ArticleJournal of proteome research2010
Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data.
Article in Journal of proteome research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
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- Multi-Omic Profiling Identifies Conserved Metabolic Pathways Critical for SARS-CoV-2 Variants Infection.bioRxiv : the preprint server for biology · 2025Article
- Proteomic characterization ofmSystems · 2025Article
- Multi-platform omics analysis of Nipah virus infection reveals viral glycoprotein modulation of mitochondria.Cell reports · 2025Article
- Advanced multi-modal mass spectrometry imaging reveals functional differences of placental villous compartments at microscale resolution.Nature communications · 2025Article
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- A compendium of multi-omics data illuminating host responses to lethal human virus infections.Scientific data · 2024Article
- Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.bioRxiv : the preprint server for biology · 2023Article
- Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts.Microbial cell factories · 2023Article
- PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.Nature communications · 2023Article
- Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections.Heliyon · 2023Article
- Itaconic acid production is regulated by LaeA inMetabolic engineering communications · 2022Article
- Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.PLoS computational biology · 2022Article
- Proteomic Profile of Procoagulant Extracellular Vesicles Reflects Complement System Activation and Platelet Hyperreactivity of Patients with Severe COVID-19.Frontiers in cellular and infection microbiology · 2022Article
- Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing observations in LC-MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error or nonrandom mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values across experimental groups. We pair the G-test results, evaluating independence of missing data (IMD) with an analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use three LC-MS data sets to demonstrate the robustness and sensitivity of the IMD-ANOVA approach.
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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.