New methods for separating causes from effects in genomics data

By Alexander R. Statnikov, Mikael Henaff, Nikita I. Lytkin, Constantin F. Aliferis. 2012

37 citations recorded by the source corpus.

The discovery of molecular pathways is a challenging problem and its solution relies on the identification of causal molecular interactions in genomics data. Causal molecular interactions can be discovered using randomized experiments; however such experiments are often costly, infeasible, or unethical. Fortunately, algorithms that infer causal interactions from observational data have been in development for decades, predominantly in the quantitative sciences, and many of them have recently been applied to genomics data. While these algorithms can infer unoriented causal interactions between involved molecular variables (i.e., without specifying which one is the cause and which one is the effect), causally orienting all inferred molecular interactions was assumed to be an unsolvable problem until recently. In this work, we use transcription factor-target gene regulatory interactions in three different organisms to evaluate a new family of methods that, given observational data for just two causally related variables, can determine which one is the cause and which one is the effect. We have found that a particular family of causal orientation methods (IGCI Gaussian) is often able to accurately infer directionality of causal interactions, and that these methods usually outperform other causal orientation techniques. We also introduced a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods. The ensemble method was found to be more accurate than any best individual causal orientation method in the tested data. This work represents a first step towards establishing context for practical use of causal orientation methods in the genomics domain. We have found that some causal orientation methodologies yield accurate predictions of causal orientation in genomics data, and we have improved on this capability with a novel ensemble method. Our results suggest that these methods have the potential to facilitate reconstruction of molecular pathways by minimizing the number of required randomized experiments to find causal directionality and by avoiding experiments that are infeasible and/or unethical.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “The former conclusion about the performance of ANM-pHSIC is in line with earlier reports, but the latter conclusion is surprising, considering that good performance of IGCI-slope and IGCI-ent-1sp has been reported on several occasions in earlier work (Daniušis et al., 2010; Mooij et al., 2010; Janzi”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “The former conclusion is in line with earlier reports, but the latter conclusion is surprising, considering that good performance of IGCI-slope and IGCI-ent-1sp has been reported on several occassions in earlier work (Daniušis et al., 2010; Mooij et al., 2010; Janzing et al., 2012; Statnikov et al.,”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, unknown): “| The reader is referred to Statnikov et al. (2012) and Mooij et al. (2016) for a thorough review and benchmark of the pairwise methods in the bivariate case.”
Score-based causal learning in additive noise models (2013, background): “Some real-life examples include identifying gene expression networks [Statnikov et al., 2012, Stekhoven et al., 2012] and analysing fMRI data from the human brain [Ramsey et al., 2010].”
Causal inference in partially linear structural equation models (2016, background): “Examples include the identification of causal molecular mechanisms in genomics [24, 25], the investigation of causal relations among activity in brain regions from fMRI data [19] or the search for causal associations in public health [7].”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, background): “The reader is referred to Statnikov et al. (2012); Mooij et al. (2016); Guyon et al. (2019) for a thorough presentation of the bivariate problem.”