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Laurent Gatto
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- affiliation: UCLouvain, Brussels, Belgium
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2020 – today
- 2024
- [j11]Philippe Hauchamps, Babak Bayat, Simon Delandre, Mehdi Hamrouni, Marie Toussaint, Stephane Temmerman, Dan Lin, Laurent Gatto:
CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data. BMC Bioinform. 25(1): 80 (2024) - [i4]Jenny Drnevich, Frederick J. Tan, Fabricio Almeida-Silva, Robert Castelo, Aedín C. Culhane, Sean Davis, Maria A. Doyle, Susan Holmes, Leo Lahti, Alexandru Mahmoud, Kozo Nishida, Marcel Ramos, Kevin C. Rue-Albrecht, David J. H. Shih, Laurent Gatto, Charlotte Soneson:
Learning and teaching biological data science in the Bioconductor community. CoRR abs/2410.01351 (2024) - 2023
- [j10]Kelly B. Eckenrode
, Dario Righelli
Curated single cell multimodal landmark datasets for R/Bioconductor. PLoS Comput. Biol. 19(8) (2023) - 2021
- [i3]Theo Killian, Laurent Gatto
Exploiting the DepMap cancer dependency data using the depmap R package. F1000Research 10: 416 (2021) - 2020
- [j9]Oliver M. Crook
A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection. PLoS Comput. Biol. 16(11): 1008288 (2020)
2010 – 2019
- 2019
- [j8]Johannes Rainer
ensembldb: an R package to create and use Ensembl-based annotation resources. Bioinform. 35(17): 3151-3153 (2019) - [i2]Oliver M. Crook, Lisa M. Breckels
A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Research 8: 446 (2019) - 2018
- [j7]Oliver M. Crook
A Bayesian mixture modelling approach for spatial proteomics. PLoS Comput. Biol. 14(11) (2018) - 2017
- [j6]Samuel Wieczorek
DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics. Bioinform. 33(1): 135-136 (2017) - [j5]Felipe da Veiga Leprevost
BioContainers: an open-source and community-driven framework for software standardization. Bioinform. 33(16): 2580-2582 (2017) - 2016
- [j4]Lisa M. Breckels, Sean B. Holden, David Wojnar, Claire M. Mulvey
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput. Biol. 12(5) (2016) - [j3]Yasset Pérez-Riverol
Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Comput. Biol. 12(7) (2016) - [p1]Lisa M. Breckels, Sebastian Gibb
Chapter 14. R for Proteomics. Proteome Informatics 2016: 321-364 - [i1]Lisa M. Breckels
A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Research 5: 2926 (2016) - 2014
- [j2]Laurent Gatto
Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinform. 30(9): 1322-1324 (2014) - 2012
- [j1]Laurent Gatto
MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinform. 28(2): 288-289 (2012)
Coauthor Index
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