MSqRob takes the missing hurdle: uniting intensity- and count-based proteomics

Goeminne, Ludger; Sticker, Adriaan; Martens, Lennart; Gevaert, Kris; Clement, Lieven

doi:10.1101/782466

Cited by 11 publications

(17 citation statements)

References 22 publications

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“…A solution to this issue is to explicitly model the protein expression and the protein detection rate. The hurdle model is very compelling in this regard [37]. The hurdle model consist of two components.…”

Section: Batch Effects and Data Missingness Are Not Independentmentioning

confidence: 99%

Replication of single-cell proteomics data reveals important computational challenges

Vanderaa

Gatto

2021

Expert Review of Proteomics

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Introduction Mass spectrometry-based proteomics is actively embracing quantitative, singlecell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses.Areas covered This special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs.We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses. Expert opinionReplicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges.

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Section: Batch Effects and Data Missingness Are Not Independentmentioning

confidence: 99%

Replication of single-cell proteomics data reveals important computational challenges

Vanderaa

Gatto

2021

Expert Review of Proteomics

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“…Even more importantly, this also introduces chemical noise that interferes with quantification of important hPTMs [25] , [26] , [27] . Several biological hPTMs are affected by propionylation such as (i) propionyl groups can be chemical or biological in origin, undermining correct biological interpretation, (ii) methylated Ks are propionylated to butyryl, rendering methyl and butyryl indistinguishable [26] , and (iii) propionylation can also introduce formylation, again undermining correct biological interpretation [28] . Unfortunately, no good alternative for chemical derivatization has been proposed to this date for the study of histones .…”

Section: Side Notesmentioning

confidence: 99%

“…This is a novel, highly specific protease that does not cleave after K and thus eliminates the need for propionylation. Consequently, this workflow yields many benefits: (i) a simplified workflow with reduced technical variation [18] , [19] , [20] , (ii) an increased LC-resolution for similarly modified peptidoforms, thus reducing ambiguity [20 , 29] , (iii) less complex peptides, allowing better predictions, and (iv) a reduced chemical noise [25] , [26] , [27] .…”

Section: Side Notesmentioning

confidence: 99%

Comprehensive histone epigenetics: A mass spectrometry based screening assay to measure epigenetic toxicity

et al. 2020

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Evidence of the involvement of epigenetics in pathologies such as cancer, diabetes, and neurodegeneration has increased global interest in epigenetic modifications. For nearly thirty years, it has been known that cancer cells exhibit abnormal DNA methylation patterns. In contrast, the large-scale analysis of histone post-translational modifications (hPTMs) has lagged behind because classically, histone modification analysis has relied on site specific antibody-based techniques. Mass spectrometry (MS) is a technique that holds the promise to picture the histone code comprehensively in a single experiment. Therefore, we developed an MS-based method that is capable of tracking all possible hPTMs in an untargeted approach. In this way, trends in single and combinatorial hPTMs can be reported and enable prediction of the epigenetic toxicity of compounds. Moreover, this method is based on the use of human cells to provide preliminary data, thereby omitting the need to sacrifice laboratory animals. Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort. Still, this novel toxicoepigenetic assay and the data it generates holds great potential for, among others, pharmaceutical industry, food science, clinical diagnostics and, environmental toxicity screening. There is a growing interest in epigenetic modifications, and more specifically in histone post-translational modifications (hPTMs). We describe an MS-based workflow that is capable of tracking all possible hPTMs in an untargeted approach that makes use of human cells. Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort.

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“…However, this approach suffers from many missing values at the peptide or protein level, which significantly reduces the amount of quantifiable proteins with an average of 44% missing values [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment and Outlook on Methods for Imputing Proteomics Data

Chang

Chao

Parker

et al. 2021

Preprint

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Background: Missing values are a major issue in quantitative proteomics data analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, comparative assessment on the accuracy of existing methods remains inconclusive, mainly because the true missing mechanisms are complex and the existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of current and future development.Results: We first report an assessment of eight representative methods collectively targeting three typical missing mechanisms. The selected methods are compared on both realistic simulation and real proteomics datasets, and the performance is evaluated using three quantitative measures. We then discuss fused regularization matrix factorization, a popular low-rank matrix factorization framework with similarity and/or biological regularization, which is extendable to integrating multi-omics data such as gene expressions or clinical variables. We further explore the potential application of convex analysis of mixtures, a biologically-inspired latent variable modeling strategy, to missing value imputation. The preliminary results on proteomics data are provided together with an outlook into future development directions.Conclusion: While a few winners emerged from our comparative assessment, data-driven evaluation of imputation methods is imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Imputation accuracy may vary with signal intensity. Fused regularization matrix factorization provides a possibility of incorporating external information. Convex analysis of mixtures presents a biologically plausible new approach.

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MSqRob takes the missing hurdle: uniting intensity- and count-based proteomics

Cited by 11 publications

References 22 publications

Replication of single-cell proteomics data reveals important computational challenges

Replication of single-cell proteomics data reveals important computational challenges

Comprehensive histone epigenetics: A mass spectrometry based screening assay to measure epigenetic toxicity

Comparative Assessment and Outlook on Methods for Imputing Proteomics Data

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