ArticleStatistics in medicine2014
Covariance adjustment for batch effect in gene expression data.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients.Briefings in bioinformatics · 2016Pooled it
- A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.Bioinformatics advances · 2026Article
- Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.medRxiv : the preprint server for health sciences · 2024Article
- Topological analysis of interaction patterns in cancer-specific gene regulatory network: persistent homology approach.Scientific reports · 2021Article
- Article
- Stability of Imbalanced Triangles in Gene Regulatory Networks of Cancerous and Normal Cells.Frontiers in physiology · 2020Article
- A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies.Scientific reports · 2017Article
- Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment.BMC bioinformatics · 2016Article
- Statistical Issues in the Design and Analysis of nCounter Projects.Cancer informatics · 2014Review
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Authors and funding
3 authors.
Funding
Abstract
Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.