As the use of flow cytometry as a research technology expands and flow data becomes more complex it is necessary to ensure that the data being generated is of sufficient quality to justify the biological inferences being drawn. A fully automated system, currently being developed, examines the key properties of both the data and metadata contained within the FCS files in order to assist users and supervisors in rapidly assessing the technical suitability of the data generated. We have called this system Flow Automated Data Quality Control (FADQC).

To demonstrate how FADQC works, we conducted  series of mock experiments to look at three inconsistencies commonly found in flow cytometry data post-acquisition and analysed the FADQC report.

• Firstly, the PMT voltage for a single parameter in one sample was modified.
• Secondly the compensation values for one sample were modified and
• Thirdly a disruption in flow rate was simulated during acquisition/recording.

Changes in PMT voltage and compensation values were identified in FADQC as changes in panel layout and changes in compensation values respectively. Finally, changes in flow rate due to a disruption in sample acquisition could be easily visualised using the movie feature in FADQC which displays recorded events over time.

This demonstrates that common anomalies affecting the quality of flow data can be autonomously detected by FADQC. Implementing FADQC routinely into a flow cytometry facility will assist in identifying issues in sample handling, preparation and during data acquisition, and could be instrumental in preventing the publishing of misleading data.

**Corresponding Author- r.salomon@garvan.org.au **