Applications of Causal Discovery Methods in Biomedicine

Transcription

1 Applications of Causal Discovery Methods in Biomedicine Sisi Ma New York University School of Medicine

2 NYU Center for Health Informatics & Bioinformatics Alexander Statnikov; NYU Psychiatry (PTSD Study) Isaac Galatzer-Levy; Arieh Shalev Netherland Bioinformatics Center (Regulatory Network Reconstruction Study) Patrick Kemmeren

3 Outline Causal structural learning from observational data: A case study Causal model guided experimental design

4 Causal Modeling for Post-traumatic Stress Post-traumatic Stress Response: Almost everyone experience at least one traumatic event in their life. Most people display acute stress responses. Acute stress responses diminish over time in most individuals, but about 10% - 20% people experience non-remitting stress responses long after the trauma. Persistent stress is detrimental to Physiological and psychological well-being of individuals. Galatzer-Levy et al., 2015; Ma et al. 2015; Galatzer-Levy et al., 2015 (submitted) 4

5 Causal Modeling for Post-traumatic Stress Remitting and Non-remitting Post-traumatic Stress Responses (Identified via Latent Growth Mixture Modeling) 5

6 Data: Causal Modeling for Post-traumatic Stress trauma survivors that were admitted to the ER were followed up to 4 month after the trauma. Patient history, clinical data, stress hormones, psychiatric related measurements were collected in the ER, 1 week, 1 month, and 4 month after the trauma. A total number of 135 variables were collected. Galatzer-Levy et al., 2015; Ma et al. 2015; Galatzer-Levy et al., 2016 (submitted) 6

7 Causal Modeling for Post-traumatic Stress Causal Discovery Question: What are the factors determining long term stress response after trauma exposure? Analysis Design: Apply local causal discovery algorithms (HITON-PC) to find the parent children sets for all measured variables A global causal graph depicting the relationship among all measured variables were constructed using the local to global framework LGL. Edges were oriented according the time that individual variables were measured.

8 Causal Modeling: HITON-PC Algorithm A B E T D C Local causal discovery method Easily parallelizable Easily extended for global causal discovery with the LGL framework. Aliferis et al.,

9 Causal Modeling: HITON-PC Algorithm Trace of HITON-PC A B E T D C C

10 Causal Modeling: Semi-Interleaved HITON-PC a more efficient implementation Efficient, and robust. Scalable to very big data. Easily extended for global causal discovery with the LGL framework. 10

11 Causal Modeling for Post-traumatic Stress Causal Discovery Question: What are the factors determining long term stress response after trauma exposure? Analysis Design: Apply local causal discovery algorithms (HITON-PC) to find the parent children sets for all measured variables A global causal graph depicting the relationship among all measured variables were constructed using the local to global framework LGL. Edges were oriented according the time that individual variables were measured.

12 Causal Modeling for Post-traumatic Stress The Global Causal Graph A very complicated model! 12

13 Causal Modeling for Post-traumatic Stress Example Causal Path Leading to Non-remitting Stress Responses 13

14 Causal Modeling for Post-traumatic Stress Potential intervention for non-remitting Stress Responses Acute physiological response to trauma 14

15 Causal Modeling for Post-traumatic Stress Potential Intervention for non-remitting Stress Responses Acute psychological response to trauma 15

16 Causal Modeling for Post-traumatic Stress Benefits: Provides a complete picture of the relationship among multi-modular data. Identify variables for potential intervention. Computationally efficient. Limitations: In general, complete network discovery can not be achieved with observational data alone. Violation of assumptions (faithfulness).

17 Outline Causal structural learning from observational data: A case study Causal model guided experimental design

18 Goal: Causal Model Guided Experimental Minimization and Adaptive Data Collection Combine observational data and experimental data to fully resolve causal pathways. Minimize the number of experiments need to be conducted 18

19 Causal Model-Guided Experimental Minimization and Adaptive Data Collection Simplified view of the Framework: 19

20 Causal Model Guided Experimental Minimization and Adaptive Data Collection The ODLP Algorithm: Goal: Resolving multiplicity in the network. Minimizing number of experiments. Output: Local causal pathway (parents and children) of the variable of interest. Statnikov et al.,

21 Causal Model Guided Experimental Minimization and Adaptive Data Collection Multiplicity/Target Information Equivalency: True structure of the network around the target 21

22 Causal Model Guided Experimental Minimization and Adaptive Data Collection 22

23 Causal Model Guided Experimental Minimization and Adaptive Data Collection The ODLP Algorithm Phase I: itie* 23

24 Causal Model Guided Experimental Minimization and Adaptive Data Collection True structure of the network around the target Phase I of the ODLP Algorithm: 24

25 Causal Model Guided Experimental Minimization and Adaptive Data Collection The ODLP Algorithm Phase II: Adaptively recommend experiments to perform, integrate experimental results to refine and orient the local causal pathway. (i.e. Identify Causes, Effects, and Passengers). 25

26 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP: Identifying effects Manipulate T and obtain experimental data D E. Mark all variables in V that change in D E due to manipulation of T as effects. effects 26

27 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP: direct and indirect effects Select an effect variable X that has neither been marked as indirect effect nor as direct effect. Manipulate X and obtain experimental data D E. Mark all effect variables that change in D E due to manipulation of X and belong to the same equivalence cluster as indirect effects. The last effect variable in an equivalent cluster that is not marked as indirect effect is a direct effect. Indirect effect 27

28 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP: Identifying Passengers Select an unmarked variable X from an equivalence cluster. Manipulate X and obtain experimental data D E. If T does not change in D E due to manipulation of X, mark X as a passenger and mark all other non-effect variables that change in D E due to manipulation of X as passengers; otherwise mark X as a cause. Passengers 28

29 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP: Identifying Causes For every cause X, mark X as a direct cause if there exist no other cause in the same equivalence cluster that changes due to manipulation of X; otherwise mark X as an Indirect cause. If there is an equivalence cluster that contains a single unmarked variable X and all marked variables in this cluster (if any) are only passengers and/or effects, then mark X as a direct cause. Statnikov et al.,

30 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP vs Other Algorithms: Performance on Real Biological Data Ma et al.,

31 Causal Model Guided Experimental Minimization and Adaptive Data Collection ODLP vs Other Algorithms: Performance on Real Biological Data 31

32 Reference 1. Ma, S., Kemmeren, P., Gresham, D., & Statnikov, A. (2014). De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae. PloS one, 9(9), e Statnikov, A., Ma, S., Henaff, M., Lytkin, N., Efstathiadis, E., Peskin, E. R., & Aliferis, C. F. (2015). Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery. J Mach Learn Res. 3. Ma, S., Kemmeren, P., Aliferis, C. F., & Statnikov, A. (2016). An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation. Scientific reports, Aliferis, C. F., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. D. (2010). Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation. The Journal of Machine Learning Research, 11, Aliferis, C. F., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. D. (2010). Local causal and markov blanket induction for causal discovery and feature selection for classification part ii: Analysis and extensions.the Journal of Machine Learning Research, 11,

33 Thanks!