HelloWorldLTY/AWGAN
Codes for paper: AWGAN: A Powerful Batch Effect Removal Model for scRNA-seq Data
AWGAN helps single-cell RNA sequencing researchers by correcting 'batch effects' in their experimental data. It takes your raw scRNA-seq datasets, along with information about which batch each sample came from, and produces a corrected data matrix that removes technical variations between batches. This makes your gene expression analysis more accurate and reliable.
No commits in the last 6 months. Available on PyPI.
Use this if you are a genomics researcher working with scRNA-seq data from multiple experiments or batches and need to remove technical variability to compare them fairly.
Not ideal if you are working with bulk RNA-seq data or other types of genomic data, as this tool is specifically designed for single-cell RNA sequencing.
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Jupyter Notebook
License
MIT
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Last pushed
Feb 09, 2023
Commits (30d)
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