Extraction and classification of cellular and genetic phenotypes from automated microscopy data
Seminar Room 1, Newton Institute
I will start the presentation by an overview over the Bioconductor project, a large international open source and open development software project for the analysis and comprehension of genomic data. Its goals are to provide access to a wide range of powerful statistical and graphical methods for the analysis of genomic data; to facilitate the integration of biological metadata in the analysis of experimental data: e.g. literature data, gene and genome annotation data; to allow the rapid development of extensible, scalable, and interoperable software; to promote high-quality documentation and reproducible research; to provide training in computational and statistical methods for the analysis of genomic data. While much of the initial focus has been on microarray analysis, one of the recent developments has been the development of methods, and computational infrastructure, for the analysis of cell-based assays using various phenotypic readouts.
Changes in cell shape are important for many processes during development and disease. However, cellular mechanisms and molecular components that underlie these processes remain poorly understood. We here present a rapid and automated approach to identify and categorize genes based on their phenotypic signatures on a single-cell level. Perturbations by RNAi on a whole genome scale led to the identification of several hundred genes with distinct cell shape phenotypes. More than 6,000,000 cells were individually profiled into different phenotypic classes. The approach is permits the segmentation of the genome into phenotypic clusters using complex phenotypic signatures.
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