ReviewMolecular systems biology2021
Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.
Review in Molecular systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 2 of them syntheses that pooled it.
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
93 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments.Nature methods · 2023Guideline
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- Preprocessing Large-Scale Proteomics Data.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Urban PMMolecular neurobiology · 2026Article
- Review
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Article
- Food-dependent activation of one-carbon metabolism during heat shock in Mytilus californianus.The Journal of experimental biology · 2026Article
- A tumor profiling resource for ovarian cancer: insights into chemotherapy-driven heterogeneity and personalized treatment strategy.Nature communications · 2026Article
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Clarifying the scope and capabilities of ROTS in differential expression analysis.Bioinformatics (Oxford, England) · 2026Article
- How Polyphenol Metabolites Spatiotemporally Reprogram Transcription Factors and Human Proteostasis: A Metabolite-Centric Framework.Current issues in molecular biology · 2026Review
- Loss of meningothelial identity and mesenchymal fate switching in NF2-mutant meningiomas.Acta neuropathologica · 2026Article
- Review
- Genetic architecture of the murine red blood cell proteome reveals central role of hemoglobin beta cysteine 93 in maintaining redox balance.Cell genomics · 2026Article
- Identification of menstrual blood markers by proteomic mass spectrometry.International journal of legal medicine · 2026Article
- Immune Ageing Clocks: A Methods-Oriented Review of Tasks, Modalities, Models, and Recalibration.Cells · 2026Review
- A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch.Frontiers in microbiology · 2026Article
- Proteomics of aging: biomarkers for physiological systems and diseases.Frontiers in aging · 2026Review
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.