Analysis Consistency in Flow Cytometry

February 29, 2012  |  Education  |  By  |  1 Comment

Analysis Consistency in Flow Cytometry

When collecting and analyzing flow cytometry data, analysis consistency and quality control are essential in ensuring the validity of data within an experiment and among experiments carried out over time.

Quality control issues arise when there is variability in how experiments are carried out at the bench. We will tackle issues relating to data acquisition in a future post. In this post, we’ll discuss analysis-related quality concerns and introduce you to several Cytobank functionalities that are geared towards addressing these.

Where do issues surrounding quality control and analysis consistency arise?

– Multi-center endeavors to collect and analyze data
– Heads of labs who want to maintain consistency in analysis and presentation as scientists flux in and out of the lab
– Companies interested in a unified analysis and presentation style
– All scientists aiming to achieve reproducibility

Replicating data analysis strategies among similar experiments

The trend towards high dimensional flow cytometry, including mass cytometry, allows scientists to assess increasing numbers of targets simultaneously. This means that gating hierarchies will only become more complicated as they evolve to include increasing numbers of cell subsets and signaling information. A question we often receive is how to apply the same gating scheme between similar experiments.

To facilitate consistency between similar experiments on Cytobank, you can import gates from the related experiment into the new one. This ensures that the same gating strategy will be used among related experiments and can reduce error in replicating gating hierarchies. This can also be useful when research groups want to ensure everyone within a group is adhering to a group standard. Once the gates are imported, you have the option of altering gate positioning to tailor it in the event that there are scatter differences from experimental sources of variability.

Gates can be imported from one experiment to another

Centralizing and communicating data from collaborations

Whether you are contributing data to a multi-center clinical trial or collaborating with a team of scientists across the room or across the world, you will likely want to share the results of your research endeavors. Centralizing project data acquired by different researchers, regardless of their physical location, facilitates communication. Linking resulting figures to underlying data, protocols, and other supporting documents ensures that researchers can delve back into the experiment and rework the analysis and interpretation at any time without the loss of valuable information.

The way you share this information, however, can depend on the project and roles of collaborators. Some scientists may want to give full access to their collaborators early on in a research project. Others may want to share the data once gates and annotations are applied but not allow colleagues to alter gates or annotations. Some may want to share only the resulting figures. All want to have the results linked to the underlying data and experiment details.

When sharing results with collaborators on Cytobank, you determine their access permission level: whether they can only view figures, can rearrange figures (but not change annotations or gates), or have full access to the experiment. From an analysis consistency perspective, sharing illustrations on Cytobank allows collaborators to rearrange figures to compare different figure dimensions without having to redo dimensions such as gating, thereby reducing the likelihood of variability among analysis strategies. With a couple simple clicks, a figure can be rearranged to compare by donor, by channel, or by stimulation condition, without having to fully recreate the illustration, saving time and reducing the likelihood of introducing error.

Change plot display with a few simple clicks

After sharing a figure, a colleague may want to see the underlying raw data in order to explore a question that you may not have considered in your original analysis. On Cytobank, all figures are linked to the raw data. If a colleague wants to view dot plots that underlie a heatmap or histogram, the illustration view can be changed with just a couple clicks of the mouse, without having to remake plot layouts and risk mixups. If you have granted access, your collaborator can also adjust gates or clone a copy of the experiment from which the figures originated in order to manipulate the raw data in a new way.

From an organizational standpoint, collaborators may want to group related experiments into projects and have other project members have immediate access to view (and possibly edit) data no matter their physical location. In addition, keeping experiments organized and centralized into projects means that data won’t be lost over time, and well-annotated FCS files linked to analyses will essentially future-proof the data. On Cytobank, experiments that are part of a collaboration can be grouped into Projects, where project members are automatically granted access when an experiment is assigned to that project, no matter the physical location of the project member. Projects enable collaborators to group related experiments, and project leaders can manage project member access permissions, limiting some project members to a view-only mode while granting other members permission to manipulate the raw data themselves. Our Sharing tools enable you to directly share Illustrations linked to raw data with collaborators. By default, you are the only person who has access to an experiment you upload, and our sharing tools give you the power to control who can see your data and how much access they have. In addition to our main server at, we offer hosted models of Cytobank accessible by only your research group.

Hosted models of Cytobank can help you centralize data from large collaborations no matter where participants are located, and Projects help you set access permissions to figures linked to raw data. Institutions interested in a unified analysis and presentation style would also benefit from centralization.

Preserving data over time

Having well-annotated files is essential to being able to pick up analysis easily where you last left off, no matter how much time has passed. If your FCS files are sitting in a folder on your computer, unlinked to analyses, protocols, and notes, you run the risk of not being able to remember important experiment details when you open that experiment folder a year later, when preparing a manuscript or presentation. We talked about the importance of annotation in a previous post.

The analysis workflow on Cytobank leads you to annotate your FCS files so that information such as gating, stimulation conditions, and time points are associated with your experiment from the start. If you need to refer back to the experiment at a later date, these annotations are preserved and well-organized for easy retrieval. No more having to wonder what parameters corresponded with file Data.001 and risking misremembering. Illustrations you build are linked to the raw data along with any protocols, comments about the experiment (for example, any anomalies), and other attachments in one experiment bundle, so you can always refer back to the raw data and reanalyze as needed. Heads of lab can easily maintain lab continuity as members join and leave the group when data are well annotated and centralized. Cytobank offers tools for PIs to be added to experiments and Projects such that they can supervise work and help preserve this continuity.

Cloning experiments for analysis iterations

Whether you are working with high dimensional flow cytometry or simple experiments, there will be times when you want to explore an alternate analysis strategy without erasing a previous analysis. While you can generate and save multiple Illustration views within one experiment on Cytobank (dot plots, histogram overlays, heatmaps, etc), there may be times you want to perform iterations of an analysis changing how some of your files are grouped within figure dimensions. For example, you might want to try out a different gating scheme while preserving other annotations such as donor, timepoint, and stimulation condition assignments. Or maybe you ran a 30 channel, barcoded experiment and want to break off smaller pieces for analysis. Maybe you are teaching someone to perform flow cytometry analysis and want to start with a blank slate.

To generate a copy of an experiment where every aspect of analysis is preserved except for the one(s) you want to change, use our experiment cloning tools. We have options to carry over information about compensation, gates, annotations, attachments and protocols, reagent names, and more. Read more about this in our recent blog post on cloning.

Send us a note with any questions, comments or suggestions!
– Angela