Compressed spectral screening for large-scale differential correlation analysis with application in selecting glioblastoma gene modules

DOI10.1214/23-aoas1771arXiv2111.03721MaRDI QIDQ6138655

Tianxi Li, Xiwei Tang, Ajay Chatrath

Publication date: 16 January 2024

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

Full work available at URL: https://arxiv.org/abs/2111.03721

zbMATH Keywords

spectral methodsgene coexpressionhigh-dimensional correlation matricesdifferential correlation analysis

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• Adjusting batch effects in microarray expression data using empirical Bayes methods
• Estimation of high-dimensional graphical models using regularized score matching
• Sparse inverse covariance estimation with the graphical lasso
• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• Two sample tests for high-dimensional covariance matrices
• Network cross-validation by edge sampling
• Joint estimation of precision matrices in heterogeneous populations
• A spectral algorithm for learning hidden Markov models
• Inferring multiple graphical structures
• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies
• Spectral clustering and the high-dimensional stochastic blockmodel
• Inference for high-dimensional differential correlation matrices
• Finding large average submatrices in high dimensional data
• Bootstrap methods: another look at the jackknife
• Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes
• Robust high-dimensional factor models with applications to statistical machine learning
• Network differential connectivity analysis
• Entrywise eigenvector analysis of random matrices with low expected rank
• Model free estimation of graphical model using gene expression data
• The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
• Matrix estimation by universal singular value thresholding
• Consistency of spectral clustering in stochastic block models
• A spectral algorithm for latent Dirichlet allocation
• Computational and statistical boundaries for submatrix localization in a large noisy matrix
• Sparse latent factor models with interactions: analysis of gene expression data
• Exact matrix completion via convex optimization
• Sharp variable selection of a sparse submatrix in a high-dimensional noisy matrix
• Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
• Direct estimation of differential networks
• Numerical Methods for Large Eigenvalue Problems
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Scale-Free Networks: A Decade and Beyond
• Joint estimation of multiple graphical models
• Removing technical variability in RNA-seq data using conditional quantile normalization
• An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
• Stability Selection
• BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
• Cross-Validation With Confidence
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Testing differential networks with applications to the detection of gene-gene interactions
• Detecting Differential Expressions in GeneChip Microarray Studies
• Exact and Efficient Generation of Geometric Random Variates and Random Graphs
• Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
• Differential network analysis via lasso penalized D-trace loss
• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
• Hierarchical Community Detection by Recursive Partitioning
• Linear Regression and Its Inference on Noisy Network-Linked Data

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