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Automated image analysis for high-throughput cell-based microscopy assays with R and Bioconductor

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Advances in automated microscopy have made it possible to conduct large-scale cell-based assays with image-type phenotypic readouts. Reliable and reproducible automated image processing and analysis on a high-throughput scale is one of the main challenges in analysing such assays. It forms the basis for the consequent statistical analysis and assessment of biological function. The analysis cycle includes image processing, image analysis, phenotype quantification, statistical analysis of phenotypic data and assessment of biological function with the help of biological databases.

EBImage [1] is a free and open source R-based toolkit for image processing and analysis designed specifically for automated image analysis in high-throughput imaging studies. R is a powerful programming and scripting language for statistical computing [2]. It is widely used for biological data and databases through the Bioconductor project [3]. Combined with the power of R in machine learning (clustering and classification) and hypothesis testing, and with Bioconductor packages, EBImage allows to perform the full analysis cycle of imaging data from cell-based assays. The package supports a wide range of image formats. Realized image processing algorithms include image sharpening, segmentation, edge detection, morphological operations, watershed and distance transfor­mations. Functions for object detection and extraction of descriptors like size, intensity, a‑circularity etc. are also available along with routines for visualization and quality assessment. Additionally, all mathematical and signal processing algorithms that are available for R can be applied to images.

The package is used for the analysis of genome-wide RNAi microscopy screens. The experiments comprise more than 20000 genes and hundreds of thousands of images. We could easily identify a subset of genes, which loss of function lead to a particular morphological change, irregularity, of the cell shape. Genes selected in such a way are now being investigated further for their role in a particular cellular pathway.

1. EBImage project page, www.ebi.ac.uk/~osklyar/EBImage/ 2. R Project for Statistical Computing, www.r-project.org 3. Bioconductor, www.bioconductor.org

This talk is part of the Computational and Systems Biology series.

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