In biomedical research, large-scale profiling of gene expression has become routine and offers a valuable means to evaluate changes in onset and progression of diseases, in particular cancer. An overwhelming amount of cancer genomics data has become publicly available, and the complexity of these data makes it a challenge to perform in silico data exploration, integration and analysis, in particular for scientists lacking a background in computational programming or informatics. Many web interface tools make these large datasets accessible but are limited to process large datasets. To accelerate the translation of genomic data into new insights, we provide a simple method to explore and select data from cancer genomic datasets to generate gene-expression profiles of subsets that are of specific genetic, biological or clinical interest.

中文翻译：

---

一种简单的计算机方法，可从癌症基因组学数据的子集生成基因表达谱。

在生物医学研究中，基因表达的大规模概况分析已成为常规做法，并提供了宝贵的手段来评估疾病（尤其是癌症）的发作和发展变化。大量的癌症基因组学数据已经公开可用，并且这些数据的复杂性使其难以进行计算机模拟数据探索，集成和分析，特别是针对缺乏计算编程或信息学背景的科学家。许多Web界面工具使这些大型数据集可访问，但仅限于处理大型数据集。为了加速将基因组数据转换为新见解，我们提供了一种简单的方法来探索和选择癌症基因组数据集中的数据，以生成具有特定遗传，生物学或临床意义的子集的基因表达谱。