@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{Gentleman2004,
        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 = {http://dx.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{Pedrioli2004,
        pmid = {15529173},
        author = {Pedrioli, Patrick G A and Eng, Jimmy K and Hubley, Robert and Vogelzang, Mathijs and Deutsch, Eric W and Raught, Brian and Pratt, Brian and Nilsson, Erik and Angeletti, Ruth H and Apweiler, Rolf and Cheung, Kei and Costello, Catherine E and Hermjakob, Henning and Huang, Sequin and Julian, Randall K and Kapp, Eugene and McComb, Mark E and Oliver, Stephen G and Omenn, Gilbert and Paton, Norman W and Simpson, Richard and Smith, Richard and Taylor, Chris F and Zhu, Weimin and Aebersold, Ruedi},
        title = {A common open representation of mass spectrometry data and its application to proteomics research.},
        journal = {Nat. Biotechnol.},
        volume = {22},
        number = {11},
        pages = {1459-66},
        year = {2004},
        doi = {10.1038/nbt1031},
        tags = "bioinformatics, mass spectrometry, proteomics, standard"
}


@article{Martens2010,
        pmid = {20716697},
        author = {Martens, Lennart and Chambers, Matthew and Sturm, Marc and Kes
sner, Darren and Levander, Fredrik and Shofstahl, Jim and Tang, Wilfred H and Ro
mpp, Andreas and Neumann, Steffen and Pizarro, Angel D and Montecchi-Palazzi, Lu
isa and Tasman, Natalie and Coleman, Mike and Reisinger, Florian and Souda, Pune
et and Hermjakob, Henning and Binz, Pierre-Alain and Deutsch, Eric W},
        title = {mzML - a Community Standard for Mass Spectrometry Data.},
        journal = {Molecular \& Cellular Proteomics : MCP},
        year = {2010},
        doi = {10.1074/mcp.R110.000133},
        tags = "bioinformatics, mass spectrometry, proteomics, standard, formats"
}

@Article{Taylor2007,
        Abstract = {Both the generation and the analysis of proteomics data are now widespread, and high-throughput approaches are commonplace. Protocols continue to increase in complexity as methods and technologies evolve and diversify. To encourage the standardized collection, integration, storage and dissemination of proteomics data, the Human Proteome Organization’s Proteomics Standards Initiative develops guidance modules for reporting the use of techniques such as gel electrophoresis and mass spectrometry. This paper describes the processes and principles underpinning the development of these modules; discusses the ramifications for various interest groups such as experimentalists, funders, publishers and the private sector; addresses the issue of overlap with other reporting guidelines; and highlights the criticality of appropriate tools and resources in enabling ’MIAPE-compliant’ reporting.},
        Doi = {10.1038/nbt1329},
        File = {Taylor2007.pdf:Taylor2007.pdf:PDF},
        Institution = {The HUPO Proteomics Standards Initiative, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. chris.taylor@ebi.ac.uk},
        Keywords = {Databases, Protein; Gene Expression Profiling; Genome, Human; Guidelines as Topic; Humans; Information Storage and Retrieval; Internationality; Proteomics; Research},
        Month = {Aug},
        Owner = {lgatto},
        Pii = {nbt1329},
        Pmid = {17687369},
        Timestamp = {2010.02.03},
        Url = {http://dx.doi.org/10.1038/nbt1329},
        author = {Taylor, Chris F. and Paton, Norman W. and Lilley, Kathryn S. and Binz, Pierre-Alain and Julian, Randall K. and Jones, Andrew R. and Zhu, Weimin and Apweiler, Rolf and Aebersold, Ruedi and Deutsch, Eric W. and Dunn, Michael J. and Heck, Albert J. R. and Leitner, Alexander and Macht, Marcus and Mann, Matthias and Martens, Lennart and Neubert, Thomas A. and Patterson, Scott D. and Ping, Peipei and Seymour, Sean L. and Souda, Puneet and Tsugita, Akira and Vandekerckhove, Joel and Vondriska, Thomas M. and Whitelegge, Julian P. and Wilkins, Marc R. and Xenarios, Ioannnis and Yates, John R. and Hermjakob, Henning},
        title = {The minimum information about a proteomics experiment (MIAPE).},
        journal = {Nat Biotechnol},
        volume = {25},
        number = {8},
        pages = {887-893},
        year = {2007},
        tags = "reproducibility, quality, proteomics, data management, mass spectrometry, standard"
}

@Article{Ross2004,
        Abstract = {We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5’ to 3’ decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.},
        Doi = {10.1074/mcp.M400129-MCP200},
        File = {:home/lgatto/Biblio/Ross2004.pdf:PDF},
        Institution = {Applied Biosystems, Framingham, MA 01701, USA.},
        Keywords = {Cations; Chromatography, Ion Exchange; Chromatography, Liquid; Down-Regulation; Exoribonucleases; Fungal Proteins; Indicators and Reagents; Ions; Mass Spectrometry; Models, Chemical; Peptides; Phenotype; Proteomics; RNA Helicases; RNA, Messenger; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Succinimides},
        Month = {Dec},
        Owner = {lgatto},
        Pii = {M400129-MCP200},
        Pmid = {15385600},
        Timestamp = {2010.01.27},
        Url = {http://dx.doi.org/10.1074/mcp.M400129-MCP200},
        author = {Ross, Philip L. and Huang, Yulin N. and Marchese, Jason N. and Williamson, Brian and Parker, Kenneth and Hattan, Stephen and Khainovski, Nikita and Pillai, Sasi and Dey, Subhakar and Daniels, Scott and Purkayastha, Subhasish and Juhasz, Peter and Martin, Stephen and Bartlet-Jones, Michael and He, Feng and Jacobson, Allan and Pappin, Darryl J.},
        title = {Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.},
        journal = {Mol Cell Proteomics},
        volume = {3},
        number = {12},
        pages = {1154-1169},
        year = {2004},
        tags = "iTRAQ, labelled, mass spectrometry, proteomics"
}

@article{Thompson2003,
        pmid = {12713048},
        author = {Thompson, Andrew and Sch\"afer, J\"urgen and Kuhn, Karsten and Kienle, Stefan and Schwarz, Josef and Schmidt, G\"unter and Neumann, Thomas and Johnstone, R and Mohammed, A Karim A and Hamon, Christian},
        title = {Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by {MS/MS}.},
        journal = {Anal. Chem.},
        volume = {75},
        number = {8},
        pages = {1895-904},
        year = {2003},
        tags = "mass spectrometry, labelled, quantitative, TMT"
}

@article{Smith2006,
   author="Smith, C.A. and Want, E.J. and O'Maille, G. and Abagyan,R. and Siuzdak, G.",
   title="XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification",
   journal="Analytical Chemistry",
   year="2006",
   volume="78",
   pages="779-787"
}

@Article{Huber2002,
    title = {Variance Stabilization Applied to Microarray Data Calibration and to the Quantification of Differential Expression},
    author = {Wolfgang Huber and Anja von Heydebreck and Holger Sueltmann and Annemarie Poustka and Martin Vingron},
    journal = {Bioinformatics},
    year = {2002},
    volume = {18 Suppl. 1},
    pages = {S96-S104},
}


@article{Karp2010,
        pmid = {20382981},
        author = {Karp, Natasha A and Huber, Wolfgang and Sadowski, Pawel G and Charles, Philip D and Hester, Svenja V and Lilley, Kathryn S},
        title = {Addressing accuracy and precision issues in iTRAQ quantitation.},
        journal = {Mol. Cell Proteomics},
        volume = {9},
        number = {9},
        pages = {1885-97},
        year = {2010},
        doi = {10.1074/mcp.M900628-MCP200},
        tags = "Bioconductor, bioinformatics, iTRAQ, labelled, mass spectrometry, normalisation/preprocessing, proteomics, R, quantitative, statistics"
}

@article{Taylor2008,
        pmid = {18688232},
        author = {Taylor, Chris F and Binz, Pierre-Alain and Aebersold, Ruedi and Affolter, Michel and Barkovich, Robert and Deutsch, Eric W and Horn, David M and Hühmer, Andreas and Kussmann, Martin and Lilley, Kathryn and Macht, Marcus and Mann, Matthias and Müller, Dieter and Neubert, Thomas A and Nickson, Janice and Patterson, Scott D and Raso, Roberto and Resing, Kathryn and Seymour, Sean L and Tsugita, Akira and Xenarios, Ioannis and Zeng, Rong and Julian, Randall K},
        title = {Guidelines for reporting the use of mass spectrometry in proteomics.},
        journal = {Nat. Biotechnol.},
        volume = {26},
        number = {8},
        pages = {860-1},
        year = {2008},
        doi = {10.1038/nbt0808-860},
        tags = "data management, mass spectrometry, proteomics, quality, reproducibility, standard"
}

@article{Shadforth05,
        pmid = {16242023},
        author = {Shadforth, Ian P and Dunkley, Tom P J and Lilley, Kathryn S and Bessant, Conrad},
        title = {i-Tracker: for quantitative proteomics using iTRAQ.},
        journal = {BMC Genomics},
        volume = {6},
        pages = {145},
        year = {2005},
        doi = {10.1186/1471-2164-6-145},
        tags = "iTRAQ, proteomics, mass spectrometry, quantitative, software, statistics, labelled, bioinformatics"
}

@article{Bolstad03,
        pmid = {12538238},
        author = {Bolstad, B M and Irizarry, R A and Astrand, M and Speed, T P},
        title = {A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.},
        journal = {Bioinformatics},
        volume = {19},
        number = {2},
        pages = {185-93},
        year = {2003},
        tags = "Bioconductor, bioinformatics, normalisation/preprocessing, microarray"
}

@Book{ggplot2,
    author = {Hadley Wickham},
    title = {ggplot2: elegant graphics for data analysis},
    publisher = {Springer New York},
    year = {2009},
    isbn = {978-0-387-98140-6},
    url = {http://had.co.nz/ggplot2/book},
  }

@article{Foster11,
        pmid = {21538885},
        author = {Foster, Joseph M and Degroeve, Sven and Gatto, Laurent and Visser, Matthieu and Wang, Rui and Griss, Johannes and Apweiler, Rolf and Martens, Lennart},
        title = {A posteriori quality control for the curation and reuse of public proteomics data.},
        journal = {Proteomics},
        volume = {11},
        number = {11},
        pages = {2182-94},
        year = {2011},
        doi = {10.1002/pmic.201000602},
        tags = "bioinformatics, mass spectrometry, proteomics, quality"
}

@article{Gatto2010,
        pmid = {21046620},
        author = {Gatto, Laurent and Vizca\'ino, Juan Antonio and Hermjakob, Henning and Huber, Wolfgang and Lilley, Kathryn S},
        title = {Organelle proteomics experimental designs and analysis.},
        journal = {Proteomics},
        year = {2010},
        doi = {10.1002/pmic.201000244},
        tags = "experimental design, mass spectrometry, organelle, proteomics, review"
}

@Article{Dunkley2006,
        Abstract = {A challenging task in the study of the secretory pathway is the identification and localization of new proteins to increase our understanding of the functions of different organelles. Previous proteomic studies of the endomembrane system have been hindered by contaminating proteins, making it impossible to assign proteins to organelles. Here we have used the localization of organelle proteins by the isotope tagging technique in conjunction with isotope tags for relative and absolute quantitation and 2D liquid chromatography for the simultaneous assignment of proteins to multiple subcellular compartments. With this approach, the density gradient distributions of 689 proteins from Arabidopsis thaliana were determined, enabling confident and simultaneous localization of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar membrane, plasma membrane, or mitochondria and plastids. This parallel analysis of endomembrane components has enabled protein steady-state distributions to be determined. Consequently, genuine organelle residents have been distinguished from contaminating proteins and proteins in transit through the secretory pathway.},
        Doi = {10.1073/pnas.0506958103},
        File = {:Dunkley2006.pdf:PDF},
        Institution = {Department of Biochemistry, University of Cambridge, Downing Site, Cambridge CB2 1QW, United Kingdom.},
        Keywords = {Arabidopsis Proteins, genetics/metabolism; Arabidopsis, genetics/metabolism; Organelles, genetics/metabolism; Peptide Mapping; Proteome, genetics/metabolism; Recombinant Fusion Proteins, genetics/metabolism; Subcellular Fractions, metabolism},
        Language = {eng},
        Medline-pst = {ppublish},
        Month = {Apr},
        Owner = {lgatto},
        Pii = {0506958103},
        Pmid = {16618929},
        Timestamp = {2010.01.08},
        Url = {http://dx.doi.org/10.1073/pnas.0506958103},
        author = {Dunkley, Tom P. J. and Hester, Svenja and Shadforth, Ian P. and Runions, John and Weimar, Thilo and Hanton, Sally L. and Griffin, Julian L. and Bessant, Conrad and Brandizzi, Federica and Hawes, Chris and Watson, Rod B. and Dupree, Paul and Lilley, Kathryn S.},
        title = {Mapping the Arabidopsis organelle proteome.},
        journal = {Proc Natl Acad Sci USA},
        volume = {103},
        number = {17},
        pages = {6518-6523},
        year = {2006},
        tags = "Arabidopsis, LOPIT, labelled, mass spectrometry, proteomics, quantitative, iTRAQ"
}

@Article{Foster2006,
        Abstract = {Protein localization to membrane-enclosed organelles is a central feature of cellular organization. Using protein correlation profiling, we have mapped 1,404 proteins to ten subcellular locations in mouse liver, and these correspond with enzymatic assays, marker protein profiles, and confocal microscopy. These localizations allowed assessment of the specificity in published organellar proteomic inventories and demonstrate multiple locations for 39% of all organellar proteins. Integration of proteomic and genomic data enabled us to identify networks of coexpressed genes, cis-regulatory motifs, and putative transcriptional regulators involved in organelle biogenesis. Our analysis ties biochemistry, cell biology, and genomics into a common framework for organelle analysis.},
        Doi = {10.1016/j.cell.2006.03.022},
        File = {:Foster2006.pdf:PDF},
        Institution = {Center for Experimental BioInformatics (CEBI), Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark.},
        Keywords = {Animals; Centrifugation, Density Gradient; Cluster Analysis; Databases, Protein; Fluorescent Antibody Technique; Gene Expression Profiling; Genomics; Liver, cytology; Mice; Mice, Inbred C57BL; Organelles, genetics/metabolism; Promoter Regions, Genetic, genetics; Protein Transport; Proteins, genetics/metabolism; Proteomics; Transcription Factors, metabolism},
        Language = {eng},
        Medline-pst = {ppublish},
        Month = {Apr},
        Owner = {lgatto},
        Pii = {S0092-8674(06)00369-2},
        Pmid = {16615899},
        Timestamp = {2010.01.08},
        Url = {http://dx.doi.org/10.1016/j.cell.2006.03.022},
        author = {Foster, Leonard J. and Hoog, Carmen L. de and Zhang, Yanling and Zhang, Yong and Xie, Xiaohui and Mootha, Vamsi K. and Mann, Matthias},
        title = {A mammalian organelle map by protein correlation profiling.},
        journal = {Cell},
        volume = {125},
        number = {1},
        pages = {187-199},
        year = {2006},
        tags = "PCP, label-free, mass spectrometry, organelle, proteomics, Mouse"
}

@Article{Andersen2003,
        Abstract = {The centrosome is the major microtubule-organizing centre of animal cells and through its influence on the cytoskeleton is involved in cell shape, polarity and motility. It also has a crucial function in cell division because it determines the poles of the mitotic spindle that segregates duplicated chromosomes between dividing cells. Despite the importance of this organelle to cell biology and more than 100 years of study, many aspects of its function remain enigmatic and its structure and composition are still largely unknown. We performed a mass-spectrometry-based proteomic analysis of human centrosomes in the interphase of the cell cycle by quantitatively profiling hundreds of proteins across several centrifugation fractions. True centrosomal proteins were revealed by both correlation with already known centrosomal proteins and in vivo localization. We identified and validated 23 novel components and identified 41 likely candidates as well as the vast majority of the known centrosomal proteins in a large background of nonspecific proteins. Protein correlation profiling permits the analysis of any multiprotein complex that can be enriched by fractionation but not purified to homogeneity.},
        Doi = {10.1038/nature02166},
        File = {:Andersen2003.pdf:PDF},
        Institution = {Center for Experimental BioInformatics, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark.},
        Keywords = {Amino Acid Sequence; Cell Line; Centrosome, chemistry/metabolism; Cloning, Molecular; Gene Expression Profiling; Humans; Interphase; Mass Spectrometry; Molecular Sequence Data; Proteins, chemistry/genetics/isolation /&/ purification/metabolism; Proteomics; Substrate Specificity},
        Language = {eng},
        Medline-pst = {ppublish},
        Month = {Dec},
        Owner = {lgatto},
        Pii = {nature02166},
        Pmid = {14654843},
        Timestamp = {2010.01.08},
        Url = {http://dx.doi.org/10.1038/nature02166},
        author = {Andersen, Jens S. and Wilkinson, Christopher J. and Mayor, Thibault and Mortensen, Peter and Nigg, Erich A. and Mann, Matthias},
        title = {Proteomic characterization of the human centrosome by protein correlation profiling.},
        journal = {Nature},
        volume = {426},
        number = {6966},
        pages = {570-574},
        year = {2003},
        tags = "PCP, label-free, mass spectrometry, proteomics, quantitative, organelle, GFP, tagging, subcellular"
}

@Article{Wiese2007,
        Abstract = {The peroxisome represents a ubiquitous single membrane-bound key organelle that executes various metabolic pathways such as fatty acid degradation by alpha- and beta-oxidation, ether-phospholipid biosynthesis, metabolism of reactive oxygen species, and detoxification of glyoxylate in mammals. To fulfil this vast array of metabolic functions, peroxisomes accommodate approximately 50 different enzymes at least as identified until now. Interest in peroxisomes has been fueled by the discovery of a group of genetic diseases in humans, which are caused by either a defect in peroxisome biogenesis or the deficient activity of a distinct peroxisomal enzyme or transporter. Although this research has greatly improved our understanding of peroxisomes and their role in mammalian metabolism, deeper insight into biochemistry and functions of peroxisomes is required to expand our knowledge of this low abundance but vital organelle. In this work, we used classical subcellular fractionation in combination with MS-based proteomics methodologies to characterize the proteome of mouse kidney peroxisomes. We could identify virtually all known components involved in peroxisomal metabolism and biogenesis. Moreover through protein localization studies by using a quantitative MS screen combined with statistical analyses, we identified 15 new peroxisomal candidates. Of these, we further investigated five candidates by immunocytochemistry, which confirmed their localization in peroxisomes. As a result of this joint effort, we believe to have compiled the so far most comprehensive protein catalogue of mammalian peroxisomes.},
        Doi = {10.1074/mcp.M700169-MCP200},
        File = {Wiese2007.pdf:Wiese2007.pdf:PDF},
        Institution = {Medizinisches Proteom-Center, Ruhr-Universitaet Bochum, Universitaetsstrasse 150, 44780 Bochum, Germany.},
        Keywords = {Animals; Chromatography, High Pressure Liquid; Kidney; Mice; Peroxisomes; Proteomics; Spectrometry, Mass, Electrospray Ionization; Tandem Mass Spectrometry},
        Month = {Dec},
        Owner = {lgatto},
        Pii = {M700169-MCP200},
        Pmid = {17768142},
        Timestamp = {2010.01.28},
        Url = {http://dx.doi.org/10.1074/mcp.M700169-MCP200},
        author = {Wiese, Sebastian and Gronemeyer, Thomas and Ofman, Rob and Kunze, Markus and Grou, Cláudia P. and Almeida, José A. and Eisenacher, Martin and Stephan, Christian and Hayen, Heiko and Schollenberger, Lukas and Korosec, Thomas and Waterham, Hans R. and Schliebs, Wolfgang and Erdmann, Ralf and Berger, Johannes and Meyer, Helmut E. and Just, Wilhelm and Azevedo, Jorge E. and Wanders, Ronald J. A. and Warscheid, Bettina},
        title = {Proteomics characterization of mouse kidney peroxisomes by tandem mass spectrometry and protein correlation profiling.},
        journal = {Mol Cell Proteomics},
        volume = {6},
        number = {12},
        pages = {2045-2057},
        year = {2007},
        tags = "PCP, quantitative, mass spectrometry, label-free, organelle, subcellular, Mouse, peroxisome"
}

@article{Harner2011,
        author = {Harner, Max and K\"orner, Christian and Walther, Dirk and Mokranjac, Dejana and Kaesmacher, Johannes and Welsch, Ulrich and Griffith, Janice and Mann, Matthias and Reggiori, Fulvio and Neupert, Walter},
        title = {The mitochondrial contact site complex, a determinant of mitochondrial architecture},
        journal = {EMBO J},
        volume = {30},
        number = {21},
        pages = {4356 - 4370},
        year = {2011},
        doi = {10.1038/emboj.2011.379},
        tags = "organelle, SILAC, proteomics"
}
@Article{Harner:2011,
  author =       {Harner, M and Körner, C and Walther, D and
                 Mokranjac, D and Kaesmacher, J and Welsch, U and
                 Griffith, J and Mann, M and Reggiori, F and
                 Neupert, W},
  title =        {The mitochondrial contact site complex, a
                 determinant of mitochondrial architecture.},
  journal =      {EMBO J},
  year =         {2011},
  month =        {Nov},
  number =       {21},
  volume =       {30},
  pages =        {4356-70},
  doi =          {10.1038/emboj.2011.379},
  PMID =         {22009199}}

@article{Chi2012,
        author = {Chi, S M and Nam, D},
        title = {WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.
},
        journal = {Bioinformatics},
        year = {2012},
        month = {Jan},
        number = {},
        volume = {},
        pages = {},
        doi = {10.1093/bioinformatics/bts062},
        pmid = {22296788},
        abstract = {Summary: We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. Availability: WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc},
        file = {:/home/lgatto/Biblio/Chi2012.pdf:PDF}
}
@Article{Gatto2012,
  author =       {Gatto, Laurent and Lilley, Kathryn S},
  title =        {{MS}nbase -- an {R}/{B}ioconductor package for isobaric
                 tagged mass spectrometry data visualization,
                 processing and quantitation.},
  journal =      {Bioinformatics},
  year =         {2012},
  month =        {Jan},
  number =       {2},
  volume =       {28},
  pages =        {288-9},
  doi =          {10.1093/bioinformatics/btr645},
  PMID =         {22113085}}

@Article{Ferro2010,
  author =       {Ferro, Myriam and Brugi\`ere, Sabine and Salvi, Danial and
                 Seigneurin-Berny, Daphn{\'e} and Court, Magali and Moyet, Lucas and
                 Ramus, Claire and Miras, St{\'e}phane and Mellal, Mourad and Le Gall, Sophie
                 and Kieffer-Jaquinod, Sylvie and Bruley, Christophe and Garin, J{\'e}r{\^o}me
                 and Joyard, Jacques and Masselon, Christophe and Rolland, Norbert},
  title =        {AT\_CHLORO, a comprehensive chloroplast proteome
                 database with subplastidial localization and
                 curated information on envelope proteins.},
  journal =      {Mol Cell Proteomics},
  year =         {2010},
  month =        {Jun},
  number =       {6},
  volume =       {9},
  pages =        {1063-84},
  doi =          {10.1074/mcp.M900325-MCP200},
  PMID =         {20061580}}

@Article{Tan2009,
  author =       {Tan, Denise J and Dvinge, Heidi and Christoforou, Andy and
                 Bertone, Paul and Martinez , Alfonso A and Lilley, Kathryn S},
  title =        {Mapping organelle proteins and protein complexes
                 in Drosophila melanogaster.},
  journal =      {J Proteome Res},
  year =         {2009},
  month =        {Jun},
  number =       {6},
  volume =       {8},
  pages =        {2667-78},
  doi =          {10.1021/pr800866n},
  PMID =         {19317464}}

@Article{Troyanskaya2001,
  author =       {Troyanskaya, O and Cantor, M and Sherlock, G and
                 Brown, P and Hastie, T and Tibshirani, R and
                 Botstein, D and Altman, R B},
  title =        {Missing value estimation methods for DNA
                 microarrays.},
  journal =      {Bioinformatics},
  year =         {2001},
  month =        {Jun},
  number =       {6},
  volume =       {17},
  pages =        {520-5},
  PMID =         {11395428},
  file = {:/home/lgatto/Biblio/Troyanskaya2001.pdf:PDF}}


@inproceedings{perturbo,
  author = {Courty, Nicolas and Burger, Thomas and Laurent, Johann},
  editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},
  booktitle = {in the proceedings of ECML/PKDD (1)},
  pages = {359-374},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {PerTurbo: A New Classification Algorithm Based on the Spectrum Perturbations of the Laplace-Beltrami Operator.},
  volume = 6911,
  year = 2011,
}

@inproceedings{burger2011icdar,
  author    = {Burger, Thomas and Kessentini, Yousri and Paquet, Thierry},
  title     = {Dempster-Shafer Based Rejection Strategy for Handwritten
               Word Recognition},
  booktitle = {Proceedings of the International Conference on Document Analysis and Recognition,
               (ICDAR), Beijing, China, September 18-21, 2011},
  year      = {2011},
  pages     = {528-532},
}

@Article{Breckels2013,
  author =       {Breckels, Lisa M and Gatto, Laurent and Christoforou, Andy and
                 Groen, Arnoud J and Lilley, Kathryn S and Trotter, Matthew W B},
  title =        {The effect of organelle discovery upon sub-cellular protein localisation.},
  journal =      {J Proteomics},
  year =         {2013},
  month =        {Mar},
  number =       {},
  volume =       {},
  pages =        {},
  doi =          {10.1016/j.jprot.2013.02.019},
  PMID =         {23523639}
}

@Manual{MLInterfaces,
    title = {{MLInterfaces}: Uniform interfaces to R machine learning procedures for data in Bioconductor containers},
    author = {Vince Carey and Robert Gentleman and Jess Mar and and contributions from Jason Vertrees and Laurent Gatto},
    note = {R package version 1.41.1}
  }


@Article{Ohta2010,
  author =       {Ohta, S and Bukowski-Wills, J C and
                 Sanchez-Pulido, L and Alves, Fde L and Wood, L and
                 Chen, Z A and Platani, M and Fischer, L and
                 Hudson, D F and Ponting, C P and Fukagawa, T and
                 Earnshaw, W C and Rappsilber, J},
  title =        {The protein composition of mitotic chromosomes
                 determined using multiclassifier combinatorial
                 proteomics.},
  journal =      {Cell},
  year =         {2010},
  month =        {Sep},
  number =       {5},
  volume =       {142},
  pages =        {810-21},
  doi =          {10.1016/j.cell.2010.07.047},
  PMID =         {20813266},
  file = {:/home/lgatto/Biblio/Ohta2010.pdf:PDF}}

@article{Trotter2010,
   author = {Trotter, Matthew W. B. and Sadowski, Pawel G. and Dunkley, Tom P. J. and Groen, Arnoud J. and Lilley, Kathryn S.},
   title = {Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions},
   journal = {PROTEOMICS},
   volume = {10},
   number = {23},
   publisher = {WILEY-VCH Verlag},
   issn = {1615-9861},
   url = {http://dx.doi.org/10.1002/pmic.201000359},
   doi = {10.1002/pmic.201000359},
   pages = {4213--4219},
   keywords = {Bioinformatics, Organelle proteomics, Protein localisation, Statistical models, Support vector machines},
   year = {2010},
}

@Article{Tardif2012,
  author =       {Tardif, M and Atteia, A and Specht, M and Cogne, G
                 and Rolland, N and Brugière, S and Hippler, M and
                 Ferro, M and Bruley, C and Peltier, G and Vallon,
                 O and Cournac, L},
  title =        {PredAlgo: a new subcellular localization
                 prediction tool dedicated to green algae.},
  journal =      {Mol Biol Evol},
  year =         {2012},
  month =        {Dec},
  number =       {12},
  volume =       {29},
  pages =        {3625-39},
  doi =          {10.1093/molbev/mss178},
  PMID =         {22826458}}

@Article{Nikolovski2012,
  author =       {Nikolovski, N and Rubtsov, D and Segura, M P and
                 Miles, G P and Stevens, T J and Dunkley, T P and
                 Munro, S and Lilley, K S and Dupree, P},
  title =        {Putative glycosyltransferases and other plant
                 golgi apparatus proteins are revealed by {LOPIT}
                 proteomics.},
  journal =      {Plant Physiol},
  year =         {2012},
  month =        {Oct},
  number =       {2},
  volume =       {160},
  pages =        {1037-51},
  doi =          {10.1104/pp.112.204263},
  PMID =         {22923678}}

@book{ISLwR,
    title = "An Introduction to Statistical Learning: with Applications in R",
    author = "James, G. and Witten, D. and Hastie, T. and Tibshirani, R.",
    isbn = "9781461471387",
    series = "Springer Texts in Statistics",
    year = "2013",
    publisher = "Springer"
}

@Manual{shiny,
    title = {shiny: Web Application Framework for R},
    author = {{RStudio} and {Inc.}},
    year = {2014},
    note = {R package version 0.10.1},
    url = {http://CRAN.R-project.org/package=shiny},
  }

@article{Uhlen:2010,
    author = {Uhlen, Mathias and Oksvold, Per and Fagerberg, Linn and
                  Lundberg, Emma and Jonasson, Kalle and Forsberg,
                  Mattias and Zwahlen, Martin and Kampf, Caroline and
                  Wester, Kenneth and Hober, Sophia and Wernerus,
                  Henrik and Bj\"{o}rling, Lisa and Ponten, Fredrik},
    comment = {Update of Protein Atlas. About 50\% of proteins are
                  claimed to be assessed by immunostaining in various
                  human tissues and neoplasms},
    doi = {10.1038/nbt1210-1248},
    issn = {1546-1696},
    journal = {Nature biotechnology},
    keywords = {proteomics, tissue},
    month = dec,
    number = {12},
    pages = {1248--1250},
    pmid = {21139605},
    posted-at = {2010-12-08 22:24:39},
    priority = {0},
    publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
    title = {{Towards a knowledge-based Human Protein Atlas.}},
    url = {http://dx.doi.org/10.1038/nbt1210-1248},
    volume = {28},
    year = {2010}
}


@Article{Ashburner:2000,
  author =       {Ashburner, M and Ball, C A and Blake, J A and
                 Botstein, D and Butler, H and Cherry, J M and
                 Davis, A P and Dolinski, K and Dwight, S S and
                 Eppig, J T and Harris, M A and Hill, D P and
                 Issel-Tarver, L and Kasarskis, A and Lewis, S and
                 Matese, J C and Richardson, J E and Ringwald, M
                 and Rubin, G M and Sherlock, G},
  title =        {Gene ontology: tool for the unification of
                 biology. The Gene Ontology Consortium.},
  journal =      {Nat Genet},
  year =         {2000},
  month =        {May},
  number =       {1},
  volume =       {25},
  pages =        {25-9},
  doi =          {10.1038/75556},
  PMID =         {10802651}}

@Article{Gatto:2014,
  author =       {Gatto, L and Breckels, L M and Burger, T and
                 Nightingale, D J and Groen, A J and Campbell, C
                 and Nikolovski, N and Mulvey, C M and
                 Christoforou, A and Ferro, M and Lilley, K S},
  title =        {A foundation for reliable spatial proteomics data
                 analysis.},
  journal =      {Mol Cell Proteomics},
  year =         {2014},
  month =        {Aug},
  number =       {8},
  volume =       {13},
  pages =        {1937-52},
  doi =          {10.1074/mcp.M113.036350},
  PMID =         {24846987}}

@Article{Breckels:2015,
      author = {Breckels, L.M. and Holden, S. and Wonjar, D. and
                  Mulvey, C.M and Christoforou, A. and Groen, A. and
                  Kohlbacker, O. and Lilley, K.S. and Gatto, L.},
      title = {Learning from heterogeneous data sources: an
                  application in spatial proteomics},
      journal = {bioRxiv},
      url = {http://biorxiv.org/content/early/2015/07/07/022152},
      doi = {10.1101/022152},
      publisher = {Cold Spring Harbor Labs Journals},
      year = {2015}}


@Article{Breckels:2016,
  author =       {Breckels, L M and Holden, S B and Wojnar, D and
                 Mulvey, C M and Christoforou, A and Groen, A and
                 Trotter, M W and Kohlbacher, O and Lilley, K S and
                 Gatto, L},
  title =        {Learning from Heterogeneous Data Sources: An
                 Application in Spatial Proteomics.},
  journal =      {PLoS Comput Biol},
  year =         {2016},
  month =        {May},
  number =       {5},
  volume =       {12},
  pages =        {e1004920},
  doi =          {10.1371/journal.pcbi.1004920},
  PMID =         {27175778}}


@Article{Christoforou:2016,
  author =       {Christoforou, A and Mulvey, C M and Breckels, L M
                 and Geladaki, A and Hurrell, T and Hayward, P C
                 and Naake, T and Gatto, L and Viner, R and
                 Martinez Arias, A and Lilley, K S},
  title =        {A draft map of the mouse pluripotent stem cell
                 spatial proteome.},
  journal =      {Nat Commun},
  year =         {2016},
  month =        {},
  number =       {},
  volume =       {7},
  pages =        {8992},
  doi =          {10.1038/ncomms9992},
  PMID =         {26754106}}

@article{tsne:2008,
    abstract = {{We present a new technique called "t-SNE" that
                  visualizes high-dimensional data by giving each
                  datapoint a location in a two or three-dimensional
                  map. The technique is a variation of Stochastic
                  Neighbor Embedding (Hinton and Roweis, 2002) that is
                  much easier to optimize, and produces significantly
                  better visualizations by reducing the tendency to
                  crowd points together in the center of the
                  map. t-SNE is better than existing techniques at
                  creating a single map that reveals structure at many
                  different scales. This is particularly important for
                  high-dimensional data that lie on several different,
                  but related, low-dimensional manifolds, such as
                  images ofobjects from multiple classes seen from
                  multiple viewpoints. For visualizing the structure
                  of very large data sets, we show how t-SNE can use
                  random walks on neighborhood graphs to allow the
                  implicit structure of all of the data to influence
                  the way in which a subset of the data is
                  displayed. We illustrate the performance of t-SNE on
                  a wide variety of data sets and compare it with many
                  other non-parametric visualization techniques,
                  including Sammon mapping, Isomap, and Locally Linear
                  Embedding. The visualizations produced by t-SNE are
                  significantly better than those produced by the
                  other techniques on almost all of the data sets.}},
    author = {van der Maaten, Laurens and Hinton, Geoffrey},
    citeulike-article-id = {3749741},
    citeulike-linkout-0 = {http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf},
    journal = {Journal of Machine Learning Research},
    keywords = {multidimensional, vizualization},
    month = nov,
    pages = {2579--2605},
    posted-at = {2009-01-14 18:44:52},
    priority = {2},
    title = {{Visualizing Data using t-SNE}},
    url = {http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf},
    volume = {9},
    year = {2008}
}


@Article{Breckels:2016b,
  author =	 {Breckels, LM and Mulvey, CM and Lilley, KS and Gatto L},
  title =	 {A Bioconductor workflow for processing and analysing
                  spatial proteomics data},
  journal =	 {F1000Research},
  year =	 {2016},
  doi  =         {10.12688/f1000research.10411.1}
}

@article{Crook:2018,
        author = {Crook, Oliver M and Mulvey, Claire M and Kirk, Paul
                  D. W. and Lilley, Kathryn S and Gatto, Laurent},
        title = {A Bayesian Mixture Modelling Approach For Spatial Proteomics},
        year = {2018},
        doi = {10.1101/282269},
        publisher = {Cold Spring Harbor Laboratory},
        abstract = {Analysis of the spatial sub-cellular distribution
                  of proteins is of vital importance to fully
                  understand context specific protein function. Some
                  proteins can be found with a single location within
                  a cell, but up to half of proteins may reside in
                  multiple locations, can dynamically relocalise, or
                  reside within an unknown functional
                  compartment. These considerations lead to
                  uncertainty in associating a protein to a single
                  location. Currently, mass spectrometry (MS) based
                  spatial proteomics relies on supervised machine
                  learning algorithms to assign proteins to
                  sub-cellular locations based on common gradient
                  profiles. However, such methods fail to quantify
                  uncertainty. Here we reformulate the framework on
                  which we perform statistical analysis. We propose a
                  Bayesian generative classifier based on Gaussian
                  mixture models to assign proteins probabilistically
                  to sub-cellular niches, thus proteins have a
                  probability distribution over sub-cellular
                  locations, with posterior Bayesian computation
                  performed using the expectation-maximisation (EM)
                  algorithm, as well as Markov-chain Monte-Carlo
                  (MCMC). Our methodology allows proteome-wide
                  uncertainty quantification, thus adding a further
                  layer to the analysis of spatial proteomics. Our
                  framework is flexible, allowing many different
                  systems to be analysed and reveals new modelling
                  opportunities for spatial proteomics. We find our
                  methods perform competitively with the current
                  state-of-the art machine learning methods, whilst
                  simultaneously providing more information. We
                  highlight several examples where uncertainty
                  quantification provides biologically intriguing
                  results. To our knowledge this is the first Bayesian
                  model of MS based spatial proteomics data.},
        URL = {https://www.biorxiv.org/content/early/2018/03/14/282269},
        eprint = {https://www.biorxiv.org/content/early/2018/03/14/282269.full.pdf},
        journal = {bioRxiv}
}


@article{Brooks:1998,
author = {Brooks, Stephen P.and Gelman, Andrew},
title = {General Methods for Monitoring Convergence of Iterative
                  Simulations},
journal = {Journal of Computational and Graphical Statistics},
volume = {7},
number = {4},
pages = {434-455},
year  = {1998},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.1998.10474787},
URL = {https://www.tandfonline.com/doi/abs/10.1080/10618600.1998.10474787},
eprint = {https://www.tandfonline.com/doi/pdf/10.1080/10618600.1998.10474787},
abstract = { Abstract We generalize the method proposed by Gelman and
                  Rubin (1992a) for monitoring the convergence of
                  iterative simulations by comparing between and
                  within variances of multiple chains, in order to
                  obtain a family of tests for convergence. We review
                  methods of inference from simulations in order to
                  develop convergence-monitoring summaries that are
                  relevant for the purposes for which the simulations
                  are used. We recommend applying a battery of tests
                  for mixing based on the comparison of inferences
                  from individual sequences and from the mixture of
                  sequences. Finally, we discuss multivariate
                  analogues, for assessing convergence of several
                  parameters simultaneously. }
}

@article{Gelman:1992,
 ISSN = {08834237},
 URL = {http://www.jstor.org/stable/2246093},
 abstract = {The Gibbs sampler, the algorithm of Metropolis and
                  similar iterative simulation methods are potentially
                  very helpful for summarizing multivariate
                  distributions. Used naively, however, iterative
                  simulation can give misleading answers. Our methods
                  are simple and generally applicable to the output of
                  any iterative simulation; they are designed for
                  researchers primarily interested in the science
                  underlying the data and models they are analyzing,
                  rather than for researchers interested in the
                  probability theory underlying the iterative
                  simulations themselves. Our recommended strategy is
                  to use several independent sequences, with starting
                  points sampled from an overdispersed
                  distribution. At each step of the iterative
                  simulation, we obtain, for each univariate estimand
                  of interest, a distributional estimate and an
                  estimate of how much sharper the distributional
                  estimate might become if the simulations were
                  continued indefinitely. Because our focus is on
                  applied inference for Bayesian posterior
                  distributions in real problems, which often tend
                  toward normality after transformations and
                  marginalization, we derive our results as
                  normal-theory approximations to exact Bayesian
                  inference, conditional on the observed
                  simulations. The methods are illustrated on a
                  random-effects mixture model applied to experimental
                  measurements of reaction times of normal and
                  schizophrenic patients.},
 author = {Gelman, Andrew and Rubin, Donald B.},
 journal = {Statistical Science},
 number = {4},
 pages = {457--472},
 publisher = {Institute of Mathematical Statistics},
 title = {Inference from Iterative Simulation Using Multiple Sequences},
 volume = {7},
 year = {1992}
}

@Article{code,
    title = {{CODA}: Convergence Diagnosis and Output Analysis for {MCMC}},
    author = {Plummer, Martyn and Best, Nicky and Cowles, Kate and
                  Vines, Karen },
    journal = {R News},
    year = {2006},
    volume = {6},
    number = {1},
    pages = {7--11},
    url = {https://journal.r-project.org/archive/},
    pdf = {https://www.r-project.org/doc/Rnews/Rnews_2006-1.pdf},
  }

@article{Gatto:2018,
        author = {Gatto, Laurent and Breckels, Lisa M and Lilley, Kathryn S},
        title = {Assessing sub-cellular resolution in spatial proteomics experiments},
        year = {2018},
        doi = {10.1101/377630},
        publisher = {Cold Spring Harbor Laboratory},
        abstract = {The sub-cellular localisation of a protein is
                  vital in defining its function, and a protein's
                  mis-localisation is known to lead to adverse
                  effect. As a result, numerous experimental
                  techniques and datasets have been published, with
                  the aim of deciphering the localisation of proteins
                  at various scales and resolutions, including high
                  profile mass spectrometry-based efforts. Here, we
                  present a meta-analysis assessing and comparing the
                  sub-cellular resolution of 29 such mass
                  spectrometry-based spatial proteomics experiments
                  using a newly developed tool termed $QSep$. Our goal
                  is to provide a simple quantitative report of how
                  well spatial proteomics resolve the sub-cellular
                  niches they describe to inform and guide developers
                  and users of such methods.},
        journal = {bioRxiv}
}