Batch effect adjustment based on negative binomial regression for RNA sequencing count data
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Updated
Sep 24, 2020 - R
Batch effect adjustment based on negative binomial regression for RNA sequencing count data
BEER: Batch EffEct Remover for single-cell data
Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment
Tools for Batch Effects Diagnostics and Correction
Mitigating the adverse impact of batch effects in sample pattern detection
Unbiased integration of single cell transcriptomes.
Detecting hidden batch factors through data adaptive adjustment for biological effects
Correction of batch effects in DNA methylation data
RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data
Code accompanying batch effects processing workflow for "omic" data, mainly targeted for proteomics
batchtma: R package to adjust for batch effects, for example between tissue microarrays
R script and Shiny app to perform stratified randomisation
Correction of batch effects with BEclear as a command line tool
Package for analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
Analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
Batch effect adjustment based on negative binomial regression for RNA sequencing count data. Credits: https://github.com/zhangyuqing/ComBat-seq
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