Abstract 164: Development of DeepCytoFlow: A novel deep learning pipeline enabling automated gating of flow cytometry data

Introduction Following the advent of cellular therapies, flow cytometry has become a central tool for simultaneous assessment of drug levels and disease burden due to its ability for high dimensional cellular analysis. However, identification and monitoring of myriad immune- and leukemic cell phenotypes requires a manual gating process by subject matter experts. Although this process is the current gold standard for flow data analysis, it is slow, tedious, and prone to subjectivity. Herein, we describe our approach to automate flow data analysis using novel strategies that reduces the reliance on clustering methods that are unable to filter challenging noisy data sets inherent in flow data. Methods We developed DeepCytoFlow, a novel deep learning framework adapted for flow cytometry data analysis. DeepCytoFlow utilizes a convoluted neural network structure to analyze flow cytometry data files to generate appropriately gated cell populations in a matter of minutes significantly reducing the time required for manual analysis. The neural networks were first trained using a unique data set generated manually by multiple operators and tested using an independent data set to establish concordance in assessments. In addition, we evaluated the performance of our method using both accuracy and F1-score. Results Our integrated DeepCytoFlow pipeline employing neural network design shows promise on clustering-based analysis methods in terms of accuracy, precision and recall. More specifically, DeepCytoFlow is capable of correctly classifying various immune cell lineages (e.g., CD4 and CD8 T-cells). However, a limitation was observed on its ability to classify rare cell populations with a very limited prevalence. In an effort to improve performance, we are currently designing a new network architecture, and increasing our training and test dataset's size and quality. Conclusions DeepCytoFlow provides proof of concept of the applicability of deep learning for automation of gating in flow cytometry. The high adaptability of deep learning to a large set of problems suggests that this technique can be broadly applied to a larger set of biomarkers for the purpose for automated gating. With additional improvements, we believe DeepCytoFlow can enable a fast, accurate, and robust platform for analysts to perform consistent and, repeatable flow analysis for biomarker monitoring in clinical trials investigating novel immunotherapies. Citation Format: Arjun Chandran, Jennifer Tsa