Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data

• Published in: Bioinformatics Vol. 21; no. 10; pp. 2417 - 2423
• Main Authors: Sehgal, Muhammad Shoaib B., Gondal, Iqbal, Dooley, Laurence S.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.05.2005; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological analysis; Biological and medical sciences; BRCA1 Protein - genetics; BRCA1 Protein - metabolism; BRCA2 Protein - genetics; BRCA2 Protein - metabolism; Data Interpretation, Statistical; Female; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Humans; Likelihood Functions; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Biological; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Ovarian cancer; Ovarian Neoplasms - genetics; Ovarian Neoplasms - metabolism; Principal components analysis; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Sample Size; Statistical analysis; Time series; Yeasts; Data analysis; Statistical analysis; Yeast; Multiple; Number; Estimation; Use; Biology; Malignant tumor; Microarray; Learning algorithm
• ISSN: 1367-4803; 0266-7061; 1460-2059; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bti345

Motivation: 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. Availability: The CMVE software is available upon request from the authors. Contact: Shoaib.Sehgal@infotech.monash.edu.au