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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-03-25
摘要
Background Large-scale 生物 data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous 生物 factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Results We describe a general method for automatically reducing noise in large-scale 生物 data sets. This method uses an interaction 網絡 to identify groups of correlated or anti-correlated measurements that can be combined or "filtered" to better recover an underlying 生物 signal. Similar to the process of denoising an image, a single 網絡 filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to eac
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