sievert},
    title = {Interactive Web-Based Data Visualization with R, plotly, and shiny},
    publisher = {Chapman and Hall/CRC},
    year = {2020},
    isbn = {9781138331457},
    url = {https://plotly-r.com},
  }
  
 
@ARTICLE{Gherardin2014-pj,
  title    = "{OMIP-021}: Simultaneous quantification of human conventional and
              innate-like T-cell subsets",
  author   = "Gherardin, Nicholas A and Ritchie, David S and Godfrey, Dale I
              and Neeson, Paul J",
  journal  = "Cytometry A",
  volume   =  85,
  number   =  7,
  pages    = "573--575",
  month    =  jul,
  year     =  2014,
  language = "en"
}


@ARTICLE{Monaco2016-vo,
  title    = "{flowAI}: automatic and interactive anomaly discerning tools for
              flow cytometry data",
  author   = "Monaco, Gianni and Chen, Hao and Poidinger, Michael and Chen,
              Jinmiao and de Magalh{\~a}es, Jo{\~a}o Pedro and Larbi, Anis",
  abstract = "MOTIVATION: Flow cytometry (FCM) is widely used in both clinical
              and basic research to characterize cell phenotypes and functions.
              The latest FCM instruments analyze up to 20 markers of individual
              cells, producing high-dimensional data. This requires the use of
              the latest clustering and dimensionality reduction techniques to
              automatically segregate cell sub-populations in an unbiased
              manner. However, automated analyses may lead to false discoveries
              due to inter-sample differences in quality and properties.
              RESULTS: We present an R package, flowAI, containing two methods
              to clean FCM files from unwanted events: (i) an automatic method
              that adopts algorithms for the detection of anomalies and (ii) an
              interactive method with a graphical user interface implemented
              into an R shiny application. The general approach behind the two
              methods consists of three key steps to check and remove suspected
              anomalies that derive from (i) abrupt changes in the flow rate,
              (ii) instability of signal acquisition and (iii) outliers in the
              lower limit and margin events in the upper limit of the dynamic
              range. For each file analyzed our software generates a summary of
              the quality assessment from the aforementioned steps. The
              software presented is an intuitive solution seeking to improve
              the results not only of manual but also and in particular of
              automatic analysis on FCM data. AVAILABILITY AND IMPLEMENTATION:
              R source code available through Bioconductor:
              http://bioconductor.org/packages/flowAI/ CONTACTS:
              mongianni1@gmail.com or Anis\_Larbi@immunol.a-star.edu.sg
              SUPPLEMENTARY INFORMATION: Supplementary data are available at
              Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  32,
  number   =  16,
  pages    = "2473--2480",
  month    =  aug,
  year     =  2016,
  language = "en"
}


@ARTICLE{Emmaneel2021-xy,
  title    = "{PeacoQC}: {Peak-Based} Selection of High Quality Cytometry Data",
  author   = "Emmaneel, Annelies and Quintelier, Katrien and Sichien, Dorine
              and Rybakowska, Paulina and Mara{\~n}{\'o}n, Concepci{\'o}n and
              Alarc{\'o}n-Riquelme, Marta E and Van Isterdael, Gert and Van
              Gassen, Sofie and Saeys, Yvan",
  abstract = "In cytometry analysis, a large number of markers is measured for
              thousands or millions of cells, resulting in high-dimensional
              datasets. During the measurement of these samples, erroneous
              events can occur such as clogs, speed changes, slow uptake of the
              sample etc. which can influence the downstream analysis and can
              even lead to false discoveries. As these issues can be difficult
              to detect manually, an automated approach is recommended. In
              order to filter these erroneous events out, we created a novel
              quality control algorithm, PeacoQC (Peak Extraction And Cleaning
              Oriented Quality Control), that allows for automated cleaning of
              cytometry data. The algorithm will determine density peaks per
              channel on which it will remove low quality events based on their
              position in the isolation tree and on their mean absolute
              deviation distance to these density peaks. To evaluate PeacoQC's
              cleaning capability, it was compared to three other existing
              quality control algorithms (flowAI, flowClean and flowCut) on a
              wide variety of datasets. In comparison to the other algorithms,
              PeacoQC was able to filter out all different types of anomalies
              in flow, mass and spectral cytometry data, while the other
              methods struggled with at least one type. In the quantitative
              comparison, PeacoQC obtained the highest median balanced accuracy
              and a similar running time compared to the other algorithms while
              having a better scalability for large files. To ensure that the
              parameters chosen in the PeacoQC algorithm are robust, the
              cleaning tool was run on 16 public datasets. After inspection,
              only one sample was found where the parameters should be further
              optimized. The other 15 datasets were analyzed correctly
              indicating a robust parameter choice. Overall, we present a fast
              and accurate quality control algorithm that outperforms existing
              tools and ensures high-quality data that can be used for further
              downstream analysis. An R implementation is available.",
  journal  = "Cytometry A",
  month    =  sep,
  year     =  2021,
  keywords = "Anomaly detection; Automated quality control; Data preprocessing;
              Flow cytometry; Mass cytometry; Spectral cytometry",
  language = "en"
}

@Manual{flowCore,
    title = {{flowCore}: Basic structures for flow cytometry data.},
    author = {Ellis B, Haaland P, Hahne F, Le Meur N, Gopalakrishnan N, Spidlen J, Jiang M, Finak G},
    year = {2022},
    note = {R package version 2.8.0},
    url = {https://bioconductor.org/packages/flowCore/},
  }



