Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)

scBatch: Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment

(2020) Teng Fei et al.   BIOINFORMATICS

Longitudinal measures of RNA expression and disease activity in FSHD muscle biopsies

(2020) Chao-Jen Wong et al.   HUMAN MOLECULAR GENETICS

Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies

(2020) Tristan Zindler et al.   BMC BIOINFORMATICS

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

(2020) Joanne C. Beer et al.   NEUROIMAGE

Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects?

(2019) Longjian Zhou et al.   Journal of Genetics and Genomics

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

(2018) Yuqing Zhang et al.   BMC BIOINFORMATICS

Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission

(2018) Shinya Tasaki et al.   Nature Communications

Adjusting for Batch Effects in DNA Methylation Microarray Data, a Lesson Learned

(2018) E. M. Price et al.   Frontiers in Genetics

Why Batch Effects Matter in Omics Data, and How to Avoid Them

(2017) Wilson Wen Bin Goh et al.   TRENDS IN BIOTECHNOLOGY

Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients

(2016) Romain Banchereau et al.   CELL

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

(2016) Christian Müller et al.   PLoS One

Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses

(2015) Vegard Nygaard et al.   BIOSTATISTICS

Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths

(2015) Vijay K. Venkatraman et al.   NEUROIMAGE

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

(2015) Boris P. Hejblum et al.   PLoS Computational Biology

Batch effect correction for genome-wide methylation data with Illumina Infinium platform

(2011) Zhifu Sun et al.   BMC Medical Genomics

Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

(2011) Chao Chen et al.   PLoS One

Overcoming bias and systematic errors in next generation sequencing data

(2011) Margaret A Taub et al.   Genome Medicine

Tackling the widespread and critical impact of batch effects in high-throughput data

(2010) Jeffrey T. Leek et al.   NATURE REVIEWS GENETICS

A longitudinal study of gene expression in healthy individuals

(2009) Chris Karlovich et al.   BMC Medical Genomics

Intra-individual Change Over Time in DNA Methylation With Familial Clustering

(2008) Hans T. Bjornsson   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION