Flow Cytometry Data

doi:10.1017/9781009397797.041

Chapter 41 - Flow Cytometry Data

Processing, Analysis, Artifacts, and Pitfalls

from Section 8 - Flow Cytometry Data Handling

Published online by Cambridge University Press: 20 May 2026

Xuehai Wang

Edited by

Wei Wang and

Sa A. Wang

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Wei Wang: Affiliation:
The University of Texas MD Anderson Cancer Center
Sa A. Wang: Affiliation:
The University of Texas MD Anderson Cancer Center

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Summary

This chapter gives an overview of how flow cytometry (FCM) data are processed, visualized, and analyzed, along with general analysis strategies. Rather than delving into technical details (which are discussed in lab SOP and other publications), this chapter focuses on understanding the principles of FCM data analysis from a data analyst’s perspective and offers practical tips on how to assess data quality, recognize the technical constraints of the assay, and distinguish true signals from biological or technical artifacts. Moreover, in addition to traditional data analysis approaches, this chapter will also touch on the recent trend of using machine learning, or AI, for data visualization and analysis.

Keywords

flow cytometry data processing analysis compensation transformation gating artifacts pitfalls artificial intelligence machine learning

Information

Type: Chapter
Information: Flow Cytometry in Hematolymphoid Neoplasms
A Comprehensive and Practical Guide to Diagnosis
, pp. 373 - 390

DOI: https://doi.org/10.1017/9781009397797.041 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2026

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