Single-cell RNA-sequencing(scRNA-seq)is a rapidly increasing research area in biomed-ical signal processing.However,the high complexity of single-cell data makes efficient and accurate analysis difficult.To improve th...Single-cell RNA-sequencing(scRNA-seq)is a rapidly increasing research area in biomed-ical 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 hier-archical 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.展开更多
文摘Single-cell RNA-sequencing(scRNA-seq)is a rapidly increasing research area in biomed-ical 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 hier-archical 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.
