ArticleScientific reports2018
QFMatch: multidimensional flow and mass cytometry samples alignment.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- VoxelCoder: Classification of human cellular phenotypes via autoencoder batch alignment and hyperdimensional representation of cytometry data.Patterns (New York, N.Y.) · 2026Article
- Automatic phenotyping using exhaustive projection pursuit.Communications biology · 2025Article
- Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.GigaScience · 2025Article
- Visualizing scRNA-Seq data at population scale with GloScope.Genome biology · 2024Article
- Visualizing scRNA-Seq Data at Population Scale with GloScope.bioRxiv : the preprint server for biology · 2024Article
- New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.Patterns (New York, N.Y.) · 2021Article
- JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.PLoS computational biology · 2021Article
- The Route of Vaccine Administration Determines Whether Blood Neutrophils Undergo Long-Term Phenotypic Modifications.Frontiers in immunology · 2021Article
- optimalFlow: optimal transport approach to flow cytometry gating and population matching.BMC bioinformatics · 2020Article
- diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.Communications biology · 2019Article
- Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.Communications biology · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.
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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.