title: Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.
abstract: BACKGROUND
  Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods.
  
  
  RESULTS
  The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm.
  
  
  BACKGROUND
  The CMVE software is available upon request from the authors.
authors.affiliation: Gippsland School of Computing and Information Technology, Monash University, VIC 3842, Australia. Shoaib.Sehgal@infotech.monash.edu.au
authors.forename: Muhammad Shoaib B
authors.initials: MS
authors.lastname: Sehgal
authors.forename: Iqbal
authors.initials: I
authors.lastname: Gondal
authors.forename: Laurence S
authors.initials: LS
authors.lastname: Dooley
mesh_terms: D000465:Algorithms; D019313:BRCA1 Protein / Q000235:genetics / Q000378:metabolism*; D024682:BRCA2 Protein / Q000235:genetics / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D018401:Sample Size
chemical_list: D019313:BRCA1 Protein; D024682:BRCA2 Protein; D029701:Saccharomyces cerevisiae Proteins