TV-DCT: METHOD TO IMPUTE GENE EXPRESSION DATA USING DCT BASED SPARSITY AND TOTAL VARIATION DENOISING

Most of the bioinformatics tools used in the analysis of gene expression data require complete data matrices. Missing values in data can adversely influence the downstream analysis for diagnostics and treatment. Several methods to impute missing values in gene data have been developed. However, most of these work at high levels of observability. In this paper, we have proposed a novel 2-stage method, namely, TV-DCT for imputing incomplete gene expression matrices using Total Variation denoising and Discrete Cosine Transform Domain Sparsity (TV-DCT) that achieves smaller imputation errors, consistently, at all levels of observability. The proposed method has been compared with three state-of-the-art matrix completion methods on three different cancer datasets and is observed to perform better. The validation of imputed data has been demonstrated on the application of classification.