Useful Publications

References appearing in blog articles will be collected here and organized into topics.

Compensation and unmixing

Ferrer-Font et al. showing side-by-side unmixing with bead or cell controls.
Konecny et al. using only cell controls for 50-color flow.
Liechti et al. using cell controls where necessary to prevent errors (see supplementary figure S3B).
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."
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".
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."
CaptureBody enables accurate unmixing for spectral flow cytometry (Zambidis et al). Turn your (immune) cells into bead-like controls. Highly recommended. Works great.

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

Overviews and benchmarks

Gate-based tools

OpenCyto (Finak et al. 2014, PLOS Computational Biology): Gating templates, with gates recalculated per sample.
flowDensity (Malek et al. 2015, Bioinformatics): Density-based reproduction of manual gating.
An introduction to automated flow cytometry gating tools and their implementation (Verschoor et al. 2015, Front Immunol): Introduces FLOCK.
AutoGate: automating analysis of flow cytometry data (Meehan et al. 2014, Immunol Res)

Machine-learning approaches

ElastiGate (Irvine et al. 2025, Scientific Reports): Gate transfer by elastic image registration.
Automated analysis of flow cytometry data with minimal training files (Irvine et al. 2025, Cytometry B): Clinical evaluation of ElastiGate.
UNITO (Chen et al. 2025, Nature Communications): Gating as image segmentation.
GateNet (Fisch et al. 2024, Computers in Biology and Medicine):
CyGate (Na et al. 2023, Analytical Chemistry)
CytoPy: an autonomous cytometry analysis framework (Burton et al. 2021, PLOS Computational Biology). Not the same Burton.
FLAG-X: towards automated gating of clinical flow cytometry data (2026, bioRxiv preprint)

FlowSOM (Van Gassen et al. 2015, Cytometry A).
Analyzing high-dimensional cytometry data using FlowSOM (Nature Protocols)
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
Leiden (Traag, Waltman and van Eck 2019, Scientific Reports): Improves on the Louvain algorithm used in the original Phenograph.
HDBSCAN (Campello, Moulavi and Sander 2013, PAKDD): Density-based, with a noise label. Also automatically determines cluster number.

Cluster annotation

MEM, marker enrichment modeling (Diggins et al. 2017, Nature Methods):
ScType (Ianevski, Giri and Aittokallio 2022, Nature Communications):
Cellscope A fee-for-service platform that provides cluster annotation for flow cytometry data.

limma (Ritchie et al. 2015, Nucleic Acids Research)
Linear models and empirical Bayes methods (Smyth 2004, Stat Appl Genet Mol Biol)
TREAT: testing relative to a threshold (McCarthy and Smyth 2009, Bioinformatics)
Selective inference on multiple families of hypotheses (Benjamini and Bogomolov 2014, J R Stat Soc B)
edgeR (Robinson, McCarthy and Smyth 2009, Bioinformatics): Negative-binomial models for counts.
diffcyt (Weber et al. 2019, Communications Biology)
The ASA statement on p-values (Wasserstein and Lazar 2016, The American Statistician)
Statistical errors (Nuzzo 2014, Nature)
The new statistics and a Bayesian alternative (Kruschke and Liddell 2018, Psychon Bull Rev)

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