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

Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data

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

Mitigating the adverse impact of batch effects in sample pattern detection

Unbiased integration of single cell transcriptomes.

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

NewWave

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

Correction of batch effects in DNA methylation data

ALPINE is a semi-supervised non-negative matrix factorization (NMF) framework designed to effectively distinguish between multiple phenotypic conditions based on shared biological factors, while also providing direct interpretability of condition-associated genes. The preprint is available on bioRxiv.

Detecting hidden batch factors through data adaptive adjustment for biological effects

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

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

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