|11 Apr 2012
Automation has been a great boon to the field of high-throughput screening. The Complex Object Parametric Analyzer and Sorter (COPAS) platform (Union Biometrica, Holliston, MA, USA) is a tool that allows for rapid quantification of the fluorescence, size, and optical density of small biological specimens, such as Caenorhabditis elegans, Drosophila, and zebrafish. The COPAS utilizes microfluidic approaches to draw intact live organisms through a fluorescence-compatible flow cell at extremely high rates (~50 animals per second) and quantifies the size [measured as object time-of-flight (TOF)], object optical density (EXT), and fluorescence emissions from up to three separate fluorescent channels for each animal. ...
While analysis of COPAS data is possible in other programming environments, such as Microsoft Excel and Visual Basic, MATLAB offers several significant advantages for COPAS data analyses. First, MATLAB is an interpreted language, making it very easy to learn, use, and modify. It is compatible with many different operating systems (Windows, Linux, Macintosh, etc.) and is therefore accessible to almost all users, regardless of platform. Second, MATLAB can receive user input through custom graphical user interfaces (GUIs); end-users need not have any experience with MATLAB to execute prewritten MATLAB functions. Third, MATLAB provides access to a library of common data handling methods, graphical representations, and statistical tools that can be visualized in highly flexible ways using plotting and imaging commands integrated within the MATLAB program. Such commands must often be written de novo in other programming languages. Since MATLAB is written for science and engineering applications, this library is tailored for analysis of scientific data. Finally, MATLAB is widely used throughout the biomedical research community, providing access to a strong user base for teaching, implementation, and code sharing. These advantages strongly support the use of MATLAB as the software of choice for analysis of COPAS data sets.