Department of Physiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Biotechniques. 2010 Jun;48(6):xxv-xxx. doi: 10.2144/000113427.
Complex Object Parametric Analyzer and Sorter (COPAS) devices are large-object, fluorescence-capable flow cytometers used for high-throughput analysis of live model organisms, including Drosophila melanogaster, Caenorhabditis elegans, and zebrafish. The COPAS is especially useful in C. elegans high-throughput genome-wide RNA interference (RNAi) screens that utilize fluorescent reporters. However, analysis of data from such screens is relatively labor-intensive and time-consuming. Currently, there are no computational tools available to facilitate high-throughput analysis of COPAS data. We used MATLAB to develop algorithms (COPAquant, COPAmulti, and COPAcompare) to analyze different types of COPAS data. COPAquant reads single-sample files, filters and extracts values and value ratios for each file, and then returns a summary of the data. COPAmulti reads 96-well autosampling files generated with the ReFLX adapter, performs sample filtering, graphs features across both wells and plates, performs some common statistical measures for hit identification, and outputs results in graphical formats. COPAcompare performs a correlation analysis between replicate 96-well plates. For many parameters, thresholds may be defined through a simple graphical user interface (GUI), allowing our algorithms to meet a variety of screening applications. In a screen for regulators of stress-inducible GFP expression, COPAquant dramatically accelerated data analysis and allowed us to rapidly move from raw data to hit identification. Because the COPAS file structure is standardized and our MATLAB code is freely available, our algorithms should be extremely useful for analysis of COPAS data from multiple platforms and organisms. The MATLAB code is freely available at our web site (www.med.upenn.edu/lamitinalab/downloads.shtml).
复杂对象参数分析器和分类器 (COPAS) 设备是大型荧光流式细胞仪,用于高通量分析活体模式生物,包括黑腹果蝇、秀丽隐杆线虫和斑马鱼。COPAS 在利用荧光报告基因的秀丽隐杆线虫高通量全基因组 RNA 干扰 (RNAi) 筛选中特别有用。然而,此类筛选的数据分析相对繁琐和耗时。目前,还没有计算工具可用于促进 COPAS 数据的高通量分析。我们使用 MATLAB 开发了算法 (COPAquant、COPAmulti 和 COPAcompare) 来分析不同类型的 COPAS 数据。COPAquant 读取单样本文件,筛选和提取每个文件的值和值比,然后返回数据摘要。COPAmulti 读取使用 ReFLX 适配器生成的 96 孔自动采样文件,执行样品过滤,绘制跨两个孔和板的特征图,执行一些常见的统计措施以识别命中,并以图形格式输出结果。COPAcompare 在重复的 96 孔板之间执行相关性分析。对于许多参数,可以通过简单的图形用户界面 (GUI) 定义阈值,使我们的算法能够满足各种筛选应用。在压力诱导 GFP 表达调节剂的筛选中,COPAquant 大大加快了数据分析速度,使我们能够从原始数据快速进入命中识别阶段。由于 COPAS 文件结构标准化,并且我们的 MATLAB 代码是免费提供的,因此我们的算法应该对来自多个平台和生物体的 COPAS 数据的分析非常有用。MATLAB 代码可在我们的网站 (www.med.upenn.edu/lamitinalab/downloads.shtml) 上免费获取。