The flow cytometry bioinformatics CRO used by 6 of the top 10 pharma

The Cytapex analysis pipeline replaces time-costly and subjective manual gating.

Our algorithms are consistently peer reviewed to be the best performing and state-of-the-art.

Custom tailored to your cytometry analysis needs, our automated solution is fast (60 sec) and provides robust, reproducible results.

We provide superior tools for data-driven flow cytometry analysis.

We seamlessly integrate and output into third party manual analysis tools you are already using.

Quality Checking

Quality Checking is Job 1 – With our automated approaches, the data QCs itself. We build the checks into the gating process to make sure FCS files are internally consistent, match with patient files, and that all the reportables that need to be filled out are done correctly. This is all based on making sure the data we are provided has the right metadata to start with. Of course, garbage in garbage out is just as important for automated analysis as manual analysis. So we make sure channels are consistently and correctly named to start with (and we’ll catch it if they aren’t), we use our best performing automated Time vs. Fluourescence gating algorithm and provide interactive, post-gating reports flagging variable and suspect data.

Automated Gating

Data-Driven Cytometry – Our automated gating pipelines are peer-reviewed best in the world and are in production use for clinical trial data analysis. They are collaboratively customized for each panel to reproduce your gating hierarchy in a data-driven manner. Our pipelines are proven to be robust across centres, diseases, instruments and time, and can be seamlessly integrated into existing workflows involving commercial software currently used for manual analysis.

Biomarker Discovery

From Reportables to Biomarkers – There are many choices available for unsupervised analysis, however if a goal is to also generate reportables (e.g., cell population proportions) unsupervised methods lack the performance that our customized gating pipelines have. Once those piplines are in place we combine all possible combinations of gates to generate biomarkers in a way that has proven world-class performance and at the same time is based off gates you will have already signed off on.

Transparent Implementation

Drop-in seamless implementaion – We can export automated gating results into formats you already use like FlowJo and (coming soon) FCS Express. You dont even have to lift a finger for gating to happen, just put your FCS files into a folder and the pipeline takes it from there. We generate customized PDF reports, fill in your reportable files, and post-process results into the format you need so no more copy and pasting required. We provide you the source code and offer several options of implemation including internal intranet web-driven interfaces, FlowJo plugins and pharma-secure Singularity containers. These containers provide drop-in deployment within high performace computing (HPC) environments requiring the assumption of lack of trust of both users and software. They also provide a sure way to reproduce your data analysis.

Automated gating

Biomarker discovery

Leading Performance

17 Years in the Making – Our workflows leverage algorithms we developed in-house, and that have consistently been peer-reviewed to be the best performing and state-of-the art (PMC3906045, PMC4874734, PMC4748244). We also have experience with all the commonly used automated analysis methods.

Case Studies

Automated Gating for PD-L1

To demonstrate the feasibility of automated analysis to identify low quality samples and measure dimly expressed checkpoint marker PD-L1 in patients with acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and healthy donors (HD) samples were analyzed by automated and manual analysis methods. This study correlated intra- and inter-operator reproducibility results with automated analysis to test its variability and reliability.

Case Studies

Multicentre Biomarker Study

This case study demonstrates the high reproducibility of our supervised analysis pipelines based on analysis of peripheral blood samples from healthy subjects and patients 10- days after hematopoietic stem cell transplantation, using different instruments from different vendors and across centres.

Case Studies

Automated Pipeline for Human Immune Profiling

This case study illustrates practical implementation of an automated flow cytometry analysis pipeline for human immune profiling.

  • Results showed a high concordance of cell count and percentage (of parent gate) between manual and automated analysis, particularly in high frequency and homogenous populations, such as lymphocytes (0.90 < R < 1.00).
  • Heterogeneous and low frequency populations, such as PD-L1+CD34+ CD45dimBlasts, yielded slightly lower, yet acceptable concordance with manual analysis (0.75 < R < 0.99).
  • Lower scores did not reflect poor performance of automated methods; the manual analysis of these populations was more subjective and thus a challenge to match with data-driven thresholds.
  • The variability between automated and manual analysis is comparable with inter- and intra- operator variability observed on the same dataset.
  • This study demonstrated that computational analysis provides a standardized pipeline mirroring manual analysis. It showcased the feasibility of using computational gating and reproducible quality assessment and analysis.
  • Automated methods decreased hands-on analysis time. With computational analysis, each sample took approximately 60 seconds – and that is hands-off computer time.

This study was presented by: Alberto Hidalgo Robert, Shadi Eshghi, Ryan Brinkman, Sibyl Drissler, Daniel Yokosawa, Cherie Green, W. Rodney Mathews. Implementation of Automated Gating Strategies for Quality Control and Analysis of Checkpoint Marker, PD-L1, in Hematologic Malignancies. CYTO2019, June 25th, 2019.

Full poster avilable here.

We developed an automated analysis workflow based on flowCore and flowDensity. Data was generated using DuraClone’s dry reagent. pre-formatted panel antibody cocktails technology (Beckman Coulter). The performance of automated pipelines was assessed by their ability to match on a per-event basis values obtained by an expert manual analyzer (i.e., the reference manual), currently considered the “gold standard” approach. In addition, we compared the reference manual values with those obtained by 2 additional manual analyzers who followed an identical gating strategy. Automated analyses produced results that were highly correlated with those obtained in the reference manual analysis. For example, the Spearman’s rank correlation coefficient [ρ, rs] comparing reference manual to automated analysis for the 14 Basic panel cell populations were all > 0.8. Most median F1 scores were > 0.9, with an overall F1 average of 0.93. To demonstrate robustness, we obtained an independent data set from the ONE Study, which used the same antibody panel and fluorescence intensity settings, and reanalyzed these data manually and with our automated pipelines. When all populations analyzed were combined for the two datasets, correlation values between automated and manual gating were all > 0.9, demonstrating that automated gating pipelines developed with one set of data can readily be used to accurately analyze independent data if they are collected using the same standardized methodology. To test the robustness of the analysis pipelines to alternate instrument platforms, we analyzed parallel samples acquired on a Navios (Beckman Coulter, 3 lasers) or a Fortessa X20 (BD Biosciences, 4 lasers), and obtained correlation values > 0.99. In general, we found that automated gating agreed slightly less with the reference manual than other manual gating. As in the Conrad study, lower agreement occurred especially for low-abundance, poorly defined populations (e.g., plasmablasts). Indistinct boundaries, such as between CD14+ and CD14++ (particularly in the CD16+ population), as well as between CD16– and CD16+ led to variability in manual gating (Manual 1 and Manual 2 vs. reference manual, both rs = 0.83), as well as in automated versus reference manual (rs = 0.83).

Peer-reviewed publication is availble here.

Data was generated using two staining panels that identified effector and memory or helper and regulatory T cells. A core panel of 8–10 fluorochromes was used to identify immune subsets of interest to which three fluorochromes can be added to measure 6–15 activation markers per patient per time point, depending on the desired depth of analysis. After acquisition, FCS files were opened with FlowJo software to adjust compensation and the resulting workspaces were read into R for all further data processing. While a significant focus has been rightly placed on automated gating, the adoption of automated approaches for cell population identification opens up additional avenues for improving the rigor and reproducibility of clinical trial data analysis within the analysis pipeline once data enters a computational stream. After pre-gating quality checking was competed using such approaches, gates were then set by flowDensity. Unlike typical clustering algorithms that tend to identify populations by examining all dimensions simultaneously, flowDensity is based on a sequential bivariate gating approach that generates a set of predefined cell populations using a pre-specified approach customized for each cell population of interest. The algorithm mimics manual gating steps, but chooses the best cut-off for individual markers using characteristics of their density distribution such as the slope of density, or the valley between the two peaks of the density. For activation marker gating, a gate boundary set on the fluorescence minus one control was applied to matching samples with the fluorochrome present. Gates are set independently for each data file based on these rules. Once the parameterization of the gating steps is complete, gating steps run in sequence by a single script, only requiring manual input to specify the directory where the files are located and the desired location for the outputs. The approach tends to be robust, as long as new data files are generally similar to those used to set the gate boundaries. This study then tested how the analysis pipeline with flowDensity performed compared with manual gating conducted by domain experts on data from five patients. Manual and automated analysis were performed on the same 11 populations of interest over a total of 33 time points (2–4 tests per time point), yielding 1,077 matched populations. Comparison of these populations showed a significant correlation between the manual and automated analysis of the T cell panel (see below). While the bulk of the populations showed strong agreement, manual gating of populations with few events were not matched as closely by automated analysis. The larger disagreement can be attributed primarily to the small number of events in that population (the impact of small shifts in gate placement is magnified on smaller cell populations) and secondarily to the lack of resolution between populations (i.e., smears). When controls are not used for such rare populations, there is often no objective information available to guide either automated or gating. This makes evaluation of automated analysis approaches for such cell populations especially challenging. Despite these differences, the observed trends were the same as those obtained from manual gating, which is an essential criterion for using this method in longitudinal studies.

Peer-reviewed publication is availble here.


Our bioinformatics services will enhance your cytometry analysis.

Cytapex designs bespoke automated data analysis pipelines for cytometry data that provides customers data-driven results that are rapid, robust and reproducible.

Our team has covered the spectrum of automated flow cytometry data analysis for the last 17 years. From 7 panel clinical trials involving over 10,000 samples, mouse studies looking for biomarkers across the entire genome's worth of 77,000 files, small patient studies, to instrument QC, we've seen pretty much every kind of flow cytometry dataset.

We offer expanded and singular bioinformatics services to fit your needs.

We also have experience with all the commonly used tools for the automated analysis of flow and have applied our analysis approaches on a wide spectrum of research and clinical trial data.

For more details, see a full webinar.

Quality Control

We know it not only matters how good we are at generating your results, but that we make sure what we report is reported correctly, QC’d properly and we have identified samples that do not look right.

Automated gating

Our automated gating pipelines are collaboratively customized for each client to robustly reproduce your existing gating hierarchy. Once the pipeline is designed, our gates will adjust themselves for each sample using on a robust, reproducible framework that is then further hand-crafted for each panel to capture each population in your gating hierarchy algorithmically. From then on, all you have to provide is compensated FCS files to software you run in house. You'll see in every plot the math behind the gates, giving you confidence in the results.

Biomarker Discovery

There are many choices when it comes to biomarker discovery. We will work with you to decide the best approach for biomarker discovery for your data and questions.

About us

World class leadership in flow cytometry bionformatics.

Cytapex is led by Dr. Ryan Brinkman (Distinguished Scientist, British Columbia Cancer Agency and Professor, Medical Genetics University of British Columbia). Dr. Brinkman is a recognized leader in the area of flow cytometry bionformatics, having published over 50 peer-reviewed papers in the field. He has led ISAC's Data Standards Task Force since 2005, including the development of the latest versions of the FCS standard used by all cytometers as well as the MIFlowCyt standard recommended by several journals including from Nature, PLOS and Wiley. In 2012 Dr. Brinkman launched FlowRepository, the only public repository for FCM data under the auspices of the International Society for the Advancement of Cytometry, now with over 250,000 FCS files, 1,900 registered users and 31,000 downloads. Dr. Brinkman has been recognized as an ISAC, a Terry Fox Research Institute and a Michael Smith Foundation for Health Research Scholar, and was honoured in 2018 with an ISAC Distinguished Service Award.

Our team includes bioinformaticians who have developed top-performing flow cytometry bioinformatics algorithms in peer-reviewed literature.

Here we show how easy it is to do autogating the Cytapex way, leveraging your manual gating hierarchy and with cell population identification performance better than any other automated approach and takes just seconds per file.

Here we show how we use Cytapex autogating to do biomarker discovery on hundreds of thousands of boolean combinations of your gates with peer-reviewed best performance.


Our clients include
  • 6 of the top 10 pharma
  • The top instrument vendor
  • The top reagent manufacturer
  • The biggest CROs
  • Small biotech
  • Academic labs doing basic research
  • Testing service providers
Our clients love us and we would be happy to put you in touch with those who have agreed to share their stories with you.

This is just some of the unsolicited feedback our customers have sent us:

  • "I just love this automated gating stuff. I just can’t believe it." "This is above and beyond the call of duty" “There is definitely value in the model and I can only imagine how nicely this technology would work when deployed on a well-designed panel.” "The TO lead was very impressed with your analysis display of the data. She also LOVED the idea of it being interactive." (M.E., Senior Manager @Top 10 pharma )
  • "It’s hard when you see what you have been doing for years is wrong.” (A.P., Research Scientst @Top 10 pharma) After seeing how our gating identified a critical flaw in the analysis they had been doing.
  • "You guys are the best, no one else comes close. I love the gating scheme, it looks beautiful ” and “I threw out this same file as I could not gate this. Your algorithm captured it, which is awesome” (A.S., Scientist, Analytical Development, Product Sciences @Top 10 pharma )
  • "[We] have recently worked with you on a study with semi-unsupervised analysis. We learned a lot. We are trying to do more in this aspect to support our clinical trials." (Y.S., Associate Director, Clinical Flow Cytometry @Top 10 pharma )
  • "I just wanted to give you a heads up that our collaborations have gotten a lot of visibility within [top 10 pharma company] lately and the enthusiasm from other programs and scientists is high." C.G., Senior Scientific Manger, @Top 10 pharma)
  • "I’d like to start trying [your gating] in our manual gating flow with some of our analysts and would like to make sure we’re doing it in a similar way.” (D.H. biotech client,) On wanting to start gating by hand according to our automated gating process before we handed over final code as our gating logic was working better than what they had been using in production.