Sequential imputation for missing values.
As missing values are often encountered in gene expression data, many imputation methods have been developed to substitute these unknown values with estimated values. Despite the presence of many imputation methods, these available techniques have some disadvantages. Some imputation techniques constrain the imputation of missing values to a limited set of genes, whereas other imputation methods optimise a more global criterion whereby the computation time of the method becomes infeasible. Others might be fast but inaccurate. Therefore in this paper a new, fast and accurate estimation procedure, called SEQimpute, is proposed. By introducing the idea of minimisation of a statistical distance rather than a Euclidean distance the method is intrinsically different from the thus far existing imputation methods. Moreover, this newly proposed method can be easily embedded in a multiple imputation technique which is better suited to highlight the uncertainties about the missing value estimates. A comparative study is performed to assess the estimation of the missing values by different imputation approaches. The proposed imputation method is shown to outperform some of the existing imputation methods in terms of accuracy and computation speed.
Verboven
Sabine
S
University of Antwerp, Department of Mathematics, Statistics & Actuarial Sciences, Prinsstraat 13, 2000 Antwerp, Belgium. sabine.verboven@ua.ac.be
Branden
Karlien Vanden
KV
Goos
Peter
P
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D007938:Leukemia / Q000378:metabolism; D016393:Lymphoma, B-Cell / Q000378:metabolism; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000378:metabolism