GUEST: an R package for handling estimation of graphical structure and multiclassification for error-prone gene expression data

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. 12
• Main Authors: Chen, Li-Pang, Tsao, Hui-Shan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.11.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Applications Note; Availability; Bioinformatics; Computational Biology - methods; Computer graphics; Covariance matrix; Data science; Discriminant Analysis; Error detection; Gene Expression; Gene Expression Profiling - methods; Humans; Machine learning; Random variables; Software; Supervised learning
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btae731

Summary In bioinformatics studies, understanding the network structure of gene expression variables is one of the main interests. In the framework of data science, graphical models have been widely used to characterize the dependence structure among multivariate random variables. However, the gene expression data possibly suffer from ultrahigh-dimensionality and measurement error, which make the detection of network structure challenging and difficult. The other important application of gene expression variables is to provide information to classify subjects into various tumors or diseases. In supervised learning, while linear discriminant analysis is a commonly used approach, the conventional implementation is limited in precisely measured variables and computation of their inverse covariance matrix, which is known as the precision matrix. To tackle those challenges and provide a reliable estimation procedure for public use, we develop the R package GUEST, which is known as Graphical models for Ultrahigh-dimensional and Error-prone data by the booSTing algorithm. This R package aims to deal with measurement error effects in high-dimensional variables under various distributions and then applies the boosting algorithm to identify the network structure and estimate the precision matrix. When the precision matrix is estimated, it can be used to construct the linear discriminant function and improve the accuracy of the classification. Availability and implementation The R package is available on https://cran.r-project.org/web/packages/GUEST/index.html.