CT-NNBI: Method to Impute Gene Expression Data using DCT Based Sparsity and Nuclear Norm Constraint with Split Bregman Iteration

High dimensional genomics data such as microarray gene expression and RNA sequencing, generally suffers from missing values. Incomplete data can adversely affect the downstream analysis for diagnostics and treatment. Several methods to impute missing values in gene expression data have been developed, but most of these work at high levels of observability. In this paper, we have proposed a nove12-stage method, namely, CT-NNBI of imputing incomplete gene expression matrices using Discrete Cosine Transform Domain Sparsity and Nuclear Norm Constraint with Split Bregman Iteration (CT-NNBI) that yields smaller imputation errors, consistently, at all levels of observability. The proposed method has been compared with the state-of-the-art matrix completion methods on three different cancer dataset and is observed to perform better. The validation of imputed data has been demonstrated on the application of classification.