ReviewBMC bioinformatics2012
Statistical methods for quantitative mass spectrometry proteomic experiments with labeling.
Review in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Identification of Candidate Biomarkers in Malignant Ascites from Patients with Hepatocellular Carcinoma by iTRAQ-Based Quantitative Proteomic Analysis.BioMed research international · 2018Trial
- Identification of menstrual blood markers by proteomic mass spectrometry.International journal of legal medicine · 2026Article
- Cellular ATP redistribution achieved by deleting Tgparp improves lignocellulose utilization of Trichoderma under heat stress.Biotechnology for biofuels and bioproducts · 2024Article
- Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.Journal of proteins and proteomics · 2024Article
- Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.BMC medicine · 2023Review
- Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.Journal of neurochemistry · 2023Article
- Exploring the multi-level regulation of lignocellulases in the filamentous fungus Trichoderma guizhouense NJAU4742 from an omics perspective.Microbial cell factories · 2022Article
- RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.Bioinformatics (Oxford, England) · 2021Article
- Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer.Cancers · 2020Review
- A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins.Scientific data · 2020Article
- Article
- Proteomic analysis reflects an environmental alkalinization-coupled pH-dependent mechanism of regulating lignocellulases inBiotechnology for biofuels · 2020Article
- Serum Proteomics on the Basis of Discovery of Predictive Biomarkers of Response to Androgen Deprivation Therapy in Advanced Prostate Cancer.Clinical genitourinary cancer · 2019Article
- AAV9-Molecular therapy. Methods & clinical development · 2019Article
- Label-free Quantitative Analysis of Protein Expression Alterations in miR-26a-Knockout HeLa Cells using SWATH-MS Technology.Scientific reports · 2019Article
- Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery.Proteomes · 2018Review
- Bathing in carbon dioxide-enriched water alters protein expression in keratinocytes of skin tissue in rats.International journal of biometeorology · 2017Article
- Quantitative Phosphoproteomic Analysis of T-Cell Receptor Signaling.Methods in molecular biology (Clifton, N.J.) · 2017Article
- CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization.Molecular & cellular proteomics : MCP · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.
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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.