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
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
Detecting hidden batch factors through data adaptive adjustment for biological effects
Correction of batch effects in DNA methylation data
This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI
RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data
Code accompanying batch effects processing workflow for "omic" data, mainly targeted for proteomics
R script and Shiny app to perform stratified randomisation
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