Open AccessUncovering the key dimensions of high-throughput biomolecular data using deep learning
Author(s) -
Shixiong Zhang,
Xiangtao Li,
Qiuzhen Lin,
Jiecong Lin,
KaChun Wong
Publication year - 2020
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkaa191
Subject(s) - profiling (computer programming) , biology , transcriptome , computational biology , encode , rna seq , gene ontology , benchmark (surveying) , gene expression profiling , key (lock) , computer science , artificial intelligence , gene , gene expression , genetics , ecology , geodesy , geography , operating system
Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep learning framework based on auto-encoder, termed DeepAE, is proposed to elucidate high-dimensional transcriptomic profiling data in an encode–decode manner. Comparative experiments were conducted on nine transcriptomic profiling datasets to compare DeepAE with four benchmark methods. The results demonstrate that the proposed DeepAE outperforms the benchmark methods with robust performance on uncovering the key dimensions of single-cell RNA-seq data. In addition, we also investigate the performance of DeepAE in other contexts and platforms such as mass cytometry and metabolic profiling in a comprehensive manner. Gene ontology enrichment and pathology analysis are conducted to reveal the mechanisms behind the robust performance of DeepAE by uncovering its key dimensions.