ArticleInternational journal of molecular sciences2022
Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Fully synthetic replication of complex real biological cell clusters using a novel cluster-based 'Rosetta-Routine' computational modelling process.PLoS computational biology · 2026Article
- Review
- Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization.Journal of immunology (Baltimore, Md. : 1950) · 2024Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- CAYSS: Package for Automatic Cytometry Analysis of Yeast Spore Segregation.Yeast (Chichester, England) · 2024Review
- CAR-T cell manufacturing landscape-Lessons from the past decade and considerations for early clinical development.Molecular therapy. Methods & clinical development · 2024Review
- Human leucocytes processed by fast-rate inertial microfluidics retain conventional functional characteristics.Journal of the Royal Society, Interface · 2024Article
- CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.BMC bioinformatics · 2024Article
- Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy.Frontiers in oncology · 2023Review
- Artificial intelligence and machine learning-assisted digital applications for biopharmaceutical manufacturing.Biotechnology progressReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based,
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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.