@article{Hubert2008mc,
    title = {An adjusted boxplot for skewed distributions},
    journal = {Computational Statistics & Data Analysis},
    volume = {52},
    number = {12},
    pages = {5186-5201},
    year = {2008},
    issn = {0167-9473},
    doi = {https://doi.org/10.1016/j.csda.2007.11.008},
    url = {https://www.sciencedirect.com/science/article/pii/S0167947307004434},
    author = {M. Hubert and E. Vandervieren},
    abstract = {The boxplot is a very popular graphical tool for visualizing the distribution of continuous unimodal data. It shows information about the location, spread, skewness as well as the tails of the data. However, when the data are skewed, usually many points exceed the whiskers and are often erroneously declared as outliers. An adjustment of the boxplot is presented that includes a robust measure of skewness in the determination of the whiskers. This results in a more accurate representation of the data and of possible outliers. Consequently, this adjusted boxplot can also be used as a fast and automatic outlier detection tool without making any parametric assumption about the distribution of the bulk of the data. Several examples and simulation results show the advantages of this new procedure.}
}

@article{McCarthy2017,
    author = {McCarthy, Davis J and Campbell, Kieran R and Lun, Aaron T L and Wills, Quin F},
    title = "{Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R}",
    journal = {Bioinformatics},
    volume = {33},
    number = {8},
    pages = {1179-1186},
    year = {2017},
    month = {01},
    abstract = "{Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization.We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater.Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btw777},
    url = {https://doi.org/10.1093/bioinformatics/btw777},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/33/8/1179/49038647/bioinformatics\_33\_8\_1179.pdf},
}





