Imputing and Predicting Quantitative Genetic Interactions in Epistatic MAPs

  • Colm Ryan
  • Gerard Cagney
  • Nevan Krogan
  • Pádraig Cunningham
  • Derek Greene
Part of the Methods in Molecular Biology book series (MIMB, volume 781)


Mapping epistatic (or genetic) interactions has emerged as an important network biology approach for establishing functional relationships among genes and proteins. Epistasis networks are complementary to physical protein interaction networks, providing valuable insight into both the function of individual genes and the overall wiring of the cell. A high-throughput method termed “epistatic mini array profiles” (E-MAPs) was recently developed in yeast to quantify alleviating or aggravating interactions between gene pairs. The typical output of an E-MAP experiment is a large symmetric matrix of interaction scores. One problem with this data is the large amount of missing values – interactions that cannot be measured during the high-throughput process or whose measurements were discarded due to quality filtering steps. These missing values can reduce the effectiveness of some data analysis techniques and prevent the use of others. Here, we discuss one solution to this problem, imputation using nearest neighbors, and give practical examples of the use of a freely available implementation of this method.

Key words

Protein interactions Genetic interactions Epistasis Imputation Biological networks 


  1. 1.
    Collins, S.R., Roguev, A. & Krogan, N.J. Quantitative Genetic Interaction Mapping Using the E-MAP Approach. Guide to Yeast Genetics: Functional Genomics, Proteomics, and Other Systems Analysis Volume 470, 205–231 (2010).Google Scholar
  2. 2.
    Collins, S.R., Schuldiner, M., Krogan, N.J. & Weissman, J.S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol 7, R63 (2006).Google Scholar
  3. 3.
    Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95, 14863–8 (1998).Google Scholar
  4. 4.
    Tuikkala, J., Elo, L., Nevalainen, O. & Aittokallio, T. Missing value imputation improves clustering and interpretation of gene expression microarray data. BMC Bioinformatics 9, 202 (2008).Google Scholar
  5. 5.
    Wong, S.L. et al. Combining biological networks to predict genetic interactions. Proceedings of the National Academy of Sciences of the United States of America 101, 15682–15687 (2004).Google Scholar
  6. 6.
    Kelley, R. & Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nat Biotech 23, 561–566 (2005).Google Scholar
  7. 7.
    Paladugu, S., Zhao, S., Ray, A. & Raval, A. Mining protein networks for synthetic genetic interactions. BMC Bioinformatics 9, 426 (2008).Google Scholar
  8. 8.
    Qi, Y., Suhail, Y., Lin, Y., Boeke, J.D. & Bader, J.S. Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions. Genome Research 18, 1991–2004 (2008).Google Scholar
  9. 9.
    Chipman, K. & Singh, A. Predicting genetic interactions with random walks on biological networks. BMC Bioinformatics 10, 17 (2009).Google Scholar
  10. 10.
    Ulitsky, I., Krogan, N. & Shamir, R. Towards accurate imputation of quantitative genetic interactions. Genome Biology 10, R140 (2009).Google Scholar
  11. 11.
    Ryan, C., Greene, D., Cagney, G. & Cunningham, P. Missing value imputation for epistatic MAPs. BMC Bioinformatics 11, 197 (2010).Google Scholar
  12. 12.
    Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 (2001).Google Scholar
  13. 13.
    Kim, H., Golub, G.H. & Park, H. Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21, 187–198 (2005).Google Scholar
  14. 14.
    Oba, S. et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088–2096 (2003).Google Scholar
  15. 15.
    Bø, T.H., Dysvik, B. & Jonassen, I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res. 32, e34 (2004).Google Scholar
  16. 16.
    Schuldiner, M. et al. Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile. Cell 123, 507–519 (2005).Google Scholar
  17. 17.
    Roguev, A., Wiren, M., Weissman, J.S. & Krogan, N.J. High-throughput genetic interaction mapping in the fission yeast Schizosaccharomyces pombe. Nat Meth 4, 861–866 (2007).Google Scholar
  18. 18.
    Typas, A. et al. High-throughput, quantitative analyses of genetic interactions in E. coli. Nat Meth 5, 781–787 (2008).Google Scholar
  19. 19.
    Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A.G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet 38, 896–903 (2006).Google Scholar
  20. 20.
    EMAP toolbox for MATLAB. at <>
  21. 21.
    Team, R.D.C. R: A Language and Environment for Statistical Computing. 3, 2673.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Colm Ryan
    • 1
  • Gerard Cagney
    • 2
  • Nevan Krogan
    • 3
  • Pádraig Cunningham
    • 1
  • Derek Greene
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublinIreland
  2. 2.Conway Institute of Biomolecular and Biomedical ResearchUniversity College DublinDublinIreland
  3. 3.Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical ResearchUniversity of CaliforniaSan FranciscoUSA

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