Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma

DOI10.1214/18-AOAS1136zbMath1405.62159arXiv1503.07990WikidataQ129264519 ScholiaQ129264519MaRDI QIDQ1621047

Rasmus Froberg Brøndum, Poul Svante Eriksen, Martin Bøgsted, Anders Ellern Bilgrau, Karen Dybkær

Publication date: 15 November 2018

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.07990

zbMATH Keywords

network analysismeta-analysiscovariance estimationintegrative analysisprecision estimation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Systems biology, networks (92C42)

Uses Software

• R
• glasso
• affy
• Rcpp
• knitr
• dendextend
• g Profiler
• GitHub
• correlateR

Cites Work

• Unnamed Item
• Adjusting batch effects in microarray expression data using empirical Bayes methods
• Sparse inverse covariance estimation with the graphical lasso
• Ridge estimation of inverse covariance matrices from high-dimensional data
• Permutation \(p\)-values should never be zero: calculating exact \(p\)-values when permutations are randomly drawn
• High-dimensional graphs and variable selection with the Lasso
• Exploration, normalization, and summaries of high density oligonucleotide array probe level data

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