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 implementation of MNN (Mutual Nearest Neighbors) correct in python.
RADseq Data Exploration, Manipulation and Visualization using R
An R package to test for batch effects in high-dimensional single-cell RNA sequencing data.
Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment
BEER: Batch EffEct Remover for single-cell data
Tools for Batch Effects Diagnostics and Correction
Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification
Detecting hidden batch factors through data adaptive adjustment for biological effects
Mitigating the adverse impact of batch effects in sample pattern detection
Code accompanying batch effects processing workflow for "omic" data, mainly targeted for proteomics
This repository contains iPython notebooks that run on the octave kernel to accompany tutorial and slides presented at PRNI
Uses regression and factor model approach to correct for site effects in fMRI volumes
batchtma: R package to adjust for batch effects, for example between tissue microarrays
Analyzing batch effects in single cell RNA sequencing (scRNA-seq) analysis and predicting their impact on downstream analysis.
Visualization and analysis of single-cell RNA-seq data by alternative clustering
Unbiased integration of single cell transcriptomes.
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