Batch Effects Correction with Unknown Subtypes

DOI10.1080/01621459.2018.1497494zbMath1420.62458OpenAlexW2883806880MaRDI QIDQ5231488

Yingying Wei, XiangYu Luo

Publication date: 27 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2018.1497494

zbMATH Keywords

model-based clusteringdisease subtype discoveryintegrative analysishigh-throughput experimentsinterlaboratory comparisons

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data ⋮ Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding ⋮ BGX ⋮ BUScorrect

Uses Software

• Bioconductor
• BayesDA
• svaseq
• sparcl
• fRMA

Cites Work

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• Adjusting batch effects in microarray expression data using empirical Bayes methods
• Estimating the dimension of a model
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Finite mixtures of multivariate Poisson distributions with application
• Frozen robust multiarray analysis (fRMA)
• Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data
• Clustering in the Presence of Scatter
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• A Framework for Feature Selection in Clustering
• Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology
• Bayesian Inference of Multiple Gaussian Graphical Models
• On the Identifiability of Finite Mixtures
• BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data
• Detecting differential gene expression with a semiparametric hierarchical mixture method
• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data

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