Automation in FlowJoTM software: A Guide to Using Automation to
Improve Data Quality and Reproducibility
John Quinn, PhD
FRIDAY, SEPTEMBER 24, 2021 12 pm PT, 3 pm ET

John QuinnJohn Quinn is the Director of Science and Product Development for FlowJoTM, part of BD Life Sciences Informatics. John received his PH.D. from Drexel University in Philadelphia in 2006, working on a project applying machine learning and pattern recognition techniques to the analysis of flow cytometric data. The natural progression was of course to take a job with the world’s largest purveyor of manual gating, and work from the inside to bring algorithmic techniques to mainstream flow. He has worked for FlowJoTM ever since. He is currently working on the next version of FlowJoTM software,  SeqGeqTM software, and the BD®Research Cloud, user friendly software for analyzing single cells data, sequence data, and panel design, data storage, and experimental organization, respectively.

“It is our intention to design with the user as much as for the user.”


Zoom Webinar with live Q&A, 45 min webinar, 45 min Q&A


Farside Cartoon
The Far Side - Gary Larson

In this Webinar, we will introduce the viewers to tools in FlowJoTM software that can be used to un-bias analysis and automate key steps in the flow cytometry analysis workflow. As the size and complexity of single cell analytics increases, algorithmic approaches are rapidly becoming an essential component of the biological expert’s toolbox. Many biologists lack background and experience in this area and intuitive software for quick access to bioinformatic data processing is critical. We will start by showing attendees where and how to access free FlowJoTM plugins to extend their capabilities and use the latest algorithmic approaches in a point-and-click interface. We will then discuss how data quality, reproducibility and ease of analysis can be improved by:

  • Using  FlowJoTM’s built in QC tools
  • Automating quality control via a variety of algorithms
  • Allowing for cross-study comparison through normalization
  • Using clustering tools to perform unbiased high-dimensional population identification
  • Using visualization tools to understand these populations
  • Handling increasingly larger files without increasingly large computers