# A knowledge graph framework for automated gating analysis of cytometry data

> **NIH NIH UH2** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $237,082

## Abstract

Project Summary / Abstract
Flow and mass cytometry provide multiparametric single-cell data critical for understanding the
cellular heterogeneity in various biological systems. Modern polychromatic flow cytometers
simultaneously measure about 16 parameters routinely. The next-generation mass cytometry
(CyTOF) technology allows for the simultaneous measurement of 50 or more parameters. Even
as the cytometry technology is rapidly advancing, approaches for analyzing such complex data
remain inadequate. The widely-used manual gating analysis is knowledge-driven and easy-to-
interpret, but it is subjective, labor-intensive, and not scalable to handle the increasing complexity
of the data. Recent developments of automated data-driven algorithms are able to address the
issues of manual gating, but the results from data-driven algorithms are often not intuitive for
biology experts to interpret. These limitations create a critical bottleneck for flow and mass
cytometry analysis. The overall objective of this application is to develop a novel framework that
combines both knowledge-driven and data-driven approaches to achieve automated gating
analysis of flow cytometry and CyTOF data. The specific aims are: (1) build knowledge graphs to
capture existing knowledge of manual gating analysis, (2) develop algorithms for automated
gating analysis, and (3) validate the knowledge graph framework using large-scale studies in
ImmPort. The proposed research is significant because it will enable efficient and reproducible
gating analysis and provide visualizations that are easy-to-interpret, both of which are critically
important to the research community. Such contributions will fundamentally impact single-cell
analysis of cellular heterogeneity in diverse fields including immunology, infectious diseases,
cancer, AIDS, among others.

## Key facts

- **NIH application ID:** 10172842
- **Project number:** 5UH2AI153028-02
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Peng Qiu
- **Activity code:** UH2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $237,082
- **Award type:** 5
- **Project period:** 2020-06-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10172842

## Citation

> US National Institutes of Health, RePORTER application 10172842, A knowledge graph framework for automated gating analysis of cytometry data (5UH2AI153028-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10172842. Licensed CC0.

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