期刊
JOURNAL OF PROTEOME RESEARCH
卷 15, 期 3, 页码 683-690出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.5b00911
关键词
causal inference; big data; causal networks; Bayesian networks; experimental design
Causal inference, the task of uncovering regulatory relationships between components of biomolecular pathways and networks, is a primary goal of many high throughput investigations. Statistical associations between observed protein concentrations can suggest an enticing number of hypotheses regarding the underlying causal interactions, but when do such associations reflect the underlying causal biomolecular mechanisms? The goal of this perspective is to provide suggestions for causal inference in large-scale experiments, which utilize high-throughput technologies such as mass spectrometry-based proteomics. We describe in nontechnical terms the pitfalls of inference in large data sets and suggest methods to overcome these pitfalls and reliably find regulatory associations.
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