Heuristic Non Parametric Collateral Missing Value Imputation: A Step Towards Robust Post-genomic Knowledge Discovery

Abstract

Abstract. Microarrays are able to measure the patterns of expression of thousands of genes in a genome to give profiles that facilitate much faster analysis of biological processes for diagnosis, prognosis and tailored drug discovery. Microarrays, however, commonly have missing values which can result in erroneous downstream analysis.

How to Improve Postgenomic Knowledge Discovery Using Imputation

“…Both approaches are high variance, with neither exploiting the underlying data correlations which can lead to higher estimation errors [ 9 ]. The prevailing wisdom is to accurately estimate missing values by exploiting the latent correlation structure of the microarray data [ 8 , 10 ], as manifested by the development of numerous microarray imputation techniques including collateral missing value estimation (CMVE) [ 11 ], singular value decomposition impute (SVDImpute) [ 9 ], K-nearest neighbour (KNN) [ 9 ], least square impute (LSImpute) [ 10 ], local LSimpute (LLSImpute) [ 8 ], Bayesian principal component analysis (BPCA) [ 12 ], a set of theoretic framework based on projection onto convex sets imputation (POCS Impute) method [ 13 ] and most recently, heuristic collateral missing value imputation (HCMVI) [ 14 ]. In addition, other methods which use contextual information include gene ontology-based imputation (GOImpute) [ 15 ] and metadata-based imputation technique [ 16 ].…”