title: Missing value imputation for epistatic MAPs.
abstract: BACKGROUND
  Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.
  
  
  RESULTS
  We identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.
  
  
  CONCLUSIONS
  We address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.
authors.affiliation: School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. colm.ryan@ucd.ie
authors.forename: Colm
authors.initials: C
authors.lastname: Ryan
authors.forename: Derek
authors.initials: D
authors.lastname: Greene
authors.forename: Gerard
authors.initials: G
authors.lastname: Cagney
authors.forename: Pádraig
authors.initials: P
authors.lastname: Cunningham
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D004843:Epistasis, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
references.citation: Bioinformatics. 2001 Jun;17(6):520-5
references.pmid: 11395428
references.citation: Cell. 2005 Nov 4;123(3):507-19
references.pmid: 16269340
references.citation: Proc Natl Acad Sci U S A. 2004 Nov 2;101(44):15682-7
references.pmid: 15496468
references.citation: BMC Bioinformatics. 2008 Jan 10;9:12
references.pmid: 18186917
references.citation: Nat Genet. 2000 May;25(1):25-9
references.pmid: 10802651
references.citation: Bioinformatics. 2003 Nov 1;19(16):2088-96
references.pmid: 14594714
references.citation: Bioinformatics. 2005 Jan 15;21(2):187-98
references.pmid: 15333461
references.citation: PLoS One. 2008 Sep 26;3(9):e3284
references.pmid: 18818762
references.citation: Nat Genet. 2007 Feb;39(2):199-206
references.pmid: 17206143
references.citation: Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8
references.pmid: 9843981
references.citation: Science. 2004 Feb 6;303(5659):808-13
references.pmid: 14764870
references.citation: PLoS Comput Biol. 2008 Apr 18;4(4):e1000065
references.pmid: 18421374
references.citation: Science. 2008 Oct 17;322(5900):405-10
references.pmid: 18818364
references.citation: Nucleic Acids Res. 1998 Jan 1;26(1):73-9
references.pmid: 9399804
references.citation: Mol Syst Biol. 2008;4:209
references.pmid: 18628749
references.citation: Nat Methods. 2008 Sep;5(9):781-7
references.pmid: 19160513
references.citation: Nat Methods. 2008 Aug;5(8):711-8
references.pmid: 18622397
references.citation: Nucleic Acids Res. 2009 Feb;37(3):825-31
references.pmid: 19095691
references.citation: Cell. 2009 Mar 6;136(5):952-63
references.pmid: 19269370
references.citation: Bioinformatics. 2008 Oct 15;24(20):2376-83
references.pmid: 18718945
references.citation: Nat Genet. 2006 Aug;38(8):896-903
references.pmid: 16845399
references.citation: Genome Res. 2008 Dec;18(12):1991-2004
references.pmid: 18832443
references.citation: Genome Biol. 2006;7(7):R63
references.pmid: 16859555
references.citation: Nat Biotechnol. 2005 May;23(5):561-6
references.pmid: 15877074
references.citation: Science. 2001 Dec 14;294(5550):2364-8
references.pmid: 11743205
references.citation: Nucleic Acids Res. 2010 Jan;38(Database issue):D502-7
references.pmid: 19880385
references.citation: Mol Cell. 2008 Dec 5;32(5):735-46
references.pmid: 19061648
references.citation: Bioinformatics. 2008 Dec 1;24(23):2733-9
references.pmid: 18805797
references.citation: BMC Bioinformatics. 2004 Aug 23;5:114
references.pmid: 15324460
references.citation: Nucleic Acids Res. 2004 Feb 20;32(3):e34
references.pmid: 14978222
references.citation: Nature. 2007 Apr 12;446(7137):806-10
references.pmid: 17314980