Journal Article

DANCE: a deep learning library and benchmark platform for single-cell analysis

Tang, Jiliang, Su, Runze, Lu, Qiaolin, Wang, Yixin, Tang, Wenzhuo, Venegas, Julian, Chang, Yi, Xie, Yuying, Jin, Wei, Ding, Jiayuan, Molho, Dylan, Liu, Renming, Wen, Hongzhi, Li, Lingxiao, Li, Zhaoheng, Zuo, Wangyang

2024

Overview

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Algorithms / Animal Genetics and Genomics / Benchmarking / Bioinformatics / Biomedical and Life Sciences / Cell type annotation / computer software / Dance / data collection / Data processing / Datasets / Deep Learning / ecosystems / Evolutionary Biology / Gene expression / Gene imputation / Gene Library / genome / Graphs / Human Genetics / Humans / Infrastructure / Interfaces / Life Sciences / Microbial Genetics and Genomics / Neural networks / people / Plant Genetics and Genomics / Programming languages / Reproducibility / Single-Cell Analysis / Single-cell multimodal analysis / Single-cell spatial analysis / Software / Stem cells