singlecell-batches Goto Github PK
Type: Organization
Type: Organization
boilerplate for reproducible and transparent science
One smart cookie of an algorithm. Infers both batch effects and clusters at the same time.
The algorithm identifies and removes cell cycle effects from single cell mRNAseq data
removes batch effects, good for small sample sizes, mean-centric
Cookiecutter template for a Python package. See https://github.com/audreyr/cookiecutter.
Repository organizing the data behind the singlecell batch effect correction comparison project
Template for adding an algorithm for analysis with single cell batch effects
Paper for comparison of batch effect correction algorithms in single-cell RNA-seq
"We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches."
How to get started with the single cell batches comparison
removeBatchEffect function (remove batch effect from expression data)
Landing page for manuscript comparing single-cell batch effect correction algorithms
Algorithm to correct batch effects for batches with different cell population
method is based on partial least squares and models control and target genes jointly to better infer and control for confounding effects
How to use Singularity containers for running single cell batch effect correction algorithms
Algorithm for removing batch effects in single-cell RNA-seq that leaves only the batches that are having the most fun
Varying-Censoring Aware Matrix Factorization for Single Cell RNA-Sequencing
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