<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to fill in the gene expression information in scRNA-seq data is not only an important guide for gene regulatory network construction, embryonic development, and neurological research in the brain but also provides an important basis for drug development and clinical medicine. In this paper, we propose a variable combination of single-cell gap-filling algorithms with high gap-filling accuracy and fast computation speed through the comprehensive study and analysis of image repair technology and single-cell gap-filling algorithm. The experiments demonstrate that the U-net-based gap-filling method proposed in this paper has high accuracy in recovering gene expression values, can reduce the analysis errors caused by dropout events, and applies to large-scale data sets. In summary, the variable combinatorial gap-filling method for single-cell RNA-seq data proposed in this paper can effectively improve the results of downstream analysis and promote the development of research in the field of RNA sequencing data.</p>
</abstract>

With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to fill in the gene expression information in scRNA-seq data is not only an important guide for gene regulatory network construction, embryonic development, and neurological research in the brain but also provides an important basis for drug development and clinical medicine. In this paper, we propose a variable combination of single-cell gap-filling algorithms with high gap-filling accuracy and fast computation speed through the comprehensive study and analysis of image repair technology and single-cell gap-filling algorithm. The experiments demonstrate that the U-net-based gap-filling method proposed in this paper has high accuracy in recovering gene expression values, can reduce the analysis errors caused by dropout events, and applies to large-scale data sets. In summary, the variable combinatorial gap-filling method for single-cell RNA-seq data proposed in this paper can effectively improve the results of downstream analysis and promote the development of research in the field of RNA sequencing data.

Variable combinatorial gap-filling method for single-cell RNA-seq data

Information Engineering College of Changsha Medical University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Variable combinatorial gap-filling method for single-cell RNA-seq data"}

With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to...

Variable combinatorial gap-filling method for single-cell RNA-seq data

Publicado en línea: 15 jun 2023

Páginas: -

Recibido: 12 ago 2022

Aceptado: 17 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00395

Palabras clave
Single-cell RNA, Gene sequencing, Data fill-in, dropout, U-net network

© 2023 Shi YiXia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Variable combinatorial gap-filling method for single-cell RNA-seq data

Publicado en línea: 15 jun 2023

Páginas: -

Recibido: 12 ago 2022

Aceptado: 17 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00395

Palabras claveSingle-cell RNA, Gene sequencing, Data fill-in, dropout, U-net network

© 2023 Shi YiXia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Single-cell RNA, Gene sequencing, Data fill-in, dropout, U-net network