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

R package expanding integrative analysis capabilities of Seurat by providing seamless access to popular integration methods and to an integration benchmarking toolkit.

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.

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

Correction of batch effects in DNA methylation data

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

NewWave

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

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

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