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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0250753 |
| _version_ | 1818401035043995648 |
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| author | David Ross |
| author_facet | David Ross |
| author_sort | David Ross |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T07:46:04Z |
| format | Article |
| id | doaj.art-a7cc45b26240469a83bde873e76bb014 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T07:46:04Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a7cc45b26240469a83bde873e76bb0142022-12-21T23:10:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01168e025075310.1371/journal.pone.0250753Automated analysis of bacterial flow cytometry data with FlowGateNIST.David RossFlow 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.https://doi.org/10.1371/journal.pone.0250753 |
| spellingShingle | David Ross Automated analysis of bacterial flow cytometry data with FlowGateNIST. PLoS ONE |
| title | Automated analysis of bacterial flow cytometry data with FlowGateNIST. |
| title_full | Automated analysis of bacterial flow cytometry data with FlowGateNIST. |
| title_fullStr | Automated analysis of bacterial flow cytometry data with FlowGateNIST. |
| title_full_unstemmed | Automated analysis of bacterial flow cytometry data with FlowGateNIST. |
| title_short | Automated analysis of bacterial flow cytometry data with FlowGateNIST. |
| title_sort | automated analysis of bacterial flow cytometry data with flowgatenist |
| url | https://doi.org/10.1371/journal.pone.0250753 |
| work_keys_str_mv | AT davidross automatedanalysisofbacterialflowcytometrydatawithflowgatenist |