% 32321741
@Article{sticker2020,
   Author="Sticker, A.  and Goeminne, L.  and Martens, L.  and Clement, L. ",
   Title="{{R}obust {S}ummarization and {I}nference in {P}roteome-wide {L}abel-free {Q}uantification}",
   Journal="Mol Cell Proteomics",
   Year="2020",
   Volume="19",
   Number="7",
   Pages="1209--1219",
   Month="07"
}

% 32227882
@Article{goeminne2020,
   Author="Goeminne, L. J. E.  and Sticker, A.  and Martens, L.  and Gevaert, K.  and Clement, L. ",
   Title="{{M}{S}q{R}ob {T}akes the {M}issing {H}urdle: {U}niting {I}ntensity- and {C}ount-{B}ased {P}roteomics}",
   Journal="Anal Chem",
   Year="2020",
   Volume="92",
   Number="9",
   Pages="6278--6287",
   Month="05"
}

% 26566788
@Article{goeminne2016,
   Author="Goeminne, L. J.  and Gevaert, K.  and Clement, L. ",
   Title="{{P}eptide-level {R}obust {R}idge {R}egression {I}mproves {E}stimation, {S}ensitivity, and {S}pecificity in {D}ata-dependent {Q}uantitative {L}abel-free {S}hotgun {P}roteomics}",
   Journal="Mol Cell Proteomics",
   Year="2016",
   Volume="15",
   Number="2",
   Pages="657--668",
   Month="Feb"
}



@ARTICLE{vandenbulcke2025,
  title     = "{Msqrob2TMT}: Robust linear mixed models for inferring
               differential abundant proteins in labeled experiments with
               arbitrarily complex design",
  author    = "Vandenbulcke, Stijn and Vanderaa, Christophe and Crook, Oliver
               and Martens, Lennart and Clement, Lieven",
  journal   = "Mol. Cell. Proteomics",
  publisher = "Elsevier BV",
  volume    =  24,
  number    =  7,
  pages     =  101002,
  abstract  = "Labeling strategies in mass spectrometry-based proteomics enhance
               sample throughput by enabling the acquisition of multiplexed
               samples within a single run. However, contemporary experiments
               often involve increasingly complex designs, where the number of
               samples exceeds the capacity of a single run, resulting in a
               complex correlation structure that must be addressed for accurate
               statistical inference and reliable biomarker discovery. To this
               end, we introduce msqrob2TMT, a suite of mixed model-based
               workflows specifically designed for differential abundance
               analysis in labeled mass spectrometry-based proteomics data.
               msqrob2TMT accommodates both sample-specific and feature-specific
               (e.g., peptide or protein) covariates, facilitating inference in
               experiments with arbitrarily complex designs and allowing for
               explicit correction of feature-specific covariates. We benchmark
               our innovative workflows against state-of-the-art tools,
               including DEqMS, MSstatsTMT, and msTrawler, using two spike-in
               studies. Our findings demonstrate that msqrob2TMT offers greater
               flexibility, improved modularity, and enhanced performance,
               particularly through the application of robust ridge regression.
               Finally, we demonstrate the practical relevance of msqrob2TMT in
               a real mouse study, highlighting its capacity to effectively
               account for the complex correlation structure in the data.",
  month     =  may,
  year      =  2025,
  keywords  = "TMT labeling; differential proteomics; mass spectrometry; mixed
               models; statistics",
  language  = "en"
}

@article{Staes2024,
  author       = {Staes, An and Mendes Maia, Teresa and Dufour, Sara and Bouwmeester, Robbin and Gabriels, Ralf and Martens, Lennart and Gevaert, Kris and Impens, Francis and Devos, Simon},
  title        = {Benefit of In Silico Predicted Spectral Libraries in Data‑Independent Acquisition Data Analysis Workflows},
  journal      = {Journal of Proteome Research},
  year         = {2024},
  volume       = {23},
  number       = {6},
  pages        = {2078--2089},
  doi          = {10.1021/acs.jproteome.4c00048},
  pmid         = {38666436}
}