ReviewProteomics2015
Visualization of proteomics data using R and bioconductor.
Review in Proteomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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
35 citing papers in PubMed.
- Aberrant long-chain fatty acids metabolism and its interplay with immuno-inflammatory responses in relapsing-remitting multiple sclerosis.Frontiers in immunology · 2026Article
- Peripheral metabolomic profiling reveals lipid and amino acid alterations associated with immuno-inflammatory responses in treatment-naïve late-onset Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- Metabolic profiling reveals potential biomarkers and underlying signaling pathways involved in mindfulness-based cognitive therapy-improved adolescent depression symptoms.EXCLI journal · 2026Article
- Discovering promising drug candidates for Parkinson's disease: integrating miRNA and DEG analysis with molecular dynamics and MMPBSA.Journal of computer-aided molecular design · 2025Article
- Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models.Frontiers in veterinary science · 2025Article
- Quantitative proteomics and multi-omics analysis identifies potential biomarkers and the underlying pathological molecular networks in Chinese patients with multiple sclerosis.BMC neurology · 2024Article
- Lipoteichoic Acid fromNutrients · 2024Article
- TMT quantitative proteomics reveals key proteins relevant to microRNA-1-mediated regulation in osteoarthritis.Proteome science · 2023Article
- LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.Journal of proteome research · 2023Article
- Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER.Journal of proteome research · 2023Article
- Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.Cancers · 2023Review
- Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Bridging data management platforms and visualization tools to enable ad-hoc and smart analytics in life sciences.Journal of integrative bioinformatics · 2022Article
- High-throughput proteomics: a methodological mini-review.Laboratory investigation; a journal of technical methods and pathology · 2022Review
- Scaling Up Single-Cell Proteomics.Molecular & cellular proteomics : MCP · 2022Article
- Characteristics of intestinal microbiota in C57BL/6 mice with non-alcoholic fatty liver induced by high-fat diet.Frontiers in microbiology · 2022Article
- Proteome Discoverer-A Community Enhanced Data Processing Suite for Protein Informatics.Proteomes · 2021Review
- Circadian Genes as Exploratory Biomarkers in DMD: Results From Both theFrontiers in physiology · 2021Article
- Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics.Frontiers in immunology · 2021Article
- Subcellular proteomics.Nature reviews. Methods primers · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.
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.