ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
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
Single-cell sequencing is revolutionizing biological research by enabling unprecedented cellular resolution, yet traditional multi-sample experiments are often constrained by high costs and batch effects. Sample multiplexing offers a critical solution by uniquely tagging individual cells from diverse samples for pooled sequencing, thereby dramatically boosting throughput and improving data reliability by minimizing technical variability. This review provides a comprehensive and integrated perspective on the rapidly evolving field of single-cell multiplexing. Major experimental strategies and the critical computational algorithms required for accurate sample deconvolution are surveyed, highlighting the crucial link between experimental design and computational accuracy. Furthermore, the diverse applications of these technologies in large-scale clinical cohorts, multi-omics integration, developmental biology, and high-throughput drug screening are summarized. This review serves as an essential guide for researchers, empowering them to select the most appropriate methods to accelerate discoveries in disease mechanisms, therapeutic responses, and developmental biology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.