ArticleProteomics2026
Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.
Article in Proteomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
MS-based proteomics offers powerful opportunities for biomarker discovery; nevertheless, it is associated with technical challenges, including missing values and batch effects. Although imputation and batch-correction methods are well established in proteomics, their impact remains incompletely characterized in large-scale clinical proteomics datasets. Here, we examine the practical impact and interaction of three popular imputation methods (Gaussian, ½ LOD, KNN) in combination with three batch-effect correction approaches (ComBat, ComBat with disease covariate, MNN) on differential abundance analysis in a CE-MS urine peptidomics dataset of 1,050 samples across 13 batches from chronic kidney disease (CKD) patients and controls. Downstream effects were assessed based on peptide validation between discovery and validation sets. Imputation method choice had minimal impact on the final list of disease-associated peptides (DAPs), given the missingness structure and normalization strategy. In contrast, batch-effect correction largely affected the results: MNN and especially unadjusted ComBat removed a large proportion of DAPs (
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.