Ideally during cytometry data analysis, the same gate position is appropriate for every sample in a study. Unfortunately, a lot of factors can cause variability in populations of cells, many of which are unavoidable.

Staining intensity can change due to batch effects, experimental conditions, tissue origin, cytometer performance, or just normal donor variance.

Manually adjusting gates to every sample can be extremely time-consuming, particularly if a study is large or has many markers.

With CellEngine, our next-generation cytometry analysis software, gates can be adjusted automatically, freeing analysts of this burden.

Autogating uses a manually gated training dataset as input, and it applies a machine learning algorithm to adjust the gates to the remainder of the dataset.

The algorithm follows your defined hierarchy, making the output easier to understand than unsupervised analysis methods.

The tool is easy to set up, runs in seconds, and the results are fully reviewable and changeable if needed.

1. Prepare a High-Quality Training Dataset

The training set used by the autogating algorithm is a user-defined subset of files that have been manually gated.

It should include examples from each of the experimental conditions in the study – for instance, both control and experimental samples, or samples from every tissue type.

High-quality training gates mean high-quality output, so a small number of files is usually sufficient, even for large datasets.

2. Organize and Group Files Efficiently for Consistent Gating

Files can be gated individually or in groups, with each file in the group having the same gate geometry.

For example, autogating can be instructed to gate all files for each donor the same way, thereby normalizing comparisons between individuals.

CellEngine conveniently allows defining those sample groups using metadata – in this example, the donor ID.

3. Let CellEngine Autogating Algorithm Customize Gates in Seconds

CellEngine’s autogating feature is designed to rapidly analyze large projects with many parameters. The autogating tool can adjust multiple gates simultaneously, making for a simple workflow.

CellEngine’s software and hardware optimization means that gates can be adjusted to thousands of files in seconds, so autogating is never a bottleneck to the next analysis.

If additional files are added later, autogating can correct gate positions for just those files, allowing for rapid analysis of the new data.

Autogating Easily Fits Into Your Existing Workflows

Gates created by the algorithm use the same 1D and 2D geometric shapes and hierarchy as manual gates. This allows the gates to be viewed like any other.

Furthermore, any gates that need correction can be manually adjusted. This allows autogating to be seamlessly used to supplement existing manual gating steps, so that analysts can spend more time on data interpretation and analysis.

Autogating is just one of many CellEngine features designed to improve flow cytometry analysis, particularly for large and complex experiments.

Sign up for a free two-month trial or contact us for a live demo.

 

Watch the following video from our expert to learn more

 

 

About the author:

Ideally during cytometry data analysis, the same gate position is appropriate for every sample in a study. Unfortunately, a lot of factors can cause variability in populations of cells, many of which are unavoidable.

Staining intensity can change due to batch effects, experimental conditions, tissue origin, cytometer performance, or just normal donor variance.

Manually adjusting gates to every sample can be extremely time-consuming, particularly if a study is large or has many markers.

With CellEngine, our next-generation cytometry analysis software, gates can be adjusted automatically, freeing analysts of this burden.

Autogating uses a manually gated training dataset as input, and it applies a machine learning algorithm to adjust the gates to the remainder of the dataset.

The algorithm follows your defined hierarchy, making the output easier to understand than unsupervised analysis methods.

The tool is easy to set up, runs in seconds, and the results are fully reviewable and changeable if needed.

1. Prepare a High-Quality Training Dataset

The training set used by the autogating algorithm is a user-defined subset of files that have been manually gated.

It should include examples from each of the experimental conditions in the study – for instance, both control and experimental samples, or samples from every tissue type.

High-quality training gates mean high-quality output, so a small number of files is usually sufficient, even for large datasets.

2. Organize and Group Files Efficiently for Consistent Gating

Files can be gated individually or in groups, with each file in the group having the same gate geometry.

For example, autogating can be instructed to gate all files for each donor the same way, thereby normalizing comparisons between individuals.

CellEngine conveniently allows defining those sample groups using metadata – in this example, the donor ID.

3. Let CellEngine Autogating Algorithm Customize Gates in Seconds

CellEngine’s autogating feature is designed to rapidly analyze large projects with many parameters. The autogating tool can adjust multiple gates simultaneously, making for a simple workflow.

CellEngine’s software and hardware optimization means that gates can be adjusted to thousands of files in seconds, so autogating is never a bottleneck to the next analysis.

If additional files are added later, autogating can correct gate positions for just those files, allowing for rapid analysis of the new data.

Autogating Easily Fits Into Your Existing Workflows

Gates created by the algorithm use the same 1D and 2D geometric shapes and hierarchy as manual gates. This allows the gates to be viewed like any other.

Furthermore, any gates that need correction can be manually adjusted. This allows autogating to be seamlessly used to supplement existing manual gating steps, so that analysts can spend more time on data interpretation and analysis.

Autogating is just one of many CellEngine features designed to improve flow cytometry analysis, particularly for large and complex experiments.

Sign up for a free two-month trial or contact us for a live demo.

 

Watch the following video from our expert to learn more

 

 

About the author:

If your research is subject to 21 CFR part 11, EU Annex 11, or equivalent regulations, you may have grappled with designing a regulation-compliant workflow to analyze flow cytometry data. Such regulations establish the required controls over key aspects of data analysis. Most of these requirements can only be met when deliberately designed into the analysis software. CellEngine, a new generation of flow cytometry analysis software, provides all the features required for compliance, allowing for cloud-based analysis that can fully meet regulations.

Capturing Audit Trails

Logging the creation, modification, or deletion of records is an integral part of regulation compliant research. CellEngine allows creating revisions, which are permanent and immutable snapshots of experiments that create a permanent record of analysis at any point. Detailed audit trails are captured, including what work was done, when, and by whom. Append-only comments can also be added to the audit trail, capturing why changes were made.

Personalized Data Access

Only authorized users should be able to complete tasks in compliant workflows, and all users need unique electronic signatures. Data in CellEngine is private by default but can be shared if required. CellEngine’s powerful identity and access management system lets administrators define custom roles comprised of granular permissions. Permissions can be assigned to individuals on specific folders and experiments, defining exactly what users can and cannot do. Access is highly customizable, based on study needs. For example, a reviewer could examine analysis without the ability to make modifications.

Readily Available and Protected Records

Reviewable records are available in both human-readable and electronic forms. CellEngine allows the download of complete experiments via two components: an archive of the FCS files and attachments that were uploaded to the experiment, and a JSON file containing all other aspects of the experiment. Individual aspects of an experiment, including gated populations, plots, illustrations, and statistics, are also downloadable.

CellEngine supports regulatory requirements for records to be protected, accurate, and retrievable by storing duplicate copies of data in geographically separated locations to protect against natural disaster or hardware failure. Users can be restricted from deleting experiments, and retention policies can be set for each experiment to protect against deletion entirely.

Customized Workflow Validation

Regulation-compliant research must validate the accuracy, reliability, and consistency of workflows. CellEngine is the first cytometry analysis platform to offer validation customized to your workflow. Customized test scripting from your cytometer files, analyzed with your process, can be continuously validated. As CellEngine is updated, we can provide validation reports verifying consistency.

Mandatory Training

Users must have the appropriate training to meet regulations. The CellEngine team can assist with training at any stage, including onboarding and follow-up training as needed.

Learn more about CellEngine’s many features designed to improve consistency of flow cytometry analysis, particularly for large and complex experiments.

Sign up for a free two-month trial or contact us for a live demo.

 

Watch the following video from our expert to learn more

 

 

About the author:

If your research is subject to 21 CFR part 11, EU Annex 11, or equivalent regulations, you may have grappled with designing a regulation-compliant workflow to analyze flow cytometry data. Such regulations establish the required controls over key aspects of data analysis. Most of these requirements can only be met when deliberately designed into the analysis software. CellEngine, a new generation of flow cytometry analysis software, provides all the features required for compliance, allowing for cloud-based analysis that can fully meet regulations.

Capturing Audit Trails

Logging the creation, modification, or deletion of records is an integral part of regulation compliant research. CellEngine allows creating revisions, which are permanent and immutable snapshots of experiments that create a permanent record of analysis at any point. Detailed audit trails are captured, including what work was done, when, and by whom. Append-only comments can also be added to the audit trail, capturing why changes were made.

Personalized Data Access

Only authorized users should be able to complete tasks in compliant workflows, and all users need unique electronic signatures. Data in CellEngine is private by default but can be shared if required. CellEngine’s powerful identity and access management system lets administrators define custom roles comprised of granular permissions. Permissions can be assigned to individuals on specific folders and experiments, defining exactly what users can and cannot do. Access is highly customizable, based on study needs. For example, a reviewer could examine analysis without the ability to make modifications.

Readily Available and Protected Records

Reviewable records are available in both human-readable and electronic forms. CellEngine allows the download of complete experiments via two components: an archive of the FCS files and attachments that were uploaded to the experiment, and a JSON file containing all other aspects of the experiment. Individual aspects of an experiment, including gated populations, plots, illustrations, and statistics, are also downloadable.

CellEngine supports regulatory requirements for records to be protected, accurate, and retrievable by storing duplicate copies of data in geographically separated locations to protect against natural disaster or hardware failure. Users can be restricted from deleting experiments, and retention policies can be set for each experiment to protect against deletion entirely.

Customized Workflow Validation

Regulation-compliant research must validate the accuracy, reliability, and consistency of workflows. CellEngine is the first cytometry analysis platform to offer validation customized to your workflow. Customized test scripting from your cytometer files, analyzed with your process, can be continuously validated. As CellEngine is updated, we can provide validation reports verifying consistency.

Mandatory Training

Users must have the appropriate training to meet regulations. The CellEngine team can assist with training at any stage, including onboarding and follow-up training as needed.

Learn more about CellEngine’s many features designed to improve consistency of flow cytometry analysis, particularly for large and complex experiments.

Sign up for a free two-month trial or contact us for a live demo.

 

Watch the following video from our expert to learn more

 

 

About the author: