- Visualisation of proteomics data using R and Bioconductor
- Using R and Bioconductor for proteomics data analysis
RforProteomics: http://bioconductor.org/packages/RforProteomics- R/Bioconductor work-flow
- Analysis of post translational modification with isobar.
- Processing and analysis or isobaric tagging mass spectrometry with isobar and MSnbase.
- Analysis of spatial proteomics data with pRoloc.
- Analysis of MALDI data with the MALDIquant package.
- Access to the Proteomics Standard Initiative Common QUery InterfaCe with the PSICQUIC package.
- Cardinal: A mass spectrometry imaging toolbox for statistical analysis.
- protViz: Visualising and Analysing Mass Spectrometry Related Data in Proteomics
- aLFQ: Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data.
- protiq: Protein (identification and) quantification based on peptide evidence.
- MSstats: Protein Significance Analysis in DDA, SRM
and DIA for Label-free or Label-based Proteomics Experiments. Works
with
MSnSetobjects.
- Analysis of label-free data from a Synapt G2 (including ion mobility) with synapter.
- SWATH2stats: Transform and Filter SWATH Data for Statistical Packages and
- specL: Prepare Peptide Spectrum Matches for Use in Targeted Proteomics
- SwathXtend: SWATH extended library generation and statistical data analysis
The package vignettes often provide details on how to cite the
software. There's also the citation function:
citation("pRoloc")##
## To cite package 'pRoloc' in publications use:
##
## Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley KS.
## Mass-spectrometry-based spatial proteomics data analysis using
## pRoloc and pRolocdata. Bioinformatics. 2014 May 1;30(9):1322-4.
## doi:10.1093/bioinformatics/btu013. Epub 2014 Jan 11. PubMed
## PMID: 24413670; PubMed Central PMCID: PMC3998135.
##
## Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS,
## Trotter MW. The effect of organelle discovery upon sub-cellular
## protein localisation. J Proteomics. 2013 Mar 21. doi:pii:
## S1874-3919(13)00094-8. 10.1016/j.jprot.2013.02.019. PubMed PMID:
## 23523639.
##
## Gatto L., Breckels L.M., Burger T, Nightingale D.J.H., Groen
## A.J., Campbell C., Mulvey C.M., Christoforou A., Ferro M.,
## Lilley K.S. 'A foundation for reliable spatial proteomics data
## analysis' Mol Cell Proteomics. 2014 May 20.
##
## Breckels L.M., Holden S., Wonjar D., Mulvey C.M, Christoforou
## A., Groen A., Kohlbacker O., Lilley K.S. and Gatto L. 'Learning
## from heterogeneous data sources: an application in spatial
## proteomics' bioRxiv doi: http://dx.doi.org/xxx
sessionInfo()## R version 3.3.1 Patched (2016-08-02 r71022)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel methods stats graphics grDevices utils
## [8] datasets base
##
## other attached packages:
## [1] BiocStyle_2.1.33 qvalue_2.5.2 MSnID_1.7.3
## [4] msmsTests_1.11.0 msmsEDA_1.11.0 limma_3.29.21
## [7] multtest_2.29.0 RColorBrewer_1.1-2 ggplot2_2.1.0
## [10] magrittr_1.5 hexbin_1.27.1 dplyr_0.5.0
## [13] readxl_0.1.1 gridExtra_2.2.1 RforProteomics_1.11.2
## [16] mzID_1.11.2 msdata_0.14.0 lattice_0.20-34
## [19] pRolocdata_1.11.9 pRoloc_1.13.17 MLInterfaces_1.53.1
## [22] cluster_2.0.5 annotate_1.51.1 XML_3.98-1.4
## [25] AnnotationDbi_1.35.4 IRanges_2.7.17 S4Vectors_0.11.19
## [28] MSnbase_1.99.7 ProtGenerics_1.5.1 BiocParallel_1.7.9
## [31] mzR_2.7.12 Rcpp_0.12.7 Biobase_2.33.4
## [34] BiocGenerics_0.19.2 gplots_3.0.1 knitr_1.14
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.4 GSEABase_1.35.5
## [3] splines_3.3.1 ggvis_0.4.3
## [5] digest_0.6.10 foreach_1.4.3
## [7] BiocInstaller_1.24.0 htmltools_0.3.5
## [9] gdata_2.17.0 doParallel_1.0.10
## [11] sfsmisc_1.1-0 rda_1.0.2-2
## [13] R.utils_2.4.0 lpSolve_5.6.13
## [15] colorspace_1.2-7 RCurl_1.95-4.8
## [17] jsonlite_1.1 graph_1.51.0
## [19] genefilter_1.55.2 lme4_1.1-12
## [21] impute_1.47.0 survival_2.39-5
## [23] iterators_1.0.8 gtable_0.2.0
## [25] zlibbioc_1.19.0 MatrixModels_0.4-1
## [27] R.cache_0.12.0 car_2.1-3
## [29] kernlab_0.9-25 prabclus_2.2-6
## [31] DEoptimR_1.0-6 SparseM_1.72
## [33] scales_0.4.0 vsn_3.41.5
## [35] mvtnorm_1.0-5 edgeR_3.15.6
## [37] DBI_0.5-1 xtable_1.8-2
## [39] proxy_0.4-16 mclust_5.2
## [41] preprocessCore_1.35.0 htmlwidgets_0.7
## [43] sampling_2.7 threejs_0.2.2
## [45] FNN_1.1 fpc_2.1-10
## [47] modeltools_0.2-21 R.methodsS3_1.7.1
## [49] flexmix_2.3-13 nnet_7.3-12
## [51] locfit_1.5-9.1 RJSONIO_1.3-0
## [53] caret_6.0-71 reshape2_1.4.1
## [55] munsell_0.4.3 mlbench_2.1-1
## [57] biocViews_1.41.9 tools_3.3.1
## [59] RSQLite_1.0.0 pls_2.5-0
## [61] evaluate_0.10 stringr_1.1.0
## [63] robustbase_0.92-6 caTools_1.17.1
## [65] randomForest_4.6-12 dendextend_1.3.0
## [67] RBGL_1.49.3 nlme_3.1-128
## [69] whisker_0.3-2 mime_0.5
## [71] quantreg_5.29 formatR_1.4
## [73] R.oo_1.20.0 biomaRt_2.29.2
## [75] pbkrtest_0.4-6 interactiveDisplayBase_1.11.3
## [77] e1071_1.6-7 affyio_1.43.0
## [79] tibble_1.2 stringi_1.1.2
## [81] rpx_1.9.4 trimcluster_0.1-2
## [83] Matrix_1.2-7.1 nloptr_1.0.4
## [85] gbm_2.1.1 RUnit_0.4.31
## [87] MALDIquant_1.15 data.table_1.9.6
## [89] bitops_1.0-6 httpuv_1.3.3
## [91] R6_2.2.0 pcaMethods_1.65.0
## [93] affy_1.51.1 hwriter_1.3.2
## [95] KernSmooth_2.23-15 gridSVG_1.5-0
## [97] codetools_0.2-15 MASS_7.3-45
## [99] gtools_3.5.0 assertthat_0.1
## [101] interactiveDisplay_1.11.2 chron_2.3-47
## [103] Category_2.39.0 diptest_0.75-7
## [105] mgcv_1.8-15 grid_3.3.1
## [107] rpart_4.1-10 class_7.3-14
## [109] minqa_1.2.4 shiny_0.14.1
## [111] base64enc_0.1-3