scholarly journals Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

2009 ◽  
Vol 2009 ◽  
pp. 1-2
Author(s):  
Raphael Gottardo ◽  
Ryan R. Brinkman ◽  
George Luta ◽  
Matt P. Wand
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Flow Cytometry Data

scholarly journals Computational analysis of high-throughput flow cytometry data

2012 ◽  
Vol 7 (8) ◽  
pp. 679-693 ◽  
Author(s):  
J Paul Robinson ◽  
Bartek Rajwa ◽  
Valery Patsekin ◽  
Vincent Jo Davisson
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Computational Analysis ◽  
Flow Cytometry Data

scholarly journals Analysis of High-Throughput Flow Cytometry Data Using plateCore

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Errol Strain ◽  
Florian Hahne ◽  
Ryan R. Brinkman ◽  
Perry Haaland
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Negative Control ◽  
Data Sets ◽  
Data Set ◽  
Flow Cytometry Data ◽  
Open Platform ◽  
Screening Experiments ◽  
Software Packages ◽  
Good Agreement

Flow cytometry (FCM) software packages from R/Bioconductor, such as flowCore and flowViz, serve as an open platform for development of new analysis tools and methods. We created plateCore, a new package that extends the functionality in these core packages to enable automated negative control-based gating and make the processing and analysis of plate-based data sets from high-throughput FCM screening experiments easier. plateCore was used to analyze data from a BD FACS CAP screening experiment where five Peripheral Blood Mononucleocyte Cell (PBMC) samples were assayed for 189 different human cell surface markers. This same data set was also manually analyzed by a cytometry expert using the FlowJo data analysis software package (TreeStar, USA). We show that the expression values for markers characterized using the automated approach in plateCore are in good agreement with those from FlowJo, and that using plateCore allows for more reproducible analyses of FCM screening data.

scholarly journals WebFlow: A Software Package for High-Throughput Analysis of Flow Cytometry Data

2009 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Mark M. Hammer ◽  
Nikesh Kotecha ◽  
Jonathan M. Irish ◽  
Garry P. Nolan ◽  
Peter O. Krutzik
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Software Package ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Flow Cytometry Data

scholarly journals Profiling of Toxicity and Identification of Distinct Apoptosis Profiles Using a 384-Well High-Throughput Flow Cytometry Screening Platform

2012 ◽  
Vol 17 (6) ◽  
pp. 806-812 ◽  
Author(s):  
Yen K. Luu ◽  
Payal Rana ◽  
Thomas D. Duensing ◽  
Christopher Black ◽  
Yvonne Will
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Annexin V ◽  
Flow Cytometry Data ◽  
Biological Targets ◽  
Spectral Signatures ◽  
Assay Performance ◽  
And Performance ◽  
Screening Platform ◽  
Apoptotic Pathways

Methods and techniques used to detect apoptosis have benefited from advances in technologies such as flow cytometry. With a large arsenal of lasers, fluorescent labels, and readily accessible biological targets, it is possible to detect multiple targets with unique combinations of fluorescent spectral signatures from a single sample. Traditional flow cytometry has been limited as a screening tool as the sample throughput has been low, whereas the data analysis and generation of screening relevant results have been complex. The HTFC Screening System running ForeCyt software is an instrument platform designed to perform high-throughput, multiplexed screening with seamless transformation of flow cytometry data into screening hits. We report the results of a screen that simultaneously quantified caspase 3/7 activation, annexin V binding, cell viability, and mitochondrial integrity. Assay performance over 5 days demonstrated robustness, reliability, and performance of the assay. This system is high throughput in that a 384-well plate can be read and fully analyzed within 30 min and is sensitive with an assay window of at least 10-fold for all parameters and a Z′ factor of ≥0.75 for all endpoints and time points. From a screen of 231 compounds, 11 representative toxicity profiles highlighting differential activation of apoptotic pathways were identified.

scholarly journals High-throughput flow cytometry data normalization for clinical trials

2013 ◽  
Vol 85 (3) ◽  
pp. 277-286 ◽  
Author(s):  
Greg Finak ◽  
Wenxin Jiang ◽  
Kevin Krouse ◽  
Chungwen Wei ◽  
Ignacio Sanz ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Clinical Trials ◽  
High Throughput ◽  
Data Normalization ◽  
Flow Cytometry Data

scholarly journals High throughput automated analysis of big flow cytometry data

Methods ◽  
2018 ◽  
Vol 134-135 ◽  
pp. 164-176 ◽  
Author(s):  
Albina Rahim ◽  
Justin Meskas ◽  
Sibyl Drissler ◽  
Alice Yue ◽  
Anna Lorenc ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Flow Cytometry Data

scholarly journals Automated analysis of bacterial flow cytometry data with FlowGateNIST

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0250753 ◽  
Author(s):  
David Ross
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Automatic Calibration ◽  
Flow Cytometry Data ◽  
Background Events ◽  
Main Components ◽  
Engineered Bacteria ◽  
Python Package

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.

scholarly journals Data quality assessment of ungated flow cytometry data in high throughput experiments

2007 ◽  
Vol 71A (6) ◽  
pp. 393-403 ◽  
Author(s):  
Nolwenn Le Meur ◽  
Anthony Rossini ◽  
Maura Gasparetto ◽  
Clay Smith ◽  
Ryan R. Brinkman ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Data Quality ◽  
Quality Assessment ◽  
High Throughput ◽  
Flow Cytometry Data ◽  
Data Quality Assessment ◽  
High Throughput Experiments

scholarly journals Automated Analysis of Bacterial Flow Cytometry Data with FlowGateNIST

2021 ◽  
Author(s):  
David Ross
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Automatic Calibration ◽  
Flow Cytometry Data ◽  
Background Events ◽  
Main Components ◽  
Engineered Bacteria ◽  
Python Package

AbstractFlow 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.

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