A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data.

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Tools found in the code: anndata, Biopython, clusterProfiler, DESeq2, ggplot2, h5py, igraph, Keras, PyTorch Lightning, Matplotlib, mgcv, Numba, NumPy, pandas, patchwork, PyTorch Geometric, Pyro, PyTorch, reticulate, Scanpy, scikit-learn, SciPy, seaborn, Seurat, SHAP, SingleCellExperiment, statsmodels, TensorFlow, tidyverse, UMAP, xarray

OverviewAuthors: Kyung Dae Ko, Vittorio Sartorelli
Journal: iScience
Published: 30 January 2024
Type: Research article
Status: code verified (how the code is found and verified)
DOI: 10.1016/j.isci.2024.109027
License: CC BY
Institutions: National Institutes of Health (United States), National Institute of Arthritis and Musculoskeletal and Skin Diseases (United States)
Categories: genetics / omics (modality)

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geo:GSE84133

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