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

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 ite...

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Bibliographic Details
Main Authors: Saskya Mary Soemartojo, Titin Siswantining, Yoel Fernando, Devvi Sarwinda, Herley Shaori Al-Ash, Sarah Syarofina, Noval Saputra
Format: Article
Language:English
Published: AIMS Press 2022-06-01
Series:Mathematical Biosciences and Engineering
Subjects:
biclustering
microarray
normalized root mean square error
rna sequencing
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022405?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2022405?viewType=HTML

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