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Updated: Mar 25, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Recent advances in quantitative high throughput and high content data analysis.

Ioannis K Moutsatsos¹, Christian N Parker¹

¹a Novartis Institute of Biomedical Research , Novartis - Developmental and Molecular Pathways (DMP) , Basel , Switzerland.

Expert Opinion on Drug Discovery

|March 1, 2016

Summary

This summary is machine-generated.

Advancements in high throughput screening necessitate improved data analysis. This review covers new methods for analyzing complex screening data, promoting best practices and open-source tools for biological discovery.

Keywords:

Data analysis High Content Screening High Throughput Screening qHTS

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Last Updated: Mar 25, 2026

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Area of Science:

Genomics
High Throughput Screening
Bioinformatics

Background:

High throughput screening (HTS) is a fundamental technique for biological exploration.
Technological progress in HTS, including increased capacity and multiparametric readouts, demands enhanced data analysis strategies.

Purpose of the Study:

To review recent advances in analyzing high throughput screening data.
To discuss methods for handling complex, large-scale screening datasets, including arrayed and cell-by-cell data.
To highlight open-source tools and best practices for screening data analysis.

Main Methods:

Analysis of arrayed screening data.
Analysis of cell-by-cell data from image cytometry and flow cytometry.
Development of methods for prioritizing gene targets when using multiple genomic reagents.

Main Results:

Identification and review of recent advances in HTS data analysis techniques.
Discussion of open-source data analysis methods contributing to consensus best practices.
Exploration of challenges posed by screening multiple genomic reagents.

Conclusions:

Growing data complexity in HTS requires accessible data analysis tools and novel visualizations for quality control and interpretation.
Advanced statistical and machine learning algorithms are crucial for identifying patterns in massive datasets.
User-friendly, iterative tools are needed for laboratory scientists to improve complex analysis outcomes.