# Big Flow Cytometry Data: Data Standards, Integration and Analysis

> **NIH NIH R01** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $158,388

## Abstract

PROJECT SUMMARY
Flow cytometry is a single-cell measurement technology that is data-rich and plays a critical role in basic
research and clinical diagnostics. The volume and dimensionality of data sets currently produced with modern
instrumentation is orders of magnitude greater than in the past. Automated analysis methods in the field have
made great progress in the past five years. The tools are available to perform automated cell population
identification, but the infrastructure, methods and data standards do not yet exist to integrate and compare
non-standardized big flow cytometry data sets available in public repositories. This proposal will develop the
data standards, software infrastructure and computational methods to enable researchers to leverage the large
amount of public cytometry data in order to integrate, re-analyze, and draw novel biological insights from these
data sets. The impact of this project will be to provide researchers with tools that can be used to bridge the gap
between inference from isolated single experiments or studies, to insights drawn from large data sets from
cross-study analysis and multi-center trials.

## Key facts

- **NIH application ID:** 9969443
- **Project number:** 5R01GM118417-04
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Greg Finak
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $158,388
- **Award type:** 5
- **Project period:** 2017-09-20 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9969443

## Citation

> US National Institutes of Health, RePORTER application 9969443, Big Flow Cytometry Data: Data Standards, Integration and Analysis (5R01GM118417-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9969443. Licensed CC0.

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