Automated analysis of bacterial flow cytometry data with FlowGateNIST

Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacteria...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2021-08, Vol.16 (8), p.e0250753-e0250753
Main Author: Ross, David
Format: Article
Language:English
Subjects:
Analysis
Antibiotics
Automation
Bacteria
Beads
Biology and Life Sciences
Calibration
Cell differentiation
Computer and Information Sciences
Data analysis
Engineering and Technology
Escherichia coli - cytology
Experimental methods
Experiments
Flow Cytometry
Fluorescence
Gating
Genetically modified organisms
Measurement
Medicine and Health Sciences
Metadata
Observations
Programming Languages
Research and Analysis Methods
Software packages
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250753