A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis
Journal of Beijing Institute of Technology (English Edition)
; 31(3):285-292, 2022.
Article
in English
| Scopus | ID: covidwho-1924761
ABSTRACT
Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks. © 2021 Journal of Beijing Institute of Technology
Biomedical signal processing; COVID-19; Feature extraction; ScRNA-seq; Bioinformatics; Cytology; Data handling; Extraction; Genes; RNA; Biomedical signals processing; Cell data; Feature extraction methods; Features extraction; High complexity; ITS applications; Processing applications; Research areas; Single cells
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Journal of Beijing Institute of Technology (English Edition)
Year:
2022
Document Type:
Article
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