Missing value imputation in DNA microarrays based on conjugate gradient method

Authors:
Fatemeh Dorri;Paeiz Azmi;Faezeh Dorri
Affiliations:
School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1;Department of Computer and Electrical Engineering, Modares University, Jalal Ale Ahmad Highway, P.O. Box: 14115-111, Tehran, Iran;Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Enghelab Ave, P.O. Box: 11155-4563, Tehran, Iran
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods.