TL;DR
- Flow cytometry gating is still manual, variable, and difficult to scale.
- Static rules struggle with experimental variability, which makes them hard to maintain in practice.
- Machine learning allows gating strategies to be learned from expert data and applied consistently across experiments.
- When implemented in a validated, traceable workflow that keeps scientists in control, Machine Learning turns gating into a scalable and reproducible process.
Flow cytometry gating remains one of the most manual steps in an otherwise highly advanced analytical workflow. Scientists still rely on visual interpretation to define cell populations, drawing boundaries based on experience and judgment.
The Problem with Manual Gating
This need for visual interpretation makes gating inherently subjective, as results depend on who performs the analysis and when it is done. Two analysts can interpret the same dataset slightly differently, and even the same analyst may apply different boundaries over time. Because of this, gating often requires a second-person review to ensure consistency and correctness. Although this is necessary, this also adds additional workload and introduces delays into the workflow.
Additionally, flow cytometry data is inherently variable, as differences in instrument settings, reagents, sample quality, and biology mean that no two datasets look the same. This means that a gating strategy that fits one experiment rarely transfers perfectly to the next.
As data volumes increase, this way of working becomes difficult to scale. More samples mean more manual gating, more reviewing, and more coordination between people. What starts as a careful, expert-driven process gradually becomes a bottleneck that limits throughput and slows decision-making. So how can laboratories strategically reduce workload, while also maintaining reliable results?
Why Machine Learning Fits This Problem
A more effective approach is to capture how experts gate data and allow a model to learn from those examples. Machine learning models can recognize patterns across datasets, adapt to variation, and apply gating strategies consistently across experiments.
This is where machine learning becomes genuinely useful in practice. At DataChaperone, we developed a module that allows scientists to train, evaluate, and deploy machine learning models to automate gating, without requiring advanced technical expertise to operate it.
In practice, this process works best as a collaboration between scientists and data scientists. Scientists define the gating strategy and provide the domain expertise and labeled data, while data scientists support model configuration, optimization, and evaluation. Together, they iteratively improve the model based on real experimental data and apply the best performing model to each project.
Figure: Machine learning for flow cytometry gating.
Left: Problem map of flow cytometry gating. The problem variability and input complexity exclude simple code and rules as a solution. The need for interpretability favors ML over Deep Learning and LLMs.
Right: Scatter plot comparing model predictions with the gates drawn by experts. The labels true positive (TP – green), false positive (FP – red), true negative (TN – grey), and false negative (FN – orange) visualize model performance. The concordance is 99,6%.
Bringing Machine Learning into the Workflow
The workflow has two distinct phases: model training and prediction.
Training begins by configuring a gating strategy: specifying which gates to include, and setting relevant hyperparameters such as the number of training epochs and context events. The platform tracks all configuration versions, highlights what changed between them, and records which versions were approved or rejected. This makes it easy to iterate deliberately rather than lose track of what was tried.
The model learns from labeled data where gates were previously defined by experts. A subset of the data is used for testing, so performance can be evaluated on experiments the model has never seen. After training, the platform automatically generates evaluation results for each gate, including visualizations that show exactly where the model agrees with manual gating and where it does not. Multiple training runs can be compared side by side, making it straightforward to select the best-performing model before approving it for use.
Prediction is equally straightforward. Scientists select a project configuration, upload their FCS files, and start the prediction. Results are presented as summary tables, scatter plots, and predicted gating outcomes for each sample. Crucially, every result is fully traceable: the platform always records which model version was used, which configuration was applied, and which data was analyzed.
This way of working is designed to reflect how machine learning should be applied in the lab: as a structured, transparent process that captures and scales scientific expertise. DataChaperone facilitates this workflow, enabling CRO, CDMO, biotech and pharma teams to collaborate effectively and remain in control of their models, rather than depending on a vendor to operate or maintain them.
This makes it possible to adapt quickly when experimental conditions change, without relying on external support or reimplementation of the workflow.
The Impact on the Lab
This is how Machine Learning AI standardizes a step that is traditionally manual and variable. Once the model is properly trained and validated, automated gating can be applied consistently without requiring a second-person review for routine experiments. This reduces review workload and can shorten turnaround times by days, and reduces manual lab errors. By implementing this approach, scientists can spend less time on repetitive manual work and more time on interpretation and decision-making.
When gating thus becomes consistent and automated, the benefits extend beyond this single step. Results become more reproducible and easier to compare across experiments. Data quality improves, supporting more reliable downstream analysis and reporting.
Want to find out how DataChaperone can help automate gating and other data analysis workflows in your lab? Contact us.
