ReviewAnnual review of biomedical data science2023
Computational Methods for Single-Cell Proteomics.
Review in Annual review of biomedical data science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 11 citations in OpenAlex.
- T cell immunomonitoring: a comparative analysis of traditional and novel methods to quantify and characterize human antigen-specific T cells.Nature protocols · 2026Review
- omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.Nature communications · 2026Article
- Navigating the Landscape of Cytometry-Based Single-Cell Proteomics: Quantification, Annotation, and Resources.International journal of molecular sciences · 2026Review
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphoma.Nature communications · 2025Article
- Spatial proteomics for investigating solid tumor resistance mechanisms.Cancer metastasis reviews · 2025Review
- Single-cell proteomics using mass spectrometry.Cell genomics · 2025Review
- Liquid Chromatographic and Mass Spectrometric Methods for Quantitative Proteomic Analysis from Single-Cell and Nanogram-Level Samples.Analytical chemistry · 2025Article
- A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease.Annals of clinical and translational neurology · 2024Article
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 at 4 institutions in 1 country.
Funding
Abstract
Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these 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.