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A statistical framework for protein quantitation in bottom-up MS-based proteomics
Open accessAbstract
MOTIVATION: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).
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“Peptides may be missing from one or more samples due to a variety of underlying mechanisms (Daly et al., 2008; Karpievitch et al., 2009), which can result in non-trivial amounts of potential observations being missing.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Karpievitch et al., 2009
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
Testing Statistical Hypotheses
Niewiadomska-Bugaj, M., & Bartoszynski, R. (2021) · Wiley series in probability and statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,188
Keller, A., Nesvizhskii, A., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,970
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999) · Nature Biotechnology · doi:10.1038/13690 · Cited by 4,683 · cited 1×
“…evidence that linear relationships between intensity and abundance can be attained using microcapillary liquid chromatography (µLC) separation coupled with electrospray ionization (ESI) MS/MS, based on either stable isotope labeling or label-free approaches (Gygi et al. (1999), Wang et al. (2003)).”
BackgroundMissing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230
Cited by
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Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
“These computational methods include simple additive prescriptions to combine peptide intensities (32, 33), reference-peptide-based estimates (34), and statistical frameworks utilizing additive linear models (35, 36).”
MethodLazar, C., Gatto, L., Ferro, M., Bruley, C., & Bürger, T. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00981 · Cited by 549
“This idea is not new: several recent works have proposed to perform imputation by estimating models (with maximum-likelihood [14, 15] or with empirical Bayesian [16] methods) which are rich enough to account for both types of missingness mechanisms.”
MethodNormalization and missing value imputation for label-free LC-MS analysis
Karpievitch, Y. V., Dabney, A. R., & Smith, R. D. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s5 · Cited by 345
“In this sense, using the proposed approach is similar to using the maximum likelihood model (Karpievitch et al. 2009b) to obtain the p-values for differential expression; we can get the p-values but not the p-values for imputed data.”
BackgroundMethodInfluentialSelf-citationWebb-Robertson, B.-J. M., Wiberg, H. K., Matzke, M. M., Brown, J. N., Wang, J., McDermott, J. E., Smith, R. D., Rodland, K. D., Metz, T. O., Pounds, J. G., et al. (2015) · Journal of Proteome Research · doi:10.1021/pr501138h · Cited by 294
Self-citationHigh Resolution Mass Spectrometry
Xian, F., Hendrickson, C. L., & Marshall, A. G. (2012) · Analytical Chemistry · doi:10.1021/ac203191t · Cited by 281
Liquid chromatography mass spectrometry-based proteomics: Biological and technological aspects
Karpievitch, Y. V., Polpitiya, A. D., Anderson, G., Smith, R. D., & Dabney, A. R. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas341 · Cited by 234
General Statistical Framework for Quantitative Proteomics by Stable Isotope Labeling
Navarro, P., Trevisán-Herraz, M., Bonzón‐Kulichenko, E., Núñez, E., Martínez-Acedo, P., Pérez-Hernández, D., Jorge, I., Mesa, R., Calvo, E., Carrascal, M., et al. (2014) · Journal of Proteome Research · doi:10.1021/pr4006958 · Cited by 217
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 217
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181
Dealing with missing values in large-scale studies: microarray data imputation and beyond
Aittokallio, T. A. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp059 · Cited by 171
“value estimation methods developed for microarrays, such as KNN imputation that is being applied to statistical analysis of quantitative LC-MS-based proteomics data [53], may yield sub-optimal results; it is likely that more application-specific methods are needed that also account for the missingness mechanisms [54].”
MethodA comparative study of evaluating missing value imputation methods in label-free proteomics
Jin, L., Bi, Y., Hu, C., Qu, J., Shen, S., Wang, X., & Tian, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-81279-4 · Cited by 178
Nikolovski, N., Rubtsov, D. V., Segura-Lepe, M. P., Miles, G. P., Stevens, T. J., Dunkley, T. P. J., Munro, S., Lilley, K. S., & Dupree, P. (2012) · PLANT PHYSIOLOGY · doi:10.1104/pp.112.204263 · Cited by 157
“Four of the identified proteins in this study belong to the KD clade and one to the KT clade of phosphate translocator homologous proteins, as annotated by Knappe et al. (2003).”
DanteR: an extensible R-based tool for quantitative analysis of -omics data
Taverner, T., Karpievitch, Y. V., Polpitiya, A. D., Brown, J. N., Dabney, A. R., Anderson, G., & Smith, R. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts449 · Cited by 143
Label-Free Quantitative LC–MS Proteomics of Alzheimer’s Disease and Normally Aged Human Brains
Andreev, V. P., Petyuk, V., Brewer, H. M., Karpievitch, Y. V., Xie, F., Clarke, J., Camp, D., Smith, R. D., Lieberman, A. P., Albin, R. L., et al. (2012) · Journal of Proteome Research · doi:10.1021/pr3001546 · Cited by 142
Clough, T., Thaminy, S., Ragg, S., Aebersold, R. H., & Olga Vitek, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s6 · Cited by 137
“Others argue against imputation, and transform the peak intensities to binary present/absent values [38], treat the missing intensities as censored values as in survival analysis models [18,39], or advocate testing a peptide for whether its missing intensities occur more frequently in some experimental groups than exp…”
BackgroundKarpievitch, Y. V., Taverner, T., Adkins, J., Callister, S., Anderson, G., Smith, R. D., & Dabney, A. R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp426 · Cited by 119
Goeminne, L. E., Gevaert, K., & Clement, L. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.055897 · Cited by 110
“Moreover, our strategies to improve robustness of the estimators can be easily plugged into censored regression methods or estimation approaches that adopt advanced imputation techniques for handling missing data (40).”
MethodImproved quality control processing of peptide-centric LC-MS proteomics data
Matzke, M. M., Waters, K. M., Metz, T. O., Jacobs, J. M., Sims, A. C., Baric, R. S., Pounds, J. G., & Webb-Robertson, B.-J. M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr479 · Cited by 107
Goeminne, L. J. E., Gevaert, K., & Clement, L. (2017) · Journal of Proteomics · doi:10.1016/j.jprot.2017.04.004 · Cited by 106
Webb‐Robertson, B., McCue, L. A., Waters, K. M., Matzke, M. M., Jacobs, J., Metz, T., Varnum, S. M., & Pounds, J. G. (2010) · Journal of Proteome Research · doi:10.1021/pr1005247 · Cited by 102
Ali, A., Alexandersson, E., Sandin, M., Resjö, S., Lenman, M., Hedley, P. E., Levander, F., & Andréasson, E. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-497 · Cited by 100
“In DanteR, data was filtered, missing values imputed and protein level intensities for the leading proteins calculated using Q-Rollup function with 2 to 3 peptides per protein based on median peptide intensities [57-59].”
Deciphering Thylakoid Sub-compartments using a Mass Spectrometry-based Approach
Tomizioli, M., Lazar, C., Brugière, S., Bürger, T., Salvi, D., Gatto, L., Moyet, L., Breckels, L. M., Hesse, A.-M., Lilley, K. S., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.040923 · Cited by 94
“Although different probabilistic models are proposed to separate between MCAR data and censored data (49, 50), our approach is based on a statistical investigation of the data in order to verify whether the missing values are abundance dependent or not.”
MethodMatzke, M. M., Brown, J. N., Gritsenko, M. A., Metz, T., Pounds, J. G., Rodland, K., Shukla, A. K., Smith, R. D., Waters, K. M., McDermott, J., et al. (2012) · PROTEOMICS · doi:10.1002/pmic.201200269 · Cited by 87
“A protein-level additive model accounting for the main effects of peptides and proteins is proposed by Karpievitch et al. (LM1) [16].”
MethodQuast, J.-P., Schuster, D., & Picotti, P. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab041 · Cited by 84
“Although protti does not provide a graphical user interface, unlike proteomics tools such as proteosign (Efstathiou et al., 2017), Perseus (Tyanova and Cox, 2018), Prostar (Wieczorek et al., 2017), DAnTE (Karpievitch et al., 2009), PIQMIe (Kuzniar and Kanaar, 2014), StatQuant (van Breukelen et al., 2009), LFQ-Analyst…”
Liu, C., Song, C., Yuan, Z., Fu, Y., Chi, H., Wang, L., Fan, S., Zhang, K., Zeng, W., He, S., et al. (2014) · Analytical Chemistry · doi:10.1021/ac404246w · Cited by 85
Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics
Blein‐Nicolas, M., & Zivy, M. (2016) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2016.02.019 · Cited by 97
Shah, J., Rai, S. N., DeFilippis, A. P., Hill, B. G., Bhatnagar, A., & Brock, G. N. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1547-6 · Cited by 86
“Using the imputation methods for microarray studies in MS omics studies could lead to biased results because most of the imputation techniques produce unbiased results only if the missing data are MCAR or MAR [8].”
MethodProper imputation of missing values in proteomics datasets for differential expression analysis
Liu, M., & Dongré, A. R. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa112 · Cited by 86
Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue
Liu, N. Q., Braakman, R. B. H., Stingl, C., Luider, T. M., Martens, J. W. M., Foekens, J. A., & Umar, A. (2012) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-012-9252-6 · Cited by 74
Choi, H., Kim, S., Fermin, D., Tsou, C., & Nesvizhskii, A. I. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.07.036 · Cited by 77
“The model-based methods [18] may offer a treatment of missing data at the peptide-level, but their likelihood-based imputation scheme requires more than several samples per comparison group.”
MethodProteogenomic Analysis of a Thermophilic Bacterial Consortium Adapted to Deconstruct Switchgrass
D’haeseleer, P., Gladden, J. M., Allgaier, M., Chain, P., Tringe, S. G., Malfatti, S., Aldrich, J. T., Nicora, C., Robinson, E. W., Paša‐Tolić, L., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068465 · Cited by 73
“‘‘Missingness’’ in proteomics data is typically associated with below-threshold peak intensities, so averaging only across non-missing values will overestimate protein abundance [44,45].”
BackgroundHerbers, J., Miller, R. E., Walther, A., Schindler‐Gmelch, L., Schmidt, K., Gao, W., & Rupprecht, F. (2021) · Comprehensive Psychoneuroendocrinology · doi:10.1016/j.cpnec.2021.100052 · Cited by 71
“Instead, the fact that they are missing can be considered informative, because they reflect truly low concentrations of the respective biomarker [13,14].”
BackgroundPeng, H., Wang, H., Kong, W., Li, J., & Goh, W. W. B. (2024) · Nature Communications · doi:10.1038/s41467-024-47899-w · Cited by 69
Computational Mass Spectrometry–Based Proteomics
Käll, L., & Vitek, O. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002277 · Cited by 70
“classes of statistical models. Important aspects are accurate representation of the experimental design and of within-run groupings of peaks in label-based workflows, treatment of missing data (e.g., using specialized”
BackgroundComputational and Statistical Analysis of Protein Mass Spectrometry Data
Noble, W. S., & MacCoss, M. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002296 · Cited by 64
Gianetto, Q. G., Wieczorek, S., Couté, Y., & Bürger, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.29.122770 · Cited by 67
Ammar, C., Gruber, M., Csaba, G., & Zimmer, R. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001509 · Cited by 61
“Peptide level models have been proposed (11, 12) and have recently been shown to offer superior performance compared with protein level approaches (13, 14, 15).”
ResultMulti-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior
Misiewicz, Z., Iurato, S., Kulesskaya, N., Salminen, L., Rodrigues, L., Maccarrone, G., Martins, J., Czamara, D., Laine, M. A., Sokolowska, E., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008358 · Cited by 59
The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments
O’Brien, J. J., Gunawardena, H. P., Paulo, J. A., Chen, X., Ibrahim, J. G., Gygi, S. P., & Qaqish, B. F. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1144 · Cited by 54
“The sources of missing data are rooted in known technological and scientific processes and the proportion of missing values will often exceed 50% of a dataset (Karpievitch et al., 2009).”
Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics
Gardner, M. L., & Freitas, M. A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22179650 · Cited by 56
Brown, J. N., Ortiz, G. M., Angel, T. E., Jacobs, J., Gritsenko, M., Chan, E. Y., Purdy, D. E., Murnane, R. D., Larsen, K., Palermo, R. E., et al. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.016121 · Cited by 51
“A model-based statistical approach was used to impute missing peptide abundance values (54) and a protein-level ANOVA was applied to identify statistically significant changes (p 0.”
MethodDreamAI: algorithm for the imputation of proteomics dataPreprint
Ma, W., Kim, S., Chowdhury, S., Li, Z., Yang, M., Yoo, S., Petralia, F., Jacobsen, J. R., Li, J. J., Ge, X., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.21.214205 · Cited by 50
“In addition, one study [32] reported superior performance of methods based on global structure, such as low-rank matrix completion [18] and linear model based maximum likelihood estimate [33] [34] compared to those of local similarity based methods (KNN) for label free proteomics data.”
MethodPerrin, R. J., Payton, J. E., Malone, J. P., Erdmann-Gilmore, P., Davis, A., Xiong, C., Fagan, A. M., Townsend, R. R., & Holtzman, D. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064314 · Cited by 50
“The gene-grouped and peptide intensity data were imported into DAnTE-R for statistical analysis [53,54].”
MethodResjö, S., Brus-Szkalej, M., Ali, A., Meijer, H. J. G., Sandin, M., Govers, F., Levander, F., Grenville‐Briggs, L. J., & Andréasson, E. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.065656 · Cited by 47
“ANOVA-based statistics were used because log-transformed peptide intensities in general follow a normal distribution.”
MethodInfluentialA Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics
Bramer, L. M., Irvahn, J., Piehowski, P. D., Rodland, K. D., & Webb-Robertson, B.-J. M. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00123 · Cited by 47
Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies
Milac, T. I., Randolph, T. W., & Wang, P. (2012) · Statistics and Its Interface · doi:10.4310/sii.2012.v5.n1.a7 · Cited by 44
“For statistical models of protein rollup based on ion abundances, see [3, 16].”
BackgroundGoeminne, L. J. E., Argentini, A., Martens, L., & Clement, L. (2015) · Journal of Proteome Research · doi:10.1021/pr501223t · Cited by 47
Dicker, L. H., Lin, X., & Ivanov, A. R. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.002774 · Cited by 44
“Our procedure for generating data with low match rates may not accurately mimic the missingness mechanism governing the msInspect/AMT matching procedure (40); however, we believe that our results may offer insight into the performance of ProPCA.”
ResultVasilj, A., Gentzel, M., Ueberham, E., Gebhardt, R., & Shevchenko, A. (2012) · Journal of Proteome Research · doi:10.1021/pr300147z · Cited by 41
Lichti, C. F., Liu, H., Shavkunov, A. S., Mostovenko, E., Sulman, E. P., Ezhilarasan, R., Wang, Q., Kroes, R. A., Moskal, J. C., Fenyö, D., et al. (2013) · Journal of Proteome Research · doi:10.1021/pr400786s · Cited by 42
Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins
Suomi, T., Corthals, G. L., Nevalainen, O., & Elo, L. L. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00363 · Cited by 42
Preprocessing and Analysis of LC-MS-Based Proteomic Data
Tsai, T., Wang, M., & Ressom, H. W. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3106-4_3 · Cited by 40
“In addition to analytical considerations, crucial steps include: (1) experimental design that avoids introducing bias during data acquisition and enables effective utilization of available resource [7], (2) data preprocessing pipeline that extracts meaningful features [8], and (3) statistical test that identifies sign…”
MethodMorales, D. M., Townsend, R. R., Malone, J. P., Ewersmann, C. A., Macy, E. M., Inder, T. E., & Limbrick, D. D. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.011973 · Cited by 40
“The gene-grouped and peptide intensity data were imported into DAnTE-R for statistical analysis (18, 19).”
BackgroundMethodIntegrated Identification and Quantification Error Probabilities for Shotgun Proteomics
The, M., & Käll, L. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001018 · Cited by 37
Taylor, S. L., Ruhaak, L. R., Kelly, K. L., Weiss, R. H., & Kim, K. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw010 · Cited by 41
Label-Free Glycopeptide Quantification for Biomarker Discovery in Human Sera
Mayampurath, A. M., Song, E., Mathur, A., Yu, C., Hammoud, Z. T., Mechref, Y. S., & Tang, H. (2014) · Journal of Proteome Research · doi:10.1021/pr500242m · Cited by 38
Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics
D’Angelo, G., Chaerkady, R., Yu, W., Hızal, D. B., Hess, S. Y., Zhao, W. G., Lekstrom, K. J., Guo, X., White, W. I., Roskos, L. K., et al. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b01050 · Cited by 38
C-terminal phosphorylation of NaV1.5 impairs FGF13-dependent regulation of channel inactivation
Burel, S., Coyan, F. C., Lorenzini, M., Meyer, M. R., Lichti, C. F., Brown, J. H., Loussouarn, G., Charpentier, F., Nerbonne, J. M., Townsend, R. R., et al. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.787788 · Cited by 38
Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data
Zhan, X., Patterson, A. D., & Ghosh, D. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0506-3 · Cited by 33
“where the notations for the parameters Proti, Pepij, Grpik are the same as those in [32], and the same sum-to-zero constrains are also applied in this logistic model.”
BackgroundMethodInfluentialKawahara, R., Recuero, S. D. C., Nogueira, F. C. S., Domont, G. B., Leite, K. R. M., Srougi, M., Thaysen‐Andersen, M., & Palmisano, G. (2019) · PROTEOMICS · doi:10.1002/pmic.201900174 · Cited by 33
Lichti, C. F., Fan, X., English, R. D., Zhang, Y., Li, D., Kong, F., Sinha, M., Andersen, C. R., Spratt, H. M., Luxon, B. A., et al. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00246 · Cited by 32
“Dynamic exclusion was used to remove selected precursor ions (±10 ppm) for 60 s after MS/MS acquisition.”
MethodInfluentialLi, M., & Smyth, G. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad200 · Cited by 32
“The most common MNAR model is to assume that missing values are left-censored (Karpievitch et al. 2009, 2012; Tekwe et al. 2012; Wieczorek et al. 2017).”
BackgroundMethodFocus on the spectra that matter by clustering of quantification data in shotgun proteomics
The, M., & Käll, L. (2020) · Nature Communications · doi:10.1038/s41467-020-17037-3 · Cited by 32
“Also, missing value imputation is known to induce false quantification accuracy [16].”
BackgroundStoddard, E. G., Nag, S. A., Martin, J., Tyrrell, K. J., Gibbins, T., Anderson, K. A., Shukla, A. K., CORLEY, R. A., Wright, A., & Smith, J. N. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.1c00235 · Cited by 30
Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
Taylor, S. L., Leiserowitz, G. S., & Kim, K. (2013) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2013-0021 · Cited by 30
Integrating Phosphoproteome and Transcriptome Reveals New Determinants of Macrophage Multinucleation
Rotival, M., Ko, J., Srivastava, P. K., Kerloc’h, A., Montoya, A., Mauro, C., Faull, P. A., Cutillas, P. R., Petretto, E. G., & Behmoaras, J. V. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.043836 · Cited by 28
“Briefly, in keeping with (25), we assume that peptide expression is missing with a probability that depends on both the true expression of the peptide (data is missing when the true expression is under a peptide-dependent threshold ci) and the sample specific probability of missing data (sample dependent-probability l…”
BackgroundMethodInfluentialTaylor, S. L., Ponzini, M. D., Wilson, M., & Kim, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab353 · Cited by 31
Li, Q., Fisher, K. J., Meng, W., Fang, B., Welsh, E. A., Haura, E. B., Koomen, J. M., Eschrich, S. A., Fridley, B. L., & Chen, Y. A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz488 · Cited by 26
Pattern alternating maximization algorithm for missing data in high-dimensional problems
Städler, N., Stekhoven, D. J., & Bühlmann, P. (2014) · Journal of Machine Learning Research · doi:10.5555/2627435.2670312 · Cited by 25
“…specifically designed for microarrays and is potentially very useful for many other highdimensional applications: examples include mass spectrometry-based proteomics, climate field reconstructions and image analysis in cosmology (Karpievitch et al., 2009; Schneider, 2001; Starck and Bobin, 2010).”
BackgroundA hybrid approach to protein differential expression in mass spectrometry-based proteomics
Wang, X., Anderson, G., Smith, R. D., & Dabney, A. R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts193 · Cited by 25
Garcia, W. L., Miller, C. J., Lomas, G. X., Gaither, K. A., Tyrrell, K. J., Smith, J. N., Brandvold, K. R., & Wright, A. (2022) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.1c00360 · Cited by 23
Albaum, S. P., Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, A., & Nattkemper, T. W. (2011) · Proteome Science · doi:10.1186/1477-5956-9-30 · Cited by 23
“Conclusions This work aims at paving a straight path through the jungle of computational methods to analyze mass spectrometry-based isotope-labeled datasets, targeting the two questions that typically arise in proteomics experiments: 1) which proteins are differentially regulated regarding the selected experimental co…”
BackgroundShearer, J. J., Wold, E. A., Umbaugh, C. S., Lichti, C. F., Nilsson, C. L., & Figueiredo, M. L. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1510090 · Cited by 22
“1) (Karpievitch et al. 2009; Polpitiya et al. 2008) and processed for protein quantification as previously described (Lichti et al.”
MethodInfluentialBioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections
Rahmatbakhsh, M., Gagarinova, A. G., & Babu, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.667936 · Cited by 21
High‐capacity peptide‐centric platform to decode the proteomic response to brain injury
Cortes, D. F., Landis, M. K., & Ottens, A. K. (2012) · Electrophoresis · doi:10.1002/elps.201200341 · Cited by 21
Chen, Y., Chambers, M., Li, M., Ham, A. L., Turner, J. L., Zhang, B., & Tabb, D. L. (2013) · Journal of Proteome Research · doi:10.1021/pr400438q · Cited by 22
Lindberg, T., de Ávila, R. I., Zeller, K. S., Levander, F., Eriksson, D., Chawade, A., & Lindstedt, M. (2020) · Journal of Proteomics · doi:10.1016/j.jprot.2020.103647 · Cited by 21
RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis
Glaab, E., & Schneider, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv127 · Cited by 20
“13 3 Simulation experiment for increasing numbers of technical replicates 15”
BackgroundBayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach
Shah, J., Brock, G. N., & Gaskins, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3250-2 · Cited by 20
High-Resolution Proteomic Profiling Shows Sexual Dimorphism in Zebrafish Heart-Associated Proteins
Niksirat, H., Siino, V., Steinbach, C., & Levander, F. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00387 · Cited by 20
Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data
Tekwe, C. D., Carroll, R. J., & Dabney, A. R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts306 · Cited by 19
Koopmans, F., Cornelisse, L. N., Heskes, T., & Dijkstra, T. M. H. (2014) · Journal of Proteome Research · doi:10.1021/pr500171u · Cited by 19
Nonstandard conditionally specified models for nonignorable missing data
Franks, A. M., Airoldi, E. M., & Rubin, D. B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1815563117 · Cited by 21
“37, missing values can occur for multiple reasons, at different stages of the data-collection process.”
Design and Statistical Analysis of Mass-Spectrometry-Based Quantitative Proteomics Data
Yu, F., Qiu, F., & Meza, J. E. (2016) · Elsevier eBooks · doi:10.1016/b978-0-444-63688-1.00012-4 · Cited by 20
Detecting differential protein expression in large-scale population proteomics
Ryu, S. Y., Qian, W., Camp, D., Smith, R. D., Tompkins, R. G., Davis, R. W., & Xiao, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu341 · Cited by 18
Gallo, M. C. R., Li, Q., Talasila, M., & Uhrig, R. G. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100638 · Cited by 18
“dependent acquisition (DDA), which is known to increase missing values (MV) between 77 replicate samples due to its “TopN” precursor selection approach (4, 5).”
BackgroundA Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data
Hutchings, C., Dawson, C. S., Krueger, T., Lilley, K. S., & Breckels, L. M. (2023) · F1000Research · doi:10.12688/f1000research.139116.1 · Cited by 18
High-Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections
Carin, L., Hero, A. O., Lucas, J. E., Dunson, D. B., Chen, M., Henao, R., Tibau-Piug, A., Zaas, A. K., Woods, C. W., & Ginsburg, G. S. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.943009 · Cited by 15
“In addition to these algorithms, there are a number of different analysis of variance (ANOVA) approaches that include fixed effects for protein, peptide, and experimental group [23]; include an additional random effect for cases in which subjects are measured in replicate [24]; or THERE ARE A NUMBER OF DIFFERENT REGRE…”
BackgroundElo, L. L., Karjalainen, R., Öhman, T., Hintsanen, P., Nyman, T. A., Heckman, C. A., & Aittokallio, T. A. (2014) · PROTEOMICS · doi:10.1002/pmic.201300460 · Cited by 14
“Although controlling of FDR is being used in the statistical treatment of large-scale protein identification and quantification results [21, 58, 59], it is rather rarely applied when assessing between-group protein abundance changes [60].”
MethodBioinformatics Tools to Identify and Quantify Proteins Using Mass Spectrometry Data
Ryu, S. Y. (2014) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-800168-4.00001-9 · Cited by 14
Metaprotein expression modeling for label-free quantitative proteomics
Lucas, J. E., Thompson, J. W., Dubois, L. G., McCarthy, J. J., Tillmann, H. L., Thompson, A., Shire, N. J., Hendrickson, R., Dieguez, F., Goldman, P. S., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-74 · Cited by 14
“In addition to these algorithms, there are ANOVA approaches for protein quantitation [11] and differential expression [12,13].”
MethodPeptide filtering differently affects the performances of XIC-based quantification methods
Belouah, I., Blein‐Nicolas, M., Balliau, T., Gibon, Y., Zivy, M., & Colombié, S. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.10.003 · Cited by 14
Wryk, G., Gawor, A., & Bulska, E. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413491 · Cited by 13
“%) were used to verify the thesis about the effectiveness of left-censored methods for non-random missing values [7] and to indicate the group of methods that performs best in the opposite case.”
InfluentialA Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data
Huang, Z., & Wang, C. (2022) · Metabolites · doi:10.3390/metabo12040305 · Cited by 15
The proteomic landscape of glioma stem-like cells
Lichti, C. F., Wildburger, N. C., Shavkunov, A. S., Mostovenko, E., Liu, H., Sulman, E. P., & Nilsson, C. L. (2015) · EuPA Open Proteomics · doi:10.1016/j.euprot.2015.06.008 · Cited by 12
Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology
Franks, A. M., Csárdi, G., Drummond, D. A., & Airoldi, E. M. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.964404 · Cited by 11
“Here, a two-stage missingness mechanism, capturing both informative and noninformative censoring may be more appropriate (Karpievitch et al. 2009).”
Song, X., Lichti, C. F., Townsend, R. R., & Mueckler, M. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068516 · Cited by 12
BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples
Mallikarjun, V., Richardson, S. M., & Swift, J. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00468 · Cited by 10
Hui, H. W. H., Chan, W. X., & Goh, W. W. B. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf168 · Cited by 13
Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue
Sharman, K., Patterson, N. H., Weiss, A., Neumann, E. K., Guiberson, E. R., Ryan, D. J., Gutierrez, D. B., Spraggins, J. M., de Plas, R. V., Skaar, E. P., et al. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00206 · Cited by 11
Metadata
- Authors
- Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., Heffron, F., Metz, T. O., Qian, W.-J., Yoon, H., Smith, R. D., & Dabney, A. R.
- Affiliations
- Pacific Northwest National Laboratory (US)
- 1 Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, 2Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 and 3Molecular Microbiology and Immunology, Oregon Health and Science University, Mail Code L220, Portland, OR 97201, USA
- Corresponding author
- Dabney, Alan R.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-06-17
- Volume
- 25
- Issue
- 16
- Pages
- 2028-2034
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2723007
- OpenAlex
- W2125419939
- Cited by
- 176
- References
- 29
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 9 · 2023: 8 · 2022: 12 · 2021: 11 · 2020: 8 · 2019: 10 · 2018: 8 · 2017: 10 · 2016: 10 · 2015: 14 · 2014: 20 · 2013: 12 · 2012: 19
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health (DK070146)
- National Cancer Institute (R25 CA90301, R25CA090301)
- National Institute of Allergy and Infectious Diseases (Y1 AI 489401, R01AI022933)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK070146, R21DK070146, R33DK070146)
- Pacific Northwest National Laboratory
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein quantification, shotgun proteomics, label-free quantification, informative missingness, Dante
- MeSH terms
- Proteins, Mass Spectrometry, Models, Statistical, Proteome, Databases, Protein, Proteomics
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