Highlighting nonlinear patterns in population genetics datasets

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Alanis Lobato, Gregorio ^a,b   Cannistraci, Carlo Vittorio ^c   Eriksson, Anders ^a,d   Manica, Andrea ^d   Ravasi, Timothy ^a,b  

^a Electrical and Mathematical Sciences and Engineering Division   (Saudi Arabia)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^c DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^d UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ASIAN CONTINENTAL ANCESTRY GROUP; GENETIC DATABASE; GENETICS; GENOTYPE; HUMAN; NONLINEAR SYSTEM; NONPARAMETRIC TEST; POPULATION GENETICS; PRINCIPAL COMPONENT ANALYSIS;

ALGORITHMS; ASIAN CONTINENTAL ANCESTRY GROUP; DATABASES, GENETIC; GENETICS, POPULATION; GENOTYPE; HUMANS; NONLINEAR DYNAMICS; PRINCIPAL COMPONENT ANALYSIS; STATISTICS, NONPARAMETRIC;

EID: 84944779923     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep08140     Document Type: Article

References (24)

1. Visscher, P. M., Brown, M. A., McCarthy, M. I., Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7-24 (2012).
2. Jolliffe, I. T. Principal Component Analysis. 489 (Springer, 2002).
3. Menozzi, P., Piazza, A., Cavalli-Sforza, L. Synthetic maps of human gene frequencies in Europeans. Science. 201, 786-792 (1978).
4. Patterson, N., Price, A. L., Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
5. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904-909 (2006).
6. Novembre, J., Stephens, M. Interpreting principal component analyses of spatial population genetic variation. Nat. Genet. 40, 646-649 (2008).
7. McVean, G. A genealogical interpretation of principal components analysis. PLoS Genet. 5, e1000686 (2009).
8. Cannistraci, C. V., Ravasi, T., Montevecchi, F. M., Ideker, T., Alessio, M. Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes. Bioinformatics 26, i531-i539 (2010).
9. Cannistraci, C. V., Alanis-Lobato, G., Ravasi, T. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. Bioinformatics 29, i199-i209 (2013).
10. Aaronson, S., Kuperberg, G., Granade, C., Russo, V. Complexity Zoo. (2005) at ,http://www.complexityzoo.com. Date of access: 23/04/2014.
11. Moitinho-Silva, L. et al. Specificity and transcriptional activity of microbiota associated with low and high microbial abundance sponges from the Red Sea. Mol. Ecol. 23, 1348-1363 (2013).
12. Zagar, L. et al. Stage prediction of embryonic stem cell differentiation from genome-wide expression data. Bioinformatics 27, 2546-2553 (2011).
13. Ryu, T., Seridi, L., Cannistraci, C. V., Ravasi, T. The evolution of ultraconserved elements with different phylogenetic origins. BMC Evol. Biol. 12, 1-11 (2012).
14. Tenenbaum, J. B., de Silva, V., Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323 (2000).
15. Shieh, A. D., Hashimoto, T. B., Airoldi, E. M. Tree preserving embedding. Proc. Natl. Acad. Sci. U. S. A. 108, 16916-16921 (2011).
16. Abdulla, M. A. et al. Mapping human genetic diversity in Asia. Science. 326, 1541-1545 (2009).
17. Ngamphiw, C. et al. PanSNPdb: the Pan-Asian SNP genotyping database. PLoS One 6, e21451 (2011).
18. Hatin, W. I. et al. Population genetic structure of peninsular Malaysia Malay subethnic groups. PLoS One 6, e18312 (2011).
19. Gourraud, P.-A., Génin, E., Cambon-Thomsen, A. Handling missing values in population data: consequences for maximum likelihood estimation of haplotype frequencies. Eur. J. Hum. Genet. 12, 805-812 (2004).
20. Baraldi, A. N., Enders, C. K. An introduction to modern missing data analyses. J. Sch. Psychol. 48, 5-37 (2010).
21. Gelman, A., Hill, J. in Data Anal. Using Regres. Multilevel/Hierarchical Model. 529-543 (Cambridge University Press, 2006).
22. Purcell, S. et al. PLINK: a tool set for whole-genome association and populationbased linkage analyses. Am. J. Hum. Genet. 81, 559-575 (2007).
23. Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., Moons, K. G. M. Review: a gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59, 1087-1091 (2006).
24. Shaw, B., Jebara, T. Structure preserving embedding. in Proc. 26th Annu. Int. Conf. Mach. Learn. 937-944 (ACM Press, 2009). doi:10.1145/1553374.1553494