Beyond Manual Gating: A Practical Guide to Modern Flow Cytometry Data Analysis
The article discusses the growing challenges in flow cytometry data analysis due to massive data volumes and increasing dimensionality from advanced cytometers, highlighting that traditional manual gating methods are becoming unsustainable and emphasizing the need for modern, automated analytical approaches to efficiently process and interpret complex, high-dimensional flow cytometry datasets.
Flow cytometry is an essential lab technique that uses lasers to rapidly evaluate high volumes of cells against multiple parameters. It has broad application in scientific research, clinical analysis, and treatment development. The process results in a huge amount of raw data that must be turned into actionable insights. While this has long been done through a manual gating process that progressively homes in on cell populations of interest, some key challenges are making this classical analysis approach largely unsustainable.
Key Challenges in Flow Cytometry Data Analysis
A big hurdle in using flow cytometry is turning the plentiful data coming from the tests into actionable insights. A few notable challenges stand in the way.
Challenge 1: Flow Cytometry Data Volumes
Flow cytometry data volumes can be massive. For example, in clinical trials where multiple parameters are assessed in large patient populations, each trial may have several assays completed for thousands of patients. This results in an incredible amount of raw data that needs to be quality controlled and analyzed to identify immune cells of interest. Without the right software tools, this process can be incredibly tedious.
Challenge 2: Dimensional Advances in Flow Cytometers
Flow cytometers have historically collected light using detectors for specific individual wavelengths, but recent advances have greatly improved wavelength sensitivity and increased the number of available fluorescent reagents. Output datasets are increasing exponentially. Automated analysis technology has evolved from analyzing cells in five dimensions in 1985 to about 40 dimensions by 2020, with expectations to exceed 100 dimensions as full spectral flow cytometry becomes more prevalent. The number of pairwise plots to assess becomes too cumbersome for manual analysis, making manual processes unmanageable as cytometers evolve.
Limitations of Manual Flow Cytometry Gating and Data Analysis
Manual flow cytometry data analysis and gating is not sustainable in a multiparametric, multidimensional environment for several reasons:
- Rudimentary: The 2D dimension of the computer screen is insufficient for the multiple dimensions of the data; analysts must slice data into a series of 2D projections, which is unsustainable for large datasets and limits the ability to see the larger picture.
- Variable: The subjectivity of manual human assessment can result in as much as 25% difference between analysts.
- Slow: Manual gating is time-consuming. In a clinical setting, it may take 30 minutes to 1.5 hours to analyze each sample. Outsourcing can result in turnaround times of several months, impacting patient assessment and treatment response.
- Costly: Employing multiple analysts is expensive at scale. Software can offer significant cost and time savings.
Flow Cytometry Analysis Software
Various types of flow cytometry productivity and analysis software are available, each serving a unique purpose.
Raw Instrument Data Acquisition
Collecting raw flow cytometry data can be challenging, especially when different flow cytometers produce outputs in vendor-specific formats. Lab IT often manages scripts to parse metadata and results, align data outputs, and create usable models. Tools like Dotmatics Luma Lab Connect can automatically ingest files from any file-based flow cytometer, parse files to extract metadata, and wrangle scientific data with minimal configuration. The data is then made available for more in-depth analysis.
Gating Software
Gates are central to flow cytometry analysis, but manual gating is time-consuming and subjective. Software such as FCS Express and OMIQ can streamline the gating process, provide tools for customizing and managing gates, create gating hierarchies, visualize results, integrate statistical and specialty analyses, and update corresponding visual and tabular data. Optimizing or automating the gating and analysis process can save significant time and money.
Key flow cytometry solutions include:
- FCS Express by Dotmatics: Integrated, ultrafast desktop computation tools to turn raw flow cytometry data into easily-understandable, presentation-ready results.
- OMIQ by Dotmatics: Bridges cloud-based machine learning and analytical workflow pipelines with classical gating and analysis.
Clustering and Autogating Tools
With increasing reagent availability and machine-wavelength sensitivity, high-dimensional flow cytometry is becoming standard. Analysts must examine millions of cells against multiple parameters, making traditional manual gating insufficient. Autogating and clustering tools can help.
- Autogating: Automates flow cytometry analysis pipelines to mirror manual processes. Pipelines are customized to reproduce existing gating hierarchies and adjust gates for each sample using a reproducible framework, providing rapid, robust, and reproducible results.
- Clustering Tools: Use high-dimensional and machine-learning approaches to identify clusters of cells with similar behavior. Clustering algorithms can analyze multiple parameters within large cell populations simultaneously, characterizing and categorizing diverse cell populations from single or multiple samples.
There are two types of clustering models:
- Unsupervised models: Do not use supplemental biological or clinical variables for training. Most unsupervised automated cell-population identification algorithms score around 75% in sensitivity and specificity.
- Supervised models: Use additional variables and can often attain close to 100% accuracy compared to manual analysis.
Dimensionality Reduction Tools for High-Dimensional Analysis
Dimensionality reduction tools help simplify complex high-dimensional data and provide easy-to-interpret visualizations. OMIQ by Dotmatics offers machine learning and GPU-accelerated tools for dimensionality reduction, as well as solutions for statistical differential analysis, trajectory inference, automated data cleaning, and data normalization.
Dotmatics Flow Cytometry Data Analysis Solutions
Dotmatics provides a platform that reduces the need for manual analysis by delivering powerful flow cytometry data analysis tools:
- Connect with flow cytometers to pull raw instrument data and metadata, and monitor instrument performance using Dotmatics Luma Lab Connect.
- Identify areas of interest with gating and automated gating solutions.
- Scrutinize cell subpopulations using specialty analysis, statistics, graphing, and reporting programs.
- Leverage machine learning and analytical pipelines for advanced analyses.
References
Weber and Robinson, Cytometry Part A, 2016 Dec 19; 89(12). doi: 10.1002/cyto.a.2303
Related
Dotmatics Acquires EasyPanel to Expand Flow Cytometry Capabilities With Advanced Panel Design
Dotmatics has acquired EasyPanel, a company specializing in automated flow cytometry panel design software, to enhance its flow cytometry solutions by integrating advanced panel design capabilities that optimize experimental workflows from initial design through data analysis, complementing its existing tools like OMIQ and FCS Express.
Reimagined Flow Cytometry Software
The Dotmatics Flow Cytometry solution integrates three leading platforms—OMIQ, Cytapex, and FCS Express—to deliver a powerful, flexible, and clinically validated software suite that combines classic and high-dimensional flow cytometry analysis with machine learning, automation, robust data management, and multi-user collaboration, designed to accelerate scientific innovation and meet enterprise and clinical needs.
Modernizing Flow Cytometry Analysis to Accelerate Oncology R&D Innovation
The Dotmatics Luma Flow Cytometry Workflow modernized a major US pharma's oncology R&D by automating data uploads, intelligent tagging, centralized storage, and advanced cloud-based analysis, resulting in rapid deployment, significant time savings per scientist, improved data accessibility, seamless sharing, and enhanced analytical insights.
Luma Flow Cytometry
Luma Flow Cytometry is a cloud-native, AI-ready platform that streamlines and modernizes flow cytometry data analysis and reporting by integrating advanced machine learning, real-time collaboration, automated data uploads, and compliance-ready traceability to accelerate high-dimensional immunophenotyping and regulated workflows across organizations.
End-to-End Flow Cytometry Workflow Optimization with Luma + OMIQ
Dotmatics addresses the challenges of manual data handling in flow cytometry workflows by integrating technologies like Luma and OMIQ to automate dataflows, minimize errors, and provide a seamless, end-to-end solution that unites instrument data, advanced analysis tools, and FAIR-compliant data management for optimized panel design, experiment planning, data acquisition, analysis, visualization, and contextualization.
Luma Antibody & Protein Engineering Solution for End-to-End Antibody Discovery
Dotmatics has launched the Luma Antibody & Protein Engineering Solution, the first in its Luma Multimodal R&D Solutions series, which integrates the Dotmatics Luma Scientific Intelligence Platform with key tools like Geneious, GraphPad Prism, and others to streamline and unify antibody discovery and development—particularly for monoclonal and multispecific antibodies—while supporting a broad range of therapeutic modalities including CAR-T, siRNA, ADCs, CRISPR, and vaccines within a collaborative, multimodal research environment.