Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
STAR Protoc. 2021 Nov 23;2(4):100955. doi: 10.1016/j.xpro.2021.100955. eCollection 2021 Dec 17.
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
CausalPath（causalpath.org）根据生物途径的先验知识评估蛋白质组学测量值，并推断测量特征（如全局蛋白质和磷酸化蛋白质水平）变化之间的因果关系。它使用途径资源来确定可观察的组学特征之间的潜在因果关系，这些关系称为先验关系。受蛋白质组学特征支持的先验关系子集将被报告并评估其统计显著性。最终结果是一个解释实验数据集观察到的模式的信号转导网络模型。有关此协议的使用和执行的详细信息，请参阅 Babur 等人（2021 年）。
