@article{Gatto2012,
  author = {Gatto L, Lilley K},
  journal = {Bioinformatics},
  title = {{“MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation}},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr645},
  year = 2012,
  volume = 28,
  issue = 288-289,
}

@article{Gatto2020,
  author = {Gatto L, Gibb S, Rainer J},
  journal = {bioRxiv},
  title = {{MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data}},
  url = {https://doi.org/10.1101/2020.04.29.067868},
}

@article{Gandolfo2018,
    author = {Gandolfo, Luke C. AND Speed, Terence P.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {RLE plots: Visualizing unwanted variation in high dimensional data},
    year = {2018},
    month = {02},
    volume = {13},
    url = {https://doi.org/10.1371/journal.pone.0191629},
    pages = {1-9},
    abstract = {Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data. We provide a detailed examination of these plots, with the aid of examples and simulation, explaining what they are and what they can reveal. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalization procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in many other kinds of high dimensional data, where such variation can be problematic.},
    number = {2},
    doi = {10.1371/journal.pone.0191629}
}

@article{Ritchie2015,
    author = {Ritchie, Matthew E. and Phipson, Belinda and Wu, Di and Hu, Yifang and Law, Charity W. and Shi, Wei and Smyth, Gordon K.},
    title = "{limma powers differential expression analyses for RNA-sequencing and microarray studies}",
    journal = {Nucleic Acids Research},
    volume = {43},
    number = {7},
    pages = {e47-e47},
    year = {2015},
    month = {01},
    abstract = "{limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkv007},
    url = {https://doi.org/10.1093/nar/gkv007},
    eprint = {https://academic.oup.com/nar/article-pdf/43/7/e47/7207289/gkv007.pdf},
}


