ArticleBMC bioinformatics2019
ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- Replicability of bulk RNA-Seq differential expression and enrichment analysis results for small cohort sizes.PLoS computational biology · 2025Article
- Article
- Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates.Computational and structural biotechnology journal · 2025Article
- Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.bioRxiv : the preprint server for biology · 2024Article
- A DNA-PK phosphorylation site on MET regulates its signaling interface with the DNA damage response.Oncogene · 2023Article
- An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies.Cellular and molecular life sciences : CMLS · 2022Article
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundData from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis.
resultsWe propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method.
conclusionsThe new method for the differential expression analysis of proteomic data is available as an easy to use Python package.
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