Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data

DOI10.1214/18-AOAS1227zbMath1423.62143WikidataQ114599292 ScholiaQ114599292MaRDI QIDQ2318673

Julia Fukuyama

Publication date: 15 August 2019

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

Full work available at URL: https://projecteuclid.org/euclid.aoas/1560758437

zbMATH Keywords

empirical BayesphylogenyPCAmicrobiomeadaptive generalized principal components analysisadaptive gPCA

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data ⋮ A statistical perspective on the challenges in molecular microbial biology

Uses Software

• R
• Bioconductor
• ape
• adaptiveGPCA
• PMA
• DESeq2

Cites Work

• Unnamed Item
• A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
• Analysis of a data matrix and a graph: metagenomic data and the phylogenetic tree
• Properties and refinements of the fused Lasso
• DESeq2
• Kernel-penalized regression for analysis of microbiome data
• From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis
• Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data
• Multivariate data analysis: The French way
• Spatial smoothing and hot spot detection for CGH data using the fused lasso
• Sparsity and Smoothness Via the Fused Lasso
• A Generalized Least-Square Matrix Decomposition
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions

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