Iterative bicluster-based Bayesian principal component analysis and least squares for missing-value imputation in microarray and RNA-sequencing data.

Microarray and RNA-sequencing (RNA-seq) techniques each produce gene expression data that can be expressed as a matrix that often contains missing values. Thus, a process of missing-value imputation that uses coherence information of the dataset is necessary. Existing imputation methods, such as iterative bicluster-based least squares (bi-iLS), use biclustering to estimate the missing values because genes are only similar under correlative experimental conditions.

Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data.

HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions.