A genetic algorithm for designing microarray experiments
Tóm tắt
Heuristic techniques of optimization can be useful in designing complex experiments, such as microarray experiments. They have advantages over the traditional methods of optimization, particularly in situations where the search space is discrete. In this paper, a search procedure based on a genetic algorithm is proposed to find optimal (efficient) designs for both one- and multi-factor experiments. A genetic algorithm is a heuristic optimization method that exploits the biological evolution to obtain a solution of the problem. As an example, optimal designs for
$$3\times 2$$
factorial microarray experiments are presented for different numbers of arrays and for various sets of research questions. Comparisons between different operators of the genetic algorithm are performed by simulation studies.
Tài liệu tham khảo
Bailey R (2007) Designs for two-colour microarray experiments. J R Stat Soc Ser C (Appl Stat) 56(4):365–394
Bailey R, Schiffl K, Hilgers R-D (2013) A note on robustness of D-optimal block designs for two-colour microarray experiments. J Stat Plan Inference 143(7):1195–1202
Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21(1 Suppl):33–37
Cheng C-S (1980) On the E-optimality of some block designs. J R Stat Soc Ser B 42(2):199–204
Duffull SB, Retout S, Mentré F (2002) The use of simulated annealing for finding optimal population designs. Comput Methods Progr Biomed 69(1):25–35
Glonek GFV, Solomon PJ (2004) Factorial and time course designs for cDNA microarray experiments. Biostatistics 5(1):89–111
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Gondro C, Kinghorn BP (2008) Optimization of cDNA microarray experimental designs using an evolutionary algorithm. IEEE/ACM Trans Comput Biol Bioinform 5(4):630–638
Hamada M, Martz HF, Reese CS, Wilson AG (2001) Finding near-optimal Bayesian experimental designs via genetic algorithms. Am Stat 55(3):175–181
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
John JA, Mitchell TJ (1977) Optimal incomplete block designs. J R Stat Soc Ser B (Methodol) 39(1):39–43
Kerr MK, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics 2(2):183–201
Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. J Comput Biol 7(6):819–837
Landgrebe J, Bretz F, Brunner E (2006) Efficient design and analysis of two color factorial microarray design. Comput Stat Data Anal 50(2):499–517
Latif AHMM, Bretz F, Brunner E (2009) Robustness considerations in selecting efficient two-color microarray designs. Bioinformatics 25(18):2355–2361
Vinciotti V, Khanin R, D’Alimonte D, Liu X, Cattini N, Hotchkiss G, Bucca G, de Jesus O, Rasaiyaah J, Smith CP, Kellam P, Wit E (2005) An experimental evaluation of a loop versus a reference design for two-channel microarrays. Bioinformatics 21(4):492–501
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85
Wit E, Nobile A, Khanin R (2005) Near-optimal designs for dual channel microarray studies. J R Stat Soc Ser C (Appl Stat) 54(5):817–830
Wolfinger RD, Gibson G, Wilfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 8(6):625–637
Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30(4):e15