cellXpess :: Cellular Phenotype Profiling Software Platform

Fast processing speed Written in C/C++ and optimized for modern 64-bit and multi-core CPUs. Support parallel processing and dynamic job scheduling for high-throughput applications.
Single-cell phenotype quantification Automatically identify individual cells from fluorescence microscopy images; and quantify cellular morphology, protein sub-cellular localization and other features.
User-friendly interface Intuitive graphical user interface for interactive configuration of computational algorithms and visualization of segmentation and feature extraction results.

Cellular phenotype profiling
	cellXpress is a cellular phenotype profiling software platform developed by the Loo Lab at the Bioinformatics Institute, A*STAR, Singapore. The platform is designed for fast and high-throughput analysis of cellular phenotypes based on microscopy images. It is especially useful for large-scale profiling of cellular responses to pharmacological compounds, gene knockdowns, and/or toxic substances.

8 March 2017 cellXpress pro 1.4.2 is available now. This is a critical update that fixes a bug in loading images with no cell. All users are recommended to update their cellXpress to this latest version.
13 Feb 2017 cellXpress pro 1.4.1 is available now. This is a critical update that fixes a bug in loading images with non-zero-based channel indices. All users are recommended to update their cellXpress to this latest version.

Features Highlight

Single-cell phenotype quantification Automatically identify individual cells from fluorescence microscopy images; and quantify cellular morphology, protein sub-cellular localization and other features.
Plate-based data organization Design to handle high volume of image data acquired from 96/384-well plates. Analyses of different fluorescent-marker configurations can be performed on the same plate data.
Quick plate analysis Perform cell count and fluorescence intensity measurements over the whole plates, and visualize well-to-well trend and variability.
Phenotypic profiling Transform raw image features into discriminative profiles that can be used to characaterize changes in cellular phenotypes.

Fast processing speed Written in C++ and optimized for modern 64-bit and multi-core CPUs. Support parallel processing and dynamic job scheduling for high-throughput applications.
User-friendly interface Intuitive graphical user interface for interactive configuration of computational algorithms and visualization of segmentation and feature extraction results.
Split-screen viewer View and compare different cell images, segmentation results and feature values side-by-side in the same windows.
Cross-platform data sharing Access the same projects under different operating systems. Data files can be imported into the R software environment for further processing or visualization.