The following packages have been used to generate this document.
## R version 4.0.3 Patched (2021-01-13 r79827)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/libf77blas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_BE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_BE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_BE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] patchwork_1.1.1 factoextra_1.0.7
## [3] gplots_3.1.1 limma_3.46.0
## [5] forcats_0.5.1 stringr_1.4.0
## [7] purrr_0.3.4 readr_1.4.0
## [9] tidyr_1.1.3 tibble_3.1.0
## [11] tidyverse_1.3.0 QFeatures_1.1.2
## [13] MultiAssayExperiment_1.17.18 SummarizedExperiment_1.20.0
## [15] Biobase_2.50.0 GenomicRanges_1.42.0
## [17] GenomeInfoDb_1.26.4 IRanges_2.24.1
## [19] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [21] MSnID_1.24.0 mzID_1.28.0
## [23] ggplot2_3.3.3 dplyr_1.0.5
## [25] magrittr_2.0.1 Spectra_1.1.9
## [27] ProtGenerics_1.23.6 BiocParallel_1.24.1
## [29] S4Vectors_0.28.1 BiocGenerics_0.36.0
## [31] msdata_0.30.0 rpx_1.99.3
## [33] mzR_2.24.1 Rcpp_1.0.6
## [35] BiocStyle_2.18.1
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## loaded via a namespace (and not attached):
## [1] utf8_1.1.4 R.utils_2.10.1 gmm_1.6-6
## [4] tidyselect_1.1.0 RSQLite_2.2.4 AnnotationDbi_1.52.0
## [7] grid_4.0.3 norm_1.0-9.5 munsell_0.5.0
## [10] codetools_0.2-18 preprocessCore_1.52.1 withr_2.4.1
## [13] colorspace_2.0-0 highr_0.8 knitr_1.31
## [16] rstudioapi_0.13 ggsignif_0.6.1 labeling_0.4.2
## [19] GenomeInfoDbData_1.2.4 bit64_4.0.5 farver_2.1.0
## [22] pheatmap_1.0.12 rhdf5_2.34.0 vctrs_0.3.6
## [25] generics_0.1.0 xfun_0.22 BiocFileCache_1.14.0
## [28] R6_2.5.0 doParallel_1.0.16 MsCoreUtils_1.3.2
## [31] AnnotationFilter_1.14.0 bitops_1.0-6 rhdf5filters_1.2.0
## [34] cachem_1.0.4 DelayedArray_0.16.2 assertthat_0.2.1
## [37] promises_1.2.0.1 scales_1.1.1 gtable_0.3.0
## [40] affy_1.68.0 sandwich_3.0-0 rlang_0.4.10
## [43] rstatix_0.7.0 lazyeval_0.2.2 impute_1.64.0
## [46] broom_0.7.5 abind_1.4-5 BiocManager_1.30.10
## [49] yaml_2.2.1 reshape2_1.4.4 modelr_0.1.8
## [52] backports_1.2.1 httpuv_1.5.5 tools_4.0.3
## [55] bookdown_0.21 affyio_1.60.0 ellipsis_0.3.1
## [58] jquerylib_0.1.3 RColorBrewer_1.1-2 MSnbase_2.17.1
## [61] plyr_1.8.6 zlibbioc_1.36.0 RCurl_1.98-1.2
## [64] ps_1.6.0 ggpubr_0.4.0 zoo_1.8-9
## [67] haven_2.3.1 ggrepel_0.9.1 fs_1.5.0
## [70] data.table_1.14.0 openxlsx_4.2.3 reprex_1.0.0
## [73] pcaMethods_1.82.0 mvtnorm_1.1-1 msmbstyle_0.0.11
## [76] R.cache_0.14.0 hms_1.0.0 mime_0.10
## [79] evaluate_0.14 xtable_1.8-4 XML_3.99-0.5
## [82] rio_0.5.26 readxl_1.3.1 compiler_4.0.3
## [85] KernSmooth_2.23-18 ncdf4_1.17 crayon_1.4.1
## [88] R.oo_1.24.0 htmltools_0.5.1.1 later_1.1.0.1
## [91] lubridate_1.7.10 DBI_1.1.1 dbplyr_2.1.0
## [94] MASS_7.3-53 tmvtnorm_1.4-10 rappdirs_0.3.3
## [97] Matrix_1.3-2 car_3.0-10 cli_2.3.1
## [100] vsn_3.58.0
## [ reached getOption("max.print") -- omitted 39 entries ]Christoforou, Andy, Claire M Mulvey, Lisa M Breckels, Aikaterini Geladaki, Tracey Hurrell, Penelope C Hayward, Thomas Naake, et al. 2016. “A Draft Map of the Mouse Pluripotent Stem Cell Spatial Proteome.” Nat Commun 7: 8992. https://doi.org/10.1038/ncomms9992.
Cox, J, and M Mann. 2008. “MaxQuant Enables High Peptide Identification Rates, Individualized P.p.b.-range Mass Accuracies and Proteome-Wide Protein Quantification.” Nat Biotechnol 26 (12): 1367–72. https://doi.org/10.1038/nbt.1511.
Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020. “MSnbase, Efficient and Elegant R-Based Processing and Visualisation of Raw Mass Spectrometry Data.” J. Proteome Res., September.
Lazar, C, L Gatto, M Ferro, C Bruley, and T Burger. 2016. “Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.” J Proteome Res 15 (4): 1116–25. https://doi.org/10.1021/acs.jproteome.5b00981.
Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2020. SummarizedExperiment: SummarizedExperiment Container. https://bioconductor.org/packages/SummarizedExperiment.
Paulovich, Amanda G, Dean Billheimer, Amy-Joan L Ham, Lorenzo Vega-Montoto, Paul A Rudnick, David L Tabb, Pei Wang, et al. 2010. “Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance.” Mol. Cell. Proteomics 9 (2): 242–54.
Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez Cabrera, Tiffany Chan, et al. 2017. “Software for the Integration of Multi-Omics Experiments in Bioconductor.” Cancer Research 77(21); e39-42.
Sticker, Adriaan, Ludger Goeminne, Lennart Martens, and Lieven Clement. 2019. “Robust Summarization and Inference in Proteome-Wide Label-Free Quantification.” bioRxiv. https://doi.org/10.1101/668863.
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