Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition

DOI10.1214/18-AOAS1228zbMath1423.62152WikidataQ104680883 ScholiaQ104680883MaRDI QIDQ2318675

Jonathan Fischer, Yun S. Song, Miaoyan Wang

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/1560758439

zbMATH Keywords

clusteringtensor decompositiongene expressiontensor projection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items (9)

An optimal statistical and computational framework for generalized tensor estimation ⋮ Tensor clustering with planted structures: statistical optimality and computational limits ⋮ Generalized Tensor Decomposition With Features on Multiple Modes ⋮ Generalized co-clustering analysis via regularized alternating least squares ⋮ A tensor decomposition model for longitudinal microbiome studies ⋮ Modal clustering of matrix-variate data ⋮ Cause-of-death mortality forecasting using adaptive penalized tensor decompositions ⋮ MultiCluster ⋮ Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition

Uses Software

• JIVE
• SigClust
• t-SNE
• GeneCards
• CountClust

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Tensor Decompositions and Applications
• Operator norm inequalities between tensor unfoldings on the partition lattice
• Sparse logistic principal components analysis for binary data
• A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood
• Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
• Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
• Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition
• Exact matrix completion via convex optimization
• Tensor decompositions for learning latent variable models
• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
• A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization
• Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
• Most Tensor Problems Are NP-Hard

This page was built for publication: Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition