Often, the researchers best able to explore and interpret the data lack these computational skills. Given that no existing tools meet the specific needs of image-based screens, researchers have needed computational expertise to directly query databases of image-based information using command-line tools.
#Cellprofiler count aggregates around nucleus software
Existing software packages targeted for image-based screening, however, have one or more limitations which prevent sophisticated visualization and extraction of information from image-based screens: (a) they are not designed for the hierarchical data structure inherent in image-based data (each treatment condition is replicated in several samples, each sample is usually represented by several images, each image contains a population of cells, and each cell has hundreds of associated measures), (b) they ignore the inherent biological variability of cell populations such that assays requiring subpopulation analysis cannot be scored, (c) they cannot handle the volumes of data typical in image-based experiments (e.g., ~500 measurements for each of ~100 million individual cells), (d) they provide limited linking to raw or processed image data or chemical structure data, (e) they allow only limited statistical analyses of the data, (f) they are proprietary and new methods cannot be easily added, (g) they are limited to data from a particular image analysis package, (h) they require expertise in statistics or programming, and/or (i) they require intense hands-on data management. For analysis of small or very simple experiments, spreadsheet programs like Microsoft Excel are sufficient, and useful open-source tools exist for analysis and exploration of data from high throughput screens in general. The volume and richness of individual-cell data from large image-based screens is unprecedented and existing software is inadequate for the challenge of data analysis. This tool has been useful for extracting image-based measurements to score sophisticated screens, with many more in progress. We recently developed open-source image analysis software, CellProfiler, which measures a rich set of cellular features in images, such as size, shape, and staining patterns including intensity, texture, and colocalization. This capability is increasingly important given the ease now to systematically perturb cells with libraries of chemicals or gene-perturbing reagents like RNA interference or gene overexpression and collect hundreds of thousands of images of these cell samples. The scientific community has only begun to scratch the surface of computationally extracting the rich information visible in fluorescence microscopy images of cell samples. Visual analysis of cell samples has played a dominant role in the history of biology.
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