8  Additional information

8.1 Citation

Please cite this book as:

TODO: add citation once published

Please cite the msqrob2 package as:

Goeminne L, Gevaert K, Clement L (2016). “Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.” Molecular & Cellular Proteomics, 15(2), 657-668. doi:10.1074/mcp.m115.055897.

If you opt for a summarisation-based workflow, you can also cite:

Sticker A, Goeminne L, Martens L, Clement L (2020). “Robust Summarization and Inference in Proteome-wide Label-free Quantification.” Molecular & Cellular Proteomics, 19(7), 1209-1219. doi:10.1074/mcp.ra119.001624.

If you use TMT-based workflows, please cite

Vandenbulcke S, Vanderaa C, Crook O, Martens L, Clement L. msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design. bioRxiv. Published online March 29, 2024:2024.03.29.587218. doi:10.1101/2024.03.29.587218

References

Gatto, Laurent, Ruedi Aebersold, Juergen Cox, Vadim Demichev, Jason Derks, Edward Emmott, Alexander M Franks, et al. 2023. “Initial Recommendations for Performing, Benchmarking and Reporting Single-Cell Proteomics Experiments.” Nat. Methods 20 (3): 375–86.
Goeminne, Ludger J E, Kris Gevaert, and Lieven Clement. 2016. “Peptide-Level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-Dependent Quantitative Label-Free Shotgun Proteomics.” Mol. Cell. Proteomics 15 (2): 657–68.
Huang, Ting, Meena Choi, Manuel Tzouros, Sabrina Golling, Nikhil Janak Pandya, Balazs Banfai, Tom Dunkley, and Olga Vitek. 2020. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.” Mol. Cell. Proteomics 19 (10): 1706–23.
O’Brien, Jonathon J, Anil Raj, Aleksandr Gaun, Adam Waite, Wenzhou Li, David G Hendrickson, Niclas Olsson, and Fiona E McAllister. 2024. “A Data Analysis Framework for Combining Multiple Batches Increases the Power of Isobaric Proteomics Experiments.” Nat. Methods 21 (2): 290–300.
Plubell, Deanna L, Phillip A Wilmarth, Yuqi Zhao, Alexandra M Fenton, Jessica Minnier, Ashok P Reddy, John Klimek, Xia Yang, Larry L David, and Nathalie Pamir. 2017. “Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue.” Mol. Cell. Proteomics 16 (5): 873–90.
Ramond, Elodie, Gael Gesbert, Ida Chiara Guerrera, Cerina Chhuon, Marion Dupuis, Mélanie Rigard, Thomas Henry, Monique Barel, and Alain Charbit. 2015. “Importance of Host Cell Arginine Uptake in Francisella Phagosomal Escape and Ribosomal Protein Amounts.” Mol. Cell. Proteomics 14 (4): 870–81.
Savitski, Mikhail M, Gavain Sweetman, Manor Askenazi, Jarrod A Marto, Manja Lang, Nico Zinn, and Marcus Bantscheff. 2011. “Delayed Fragmentation and Optimized Isolation Width Settings for Improvement of Protein Identification and Accuracy of Isobaric Mass Tag Quantification on Orbitrap-Type Mass Spectrometers.” Anal. Chem. 83 (23): 8959–67.
Segers, Alexandre, Cristian Castiglione, Christophe Vanderaa, Elfride De Baere, Lennart Martens, Davide Risso, and Lieven Clement. 2025. omicsGMF: A Multi-Tool for Dimensionality Reduction, Batch Correction and Imputation Applied to Bulk- and Single Cell Proteomics Data.” bioRxiv, March, 2025.03.24.644996.
Shen, Xiaomeng, Shichen Shen, Jun Li, Qiang Hu, Lei Nie, Chengjian Tu, Xue Wang, et al. 2018. IonStar Enables High-Precision, Low-Missing-Data Proteomics Quantification in Large Biological Cohorts.” Proc. Natl. Acad. Sci. U. S. A. 115 (21): E4767–76.
Sticker, Adriaan, Ludger Goeminne, Lennart Martens, and Lieven Clement. 2020. “Robust Summarization and Inference in Proteome-Wide Label-Free Quantification.” Mol. Cell. Proteomics 19 (7): 1209–19.
Vandenbulcke, Stijn, Christophe Vanderaa, Oliver Crook, Lennart Martens, and Lieven Clement. 2025. Msqrob2TMT: Robust Linear Mixed Models for Inferring Differential Abundant Proteins in Labeled Experiments with Arbitrarily Complex Design.” Mol. Cell. Proteomics 24 (7): 101002.

8.2 Data sets

We refer here the data sources used in the book:

8.2.1 E. Coli LFQ spike-in data set

Original study: Shen X, Shen S, Li J, Hu Q, Nie L, Tu C, et al. (2018) Ionstar enables high-precision, low-missing-data proteomics quantification in large bio- logical cohorts. Proc. Natl. Acad. Sci. U.S.A. 115, E4767–E4776

Reanalysis study: Sticker A, Goeminne L, Martens L, Clement L. Robust Summarization and Inference in Proteome-wide Label-free Quantification. Mol Cell Proteomics. 2020;19(7):1209-1219.

Link to data: https://github.com/statOmics/MSqRobSumPaper/raw/refs/heads/master/spikein/data/maxquant/peptides.zip

Link to data in archive: TODO add link to Zenodo

Used in Chapter 1 and Chapter 3.

8.2.2 TMT spike-in data set

Original study: Huang T, Choi M, Tzouros M, Golling S, Pandya NJ, Banfai B, et al. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures. Mol Cell Proteomics. 2020;19(10):1706-1723.

Reanalysis study: Vandenbulcke S, Vanderaa C, Crook O, Martens L, Clement L. Msqrob2TMT: Robust linear mixed models for inferring differential abundant proteins in labeled experiments with arbitrarily complex design. Mol Cell Proteomics. 2025;24(7):101002.

Data source: MassIVE repository (RMSV000000265)

Link to data from archive: https://zenodo.org/records/14767905

Used in Chapter 2.

8.2.3 Francisella data set

TODO

8.2.4 Heart data set

TODO

8.2.5 Mouse diet data set

TODO

8.3 License

Creative Commons Licence
This material is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, as long as you give appropriate credit and distribute your contributions under the same license as the original.

8.4 Session Info

The following packages have been used to generate this document.

sessionInfo()
R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Brussels
tzcode source: system (glibc)

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] bookdown_0.45               tidyr_1.3.1                
 [3] scater_1.38.0               scuttle_1.20.0             
 [5] SingleCellExperiment_1.32.0 patchwork_1.3.2            
 [7] MsDataHub_1.10.0            impute_1.84.0              
 [9] ggrepel_0.9.6               ggplot2_4.0.1              
[11] ExploreModelMatrix_1.22.0   dplyr_1.1.4                
[13] ComplexHeatmap_2.26.0       BiocFileCache_3.0.0        
[15] dbplyr_2.5.1                BiocParallel_1.44.0        
[17] msqrob2book_0.0.99          msqrob2_1.18.0             
[19] QFeatures_1.20.0            MultiAssayExperiment_1.36.1
[21] SummarizedExperiment_1.40.0 Biobase_2.70.0             
[23] GenomicRanges_1.62.0        Seqinfo_1.0.0              
[25] IRanges_2.44.0              S4Vectors_0.48.0           
[27] BiocGenerics_0.56.0         generics_0.1.4             
[29] MatrixGenerics_1.22.0       matrixStats_1.5.0          

loaded via a namespace (and not attached):
  [1] splines_4.5.2           later_1.4.4             filelock_1.0.3         
  [4] tibble_3.3.0            lifecycle_1.0.4         httr2_1.2.1            
  [7] Rdpack_2.6.4            doParallel_1.0.17       rprojroot_2.1.1        
 [10] lattice_0.22-7          MASS_7.3-65             magrittr_2.0.4         
 [13] limma_3.66.0            rmarkdown_2.30          yaml_2.3.11            
 [16] remotes_2.5.0           httpuv_1.6.16           otel_0.2.0             
 [19] sessioninfo_1.2.3       pkgbuild_1.4.8          cowplot_1.2.0          
 [22] MsCoreUtils_1.22.1      DBI_1.2.3               minqa_1.2.8            
 [25] RColorBrewer_1.1-3      abind_1.4-8             pkgload_1.4.1          
 [28] purrr_1.2.0             AnnotationFilter_1.34.0 rappdirs_0.3.3         
 [31] circlize_0.4.16         irlba_2.3.5.1           codetools_0.2-20       
 [34] DelayedArray_0.36.0     DT_0.34.0               tidyselect_1.2.1       
 [37] shape_1.4.6.1           farver_2.1.2            lme4_1.1-37            
 [40] ScaledMatrix_1.18.0     viridis_0.6.5           jsonlite_2.0.0         
 [43] GetoptLong_1.1.0        BiocNeighbors_2.4.0     ellipsis_0.3.2         
 [46] iterators_1.0.14        foreach_1.5.2           tools_4.5.2            
 [49] Rcpp_1.1.0              glue_1.8.0              gridExtra_2.3          
 [52] SparseArray_1.10.3      BiocBaseUtils_1.12.0    xfun_0.54              
 [55] usethis_3.2.1           shinydashboard_0.7.3    withr_3.0.2            
 [58] BiocManager_1.30.27     fastmap_1.2.0           boot_1.3-31            
 [61] shinyjs_2.1.0           digest_0.6.39           rsvd_1.0.5             
 [64] R6_2.6.1                mime_0.13               colorspace_2.1-2       
 [67] RSQLite_2.4.5           httr_1.4.7              htmlwidgets_1.6.4      
 [70] S4Arrays_1.10.0         pkgconfig_2.0.3         gtable_0.3.6           
 [73] blob_1.2.4              S7_0.2.1                XVector_0.50.0         
 [76] htmltools_0.5.8.1       ProtGenerics_1.42.0     rintrojs_0.3.4         
 [79] clue_0.3-66             scales_1.4.0            png_0.1-8              
 [82] reformulas_0.4.2        knitr_1.50              rstudioapi_0.17.1      
 [85] reshape2_1.4.5          rjson_0.2.23            nlme_3.1-168           
 [88] curl_7.0.0              nloptr_2.2.1            cachem_1.1.0           
 [91] GlobalOptions_0.1.3     stringr_1.6.0           BiocVersion_3.22.0     
 [94] parallel_4.5.2          vipor_0.4.7             AnnotationDbi_1.72.0   
 [97] desc_1.4.3              pillar_1.11.1           vctrs_0.6.5            
[100] promises_1.5.0          BiocSingular_1.26.1     beachmat_2.26.0        
[103] xtable_1.8-4            cluster_2.1.8.1         beeswarm_0.4.0         
[106] evaluate_1.0.5          cli_3.6.5               compiler_4.5.2         
[109] rlang_1.1.6             crayon_1.5.3            plyr_1.8.9             
[112] fs_1.6.6                ggbeeswarm_0.7.3        stringi_1.8.7          
[115] viridisLite_0.4.2       Biostrings_2.78.0       lazyeval_0.2.2         
[118] devtools_2.4.6          Matrix_1.7-4            ExperimentHub_3.0.0    
[121] bit64_4.6.0-1           KEGGREST_1.50.0         statmod_1.5.1          
[124] shiny_1.11.1            AnnotationHub_4.0.0     rbibutils_2.4          
[127] igraph_2.2.1            memoise_2.0.1           bit_4.6.0