Integration tools for scRNA-seq data and spatial transcriptomics sequencing data.

Yan, Chaorui; Zhu, Yanxu; Chen, Miao; Yang, Kainan; Cui, Feifei; Zou, Quan; Zhang, Zilong

Yan, Chaorui; Zhu, Yanxu; Chen, Miao; Yang, Kainan; Cui, Feifei; Zou, Quan; Zhang, Zilong.

Afiliación

Yan C; School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
Zhu Y; School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
Chen M; School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
Yang K; School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
Cui F; School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
Zou Q; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Zhang Z; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.

Brief Funct Genomics ; 23(4): 295-302, 2024 Jul 19.

Article en En | MEDLINE | ID: mdl-38267084

ABSTRACT

ABSTRACT

Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.

Asunto(s)

Análisis de la Célula Individual; Transcriptoma; Análisis de la Célula Individual/métodos; Humanos; Transcriptoma/genética; Perfilación de la Expresión Génica/métodos; Análisis de Secuencia de ARN/métodos; RNA-Seq/métodos; Programas Informáticos; Animales; Análisis de Expresión Génica de una Sola Célula

Palabras clave

HVGs; integration; scRNA-seq data; spatial transcriptomics sequencing data

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de la Célula Individual / Transcriptoma Límite: Animals / Humans Idioma: En Revista: Brief Funct Genomics Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google