prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Wolski, Witold; Nanni, Paolo; Grossmann, Jonas; D’Errico, M.; Schlapbach, Ralph; Panse, Christian

doi:10.1101/2022.06.07.494524

Cited by 16 publications

(13 citation statements)

References 59 publications

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“…Our results are in line with Wolski et al, who suggest that statistical models of differential expression that do not impute, but rather explicitly model missingness, tend to outperform traditional models. 52 As we performed differential expression analysis on only three data sets, we do not claim our results will generalize to all proteomics data. Instead, our results suggest that researchers should empirically evaluate whether imputation improves accuracy of their differential expression analysis on a case-bycase basis, using procedures similar to the one we introduce here.…”

Section: Discussionmentioning

confidence: 91%

Evaluating Proteomics Imputation Methods with Improved Criteria

Harris,

Fondrie,

et al. 2023

J. Proteome Res.

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Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, “downstream-centric” criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

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Section: Discussionmentioning

confidence: 91%

Evaluating Proteomics Imputation Methods with Improved Criteria

Harris,

Fondrie,

et al. 2023

J. Proteome Res.

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“…This can prevent researchers from refining a workflow to fit their specific needs. Finally, the majority of proteomics workflows utilise or structures which limits their traceability, as is the case for , and 54 – 56 …”

Section: Discussionmentioning

confidence: 99%

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Hutchings,

Dawson,

Krueger

et al. 2023

F1000Res

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Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

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“…To investigate differentially expressed proteins for the high−low diet contrast, we fitted a mixed effects model with the normalized abundances as the response variable, diet and isoline as fixed effects and biological replicates as a random effect, using the build_model function ( prolfqua package [59]). To test for diet-dependent enrichment of genes that encode ejaculate proteins, we ranked proteins by their t -statistics obtained from the high−low diet contrasts.…”

Section: Methodsmentioning

confidence: 99%

Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome

Zeender,

Pfammatter,

Roschitzki

et al. 2023

Proc. R. Soc. B.

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Ejaculate proteins are key mediators of post-mating sexual selection and sexual conflict, as they can influence both male fertilization success and female reproductive physiology. However, the extent and sources of genetic variation and condition dependence of the ejaculate proteome are largely unknown. Such knowledge could reveal the targets and mechanisms of post-mating selection and inform about the relative costs and allocation of different ejaculate components, each with its own potential fitness consequences. Here, we used liquid chromatography coupled with tandem mass spectrometry to characterize the whole-ejaculate protein composition across 12 isogenic lines of Drosophila melanogaster that were reared on a high- or low-quality diet. We discovered new proteins in the transferred ejaculate and inferred their origin in the male reproductive system. We further found that the ejaculate composition was mainly determined by genotype identity and genotype-specific responses to larval diet, with no clear overall diet effect. Nutrient restriction increased proteolytic protein activity and shifted the balance between reproductive function and RNA metabolism. Our results open new avenues for exploring the intricate role of genotypes and their environment in shaping ejaculate composition, or for studying the functional dynamics and evolutionary potential of the ejaculate in its multivariate complexity.

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prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Cited by 16 publications

References 59 publications

Evaluating Proteomics Imputation Methods with Improved Criteria

Evaluating Proteomics Imputation Methods with Improved Criteria

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome

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