Background: Knockdown or overexpression of genes is widely used to identify genes that play important roles in many\naspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that\nallow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However,\nconventional methods rely heavily on the assumption of normality and they often give incorrect results when the\nassumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method\nto test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when\nthe directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a\ncascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method\nfor estimating DAGs.\nResults: We demonstrate that causal inference with the non-paranormal method significantly improves the performance\nin estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA\noutperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and\nregulators that control the browning of white adipocytes in mice. Our results show that performance improvement in\nestimating DAGs contributes to an accurate estimation of causal effects.\nConclusions: Although the simplest alternative procedure was used, our proposed method enables us to design efficient\nintervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of\nits generality.
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