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FlowDx will perform complex multi-parameter analyses in an automated and rigorously validated manner.

News From the FlowDx Blog
Tree Star has existing software that services a large portion of research flow cytometry, but now seeks to reinvent the software for a new market with more-rigorous procedural requirements. The translation from lab to hospital requires higher quality assurance requirements and a higher level of integrated validation than we have used before. This work is supported by grant #1 R43 RR024094-01A1 from the National Institutes of Health awarded to Tree Star, Inc.

The consensus framework for best practices in software engineering is known as the Rational Unified Process (RUP), which provides the fundamental principles and disciplines of iterative development. The FlowDx project team has determined which RUP requirements apply to our project and has created over 50 documents that comprise the project plan.

1. Table of Contents

1.1 Flow cytometry is used to rapidly gather large quantities of data on cell type and function. The manual process of classifying hundreds of thousands of cells forms a bottleneck in diagnostics, high-throughput screening, clinical trials, and large-scale research experiments. The process currently requires a trained expert to identify populations on a digital graph of the data by manually drawing regions (gates). As the complexity of the data increases, this gating task becomes more lengthy and laborious. Minimizing or eliminating human processing is essential to increasing both throughput and consistency.


In clinical tests and diagnostic environments, automated gating would eliminate a complex set of human instructions and decisions in the Standard Operating Procedure (SOP), reducing error and speeding results to the doctor. Currently no software is available to perform complex multi-parameter analyses in an automated and rigorously validated manner. FlowDx will fill an important gap in the evolution of the technology and facilitate ever larger phenotypic studies and for the translation of this research process to a clinical environment.


1.2 Vision Statement

2. Methods 

2.1 Project Management - Rational Unified Process
2.1.5 Phase Plan and Milestones
2.1.6 Artifacts 

2.2. Use Cases
2.2.1 Synthetic
2.2.2 GvHD
2.2.3 SIV
2.3 Algorithms
2.4 Metrics
2.5 Infrastructure
2.6 Workflow

3. Results

3.1 In the first round, preliminary results are small in quantity and tainted by an evolving experimental protocol from one subject to the next. Processes were refined, training was standardized, and we saw the variance between human classifiers decline. Qualitatively, we show enough to contend that the project will be productive going forward.

3.2 Use Cases
3.2.1 Synthetic Data
3.2.2 GvHD report
3.2.3 SIV report
3.3 Algorithms
3.4 Metrics
3.5 Infrastructure 
3.6 Phase II Application Documents
3.6.1 Preliminary Studies - Progress Report
3.6.2 Research Design and Methods
3.6.8 Commercializtion Plan
3.7 Lifecycle Architecture Milestone
3.8 Summer Intern Program

4. Discussion:

Much of this preliminary work was spent in learning the Rational Unified Process, administrative planning of the project, and some very laborious manual analyses. The need for a smart repository/analysis tool was discovered during the first grant phase. The design and implementation of this unique tool has postponed the large-scale research and analysis. 

4.1 Project Summary
4.2 Use Cases
4.2.1 Synthetic
4.2.2 GvHD
4.2.3 SIV
4.3 Algorithms
4.4 Metrics
4.5 Infrastructure

5. References

5.1 Literature references
5.2 Internal Resources and Roles
5.3 External Collaborators
5.4 External Consultants