Uncovering the key dimensions of high-throughput biomolecular data using deep learning

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Scopus Citations
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Original languageEnglish
Pages (from-to)e56
Journal / PublicationNucleic Acids Research
Volume48
Issue number10
Online published31 Mar 2020
Publication statusPublished - 4 Jun 2020

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Abstract

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.

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Uncovering the key dimensions of high-throughput biomolecular data using deep learning. / Zhang, Shixiong; Li, Xiangtao; Lin, Qiuzhen et al.
In: Nucleic Acids Research, Vol. 48, No. 10, 04.06.2020, p. e56.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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