Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectivel...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Yang, Sheng, Qiongyu, Qi, Jing, Hua, Jiao, Yang, Bo, Wan, Lei, Jin, Shuilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2023
Materias:
Methods
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317120/
https://www.ncbi.nlm.nih.gov/pubmed/37308294
http://dx.doi.org/10.1101/gr.277522.122

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317120/
https://www.ncbi.nlm.nih.gov/pubmed/37308294
http://dx.doi.org/10.1101/gr.277522.122

Ejemplares similares