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A Framework for Visualization of Microarray Data and Integrated Meta Information

机译：微阵列数据和集成元信息可视化的框架

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摘要

We have developed a methodology that allows integration of microarray data and meta information within a visualization in order to guide the investigator during data exploration and analysis. A simple mathematical framework is introduced that uses scoring functions to map meta information to relevance ratings of genes. To explore the potential of this framework we extended the traditional heatmap with new features to graphically represent the relevance ratings. These ratings are visualized by an additional color gradient, by scaling the vertical height of matrix rows, by rearranging rows or by inserting new columns into the heatmap. This visualization is called an enhanced heatmap. We have applied our approach to microarray data of the Saccharomyces cerevisiae cell cycle, complemented with supplemental data that we both derived from the microarray data itself and retrieved from public databases. Using these data we demonstrate how this visualization concept can be efficiently used to identify certain features of genes and to detect inconsistencies in the data. Thus, the investigator has the possibility to get an overview of data from various sources and at the same time can gain a deeper insight into the structure of the combined data. The concept is not restricted to heatmaps, and can be used to extend further visualization techniques, such as profile plots. We found that our method is a powerful tool to integrate supplemental data into microarray visualizations and that it increases the efficiency of visual data exploration, which is a fundamental part of microarray data analyses.
机译：我们已经开发出一种方法，可以在可视化中集成微阵列数据和元信息，以便在数据探索和分析过程中指导研究者。引入了一个简单的数学框架，该框架使用评分功能将元信息映射到基因的相关性等级。为了探索该框架的潜力，我们扩展了具有新功能的传统热图，以图形方式表示相关性评级。通过附加的颜色梯度，缩放矩阵行的垂直高度，重新排列行或在热图中插入新列，可以直观地看到这些等级。这种可视化称为增强的热图。我们已经将我们的方法应用于酿酒酵母细胞周期的微阵列数据，并补充了我们既从微阵列数据本身衍生又从公共数据库检索的补充数据。使用这些数据，我们演示了如何将这种可视化概念有效地用于识别基因的某些特征并检测数据中的不一致之处。因此，研究人员有可能获得来自各种来源的数据概览，同时可以更深入地了解组合数据的结构。该概念不仅限于热图，还可以用于扩展其他可视化技术，例如轮廓图。我们发现我们的方法是将补充​​数据集成到微阵列可视化中的强大工具，并且它提高了可视数据探索的效率，这是微阵列数据分析的基本组成部分。

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