Batch effect adjustment based on negative binomial regression for RNA sequencing count data

An R package to test for batch effects in high-dimensional single-cell RNA sequencing data.

An implementation of MNN (Mutual Nearest Neighbors) correct in python.

RADseq Data Exploration, Manipulation and Visualization using R

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

Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification

Unbiased integration of single cell transcriptomes.

Imputation method for scRNA-seq based on low-rank approximation

Visualization and analysis of single-cell RNA-seq data by alternative clustering

NewWave

Correction of batch effects in DNA methylation data

Detecting hidden batch factors through data adaptive adjustment for biological effects

This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI

Code accompanying batch effects processing workflow for "omic" data, mainly targeted for proteomics

RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data

batchtma: R package to adjust for batch effects, for example between tissue microarrays