The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting

中文翻译：

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FC1000：系统性扰动人类细胞的标准化基因表达变化

对人类细胞中遗传和化学扰动的转录反应的系统研究仍处于早期阶段。迄今为止最大的可用数据集是新发布的 L1000 纲要。凭借其处理过的人类细胞的 130 万个基因表达谱，它为生物医学数据挖掘提供了许多机会，但也带来了新维度的数据标准化挑战。我们开发了一种新颖实用的方法，以基于 RUV（删除不需要的变化）统计框架从 L1000 中获得对倍数变化响应曲线的准确估计。将 RUV 扩展到大数据设置，我们提出了一种估计程序，其中基础 RUV 模型通过数据集特定统计措施的反馈进行调整，反映