Useful Publications
References appearing in blog articles will be collected here and organized into topics.
Compensation and unmixing
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exaexa/panelbuilder: Panel optimization and unmixing tool for multicolor and spectral cytometry
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Unmixing (David Novo et al.)
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Compensation-induced spread (Mario Roederer)
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Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats (Mario Roederer)
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Compensation in Flow Cytometry (Mario Roederer)
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Improved Compensation in Flow Cytometry by Multi-Variable Optimization (Sugár et al.)
Single color controls: beads or cells
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Ferrer-Font et al. showing side-by-side unmixing with bead or cell controls.
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Konecny et al. using only cell controls for 50-color flow.
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Liechti et al. using cell controls where necessary to prevent errors (see supplementary figure S3B).
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Bhowmick et al. comparing different beads. "It is abundantly clear that bead-based correction produces suboptimal results when applied to cells, compared with cell-based controls applied to cells."
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A practitioner’s view of spectral flow cytometry (Sharma et al.). "We highly recommend the use of cells over beads as single-color controls for spectral unmixing".
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Fluorochrome-dependent specific changes in spectral profiles using different compensation beads or primary cells in full spectrum cytometry (Shevchenko et al.). "While most fluorochromes show the same spectral profile on beads and cells, we demonstrate that specific fluorochromes show a significantly different spectral profile depending on which type of compensation beads is used, and some fluorochromes should be used on cells exclusively."
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CaptureBody enables accurate unmixing for spectral flow cytometry (Zambidis et al). Turn your (immune) cells into bead-like controls. Highly recommended. Works great.
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
- UWCCC protocol
- The Power of Reagent Titration (Bonilla et al.)
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Batch correction
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CD3-relativisation approach (Glehr et al., 2026). Check it out if you're starting a new study. This appears to be a very powerful technique.
- cyCombine publication
- cyCombine code
- CytoNorm publication
- CytoNorm code
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Scaling and transforms
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Optimizing transformations for automated, high throughput analysis of flow cytometry data (Finak et al. 2010).
- A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data (Parks, Roederer and Moore 2006, Cytometry A)
- Scaling data in FlowJo (documentation)
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Automated Gating
Overviews and benchmarks
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Current trends in flow cytometry automated data analysis software (Cheung et al. 2021, Cytometry A). Surveys the tools.
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Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data (Liu et al. 2025, Briefings in Bioinformatics). Benchmarks manual gating, unsupervised clustering and auto-gating.
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Flow cytometry data analysis: recent tools and algorithms (Montante and Brinkman 2019, Int J Lab Hematol)
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Recent advances in computer-assisted algorithms for cell subtype identification of cytometry data (Liu et al. 2020, Front Cell Dev Biol):
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Computational flow cytometry: helping to make sense of high-dimensional immunology data (Saeys, Van Gassen and Lambrecht 2016, Nature Reviews Immunology)
Gate-based tools
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OpenCyto (Finak et al. 2014, PLOS Computational Biology): Gating templates, with gates recalculated per sample.
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flowDensity (Malek et al. 2015, Bioinformatics): Density-based reproduction of manual gating.
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An introduction to automated flow cytometry gating tools and their implementation (Verschoor et al. 2015, Front Immunol): Introduces FLOCK.
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AutoGate: automating analysis of flow cytometry data (Meehan et al. 2014, Immunol Res)
Machine-learning approaches
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ElastiGate (Irvine et al. 2025, Scientific Reports): Gate transfer by elastic image registration.
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Automated analysis of flow cytometry data with minimal training files (Irvine et al. 2025, Cytometry B): Clinical evaluation of ElastiGate.
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UNITO (Chen et al. 2025, Nature Communications): Gating as image segmentation.
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GateNet (Fisch et al. 2024, Computers in Biology and Medicine):
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CyGate (Na et al. 2023, Analytical Chemistry)
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CytoPy: an autonomous cytometry analysis framework (Burton et al. 2021, PLOS Computational Biology). Not the same Burton.
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FLAG-X: towards automated gating of clinical flow cytometry data (2026, bioRxiv preprint)
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Clustering
Clustering
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FlowSOM (Van Gassen et al. 2015, Cytometry A).
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Analyzing high-dimensional cytometry data using FlowSOM (Nature Protocols)
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Phenograph (Levine et al. 2015, Cell). Picks out how many clusters there "should" be. See also the blog post: https://www.colibri-cytometry.com/post/the-peculiarities-of-phenograph
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Leiden (Traag, Waltman and van Eck 2019, Scientific Reports): Improves on the Louvain algorithm used in the original Phenograph.
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HDBSCAN (Campello, Moulavi and Sander 2013, PAKDD): Density-based, with a noise label. Also automatically determines cluster number.
Cluster annotation
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MEM, marker enrichment modeling (Diggins et al. 2017, Nature Methods):
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ScType (Ianevski, Giri and Aittokallio 2022, Nature Communications):
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Cellscope A fee-for-service platform that provides cluster annotation for flow cytometry data.
Antibody titration
- Combinatorial Antibody Titrations (Burns et al.)
Differential abundance statistics
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limma (Ritchie et al. 2015, Nucleic Acids Research)
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Linear models and empirical Bayes methods (Smyth 2004, Stat Appl Genet Mol Biol)
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TREAT: testing relative to a threshold (McCarthy and Smyth 2009, Bioinformatics)
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Selective inference on multiple families of hypotheses (Benjamini and Bogomolov 2014, J R Stat Soc B)
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edgeR (Robinson, McCarthy and Smyth 2009, Bioinformatics): Negative-binomial models for counts.
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diffcyt (Weber et al. 2019, Communications Biology)
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The ASA statement on p-values (Wasserstein and Lazar 2016, The American Statistician)
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Statistical errors (Nuzzo 2014, Nature)
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The new statistics and a Bayesian alternative (Kruschke and Liddell 2018, Psychon Bull Rev)
Dimensionality reduction
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Generalized EmbedSOM on quadtree-structured self-organizing maps (Kratochvil et al. 2019, F1000Research)
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How to prepare spectral flow cytometry datasets for high dimensional data analysis
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Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data (Linderman et al. 2019, Nature Methods)
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openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (Poličar, Stražar and Zupan 2024, J Stat Softw)
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PaCMAP: Understanding how dimension reduction tools work (Wang et al. 2021, JMLR)
If you have any papers you found useful that aren't covered, please suggest them. New topics are also welcome.