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Title: cellXpress : a fast and user-friendly software platform for profiling cellular phenotypes
Authors: Laksameethanasan, Danai
Tan, Rui Zhen
Toh, Geraldine Wei-Ling
Loo, Lit-Hsin
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2013
Source: Laksameethanasan, D., Tan, R. Z., Toh, G. W.-L., & Loo, L.-H. (2013). cellXpress : a fast and user-friendly software platform for profiling cellular phenotypes. BMC bioinformatics, 14(Suppl 16), S4-.
Series/Report no.: BMC bioinformatics
Abstract: Background: High-throughput, image-based screens of cellular responses to genetic or chemical perturbations generate huge numbers of cell images. Automated analysis is required to quantify and compare the effects of these perturbations. However, few of the current freely-available bioimage analysis software tools are optimized for efficient handling of these images. Even fewer of them are designed to transform the phenotypic features measured from these images into discriminative profiles that can reveal biologically meaningful associations among the tested perturbations. Results: We present a fast and user-friendly software platform called "cellXpress" to segment cells, measure quantitative features of cellular phenotypes, construct discriminative profiles, and visualize the resulting cell masks and feature values. We have also developed a suite of library functions to load the extracted features for further customizable analysis and visualization under the R computing environment. We systematically compared the processing speed, cell segmentation accuracy, and phenotypic-profile clustering performance of cellXpress to other existing bioimage analysis software packages or algorithms. We found that cellXpress outperforms these existing tools on three different bioimage datasets. We estimate that cellXpress could finish processing a genome-wide gene knockdown image dataset in less than a day on a modern personal desktop computer. Conclusions: The cellXpress platform is designed to make fast and efficient high-throughput phenotypic profiling more accessible to the wider biological research community.
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-S16-S4
Schools: School of Computer Engineering 
Rights: © 2013 Laksameethanasan et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution Licens (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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