Document Detail


New methods for separating causes from effects in genomics data.
MedLine Citation:
PMID:  23282373     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
Authors:
Alexander Statnikov; Mikael Henaff; Nikita I Lytkin; Constantin F Aliferis
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2012-12-17
Journal Detail:
Title:  BMC genomics     Volume:  13 Suppl 8     ISSN:  1471-2164     ISO Abbreviation:  BMC Genomics     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-03     Completed Date:  2013-06-12     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  100965258     Medline TA:  BMC Genomics     Country:  England    
Other Details:
Languages:  eng     Pagination:  S22     Citation Subset:  IM    
Affiliation:
Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY 10016, USA. alexander.statnikov@med.nyu.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Area Under Curve
Databases, Factual
Escherichia coli / genetics,  metabolism
Escherichia coli Proteins / genetics,  metabolism
Gene Regulatory Networks
Genomics*
Humans
Precursor T-Cell Lymphoblastic Leukemia-Lymphoma / genetics,  metabolism
ROC Curve
Receptor, Notch1 / genetics,  metabolism
Saccharomyces cerevisiae / genetics,  metabolism
Saccharomyces cerevisiae Proteins / genetics,  metabolism
Transcription Factor RelA / genetics,  metabolism
Grant Support
ID/Acronym/Agency:
1R01LM011179-01A1/LM/NLM NIH HHS; 1UL1RR029893/RR/NCRR NIH HHS; R01 LM011179/LM/NLM NIH HHS; UL1 RR029893/RR/NCRR NIH HHS
Chemical
Reg. No./Substance:
0/Escherichia coli Proteins; 0/Receptor, Notch1; 0/Saccharomyces cerevisiae Proteins; 0/Transcription Factor RelA
Comments/Corrections

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