Document Type
Article
Abstract
In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality and complexity bring challenges to the data analysis and require extensive collaborations between biologists and bioinformaticians and/or biostatisticians. The communication between these two units demands a platform for easy data sharing and exploration. Here we developed Single-Cell Transcriptomics Annotated Viewer (SCANNER), as a public web resource for the scientific community, for sharing and analyzing scRNA-seq data in a collaborative manner. It is easy-to-use without requiring special software or extensive coding skills. Moreover, it equipped a real-time database for secure data management and enables an efficient investigation of the activation of gene sets on a single-cell basis. Currently, SCANNER hosts a database of 19 types of cancers and COVID-19, as well as healthy samples from lungs of smokers and non-smokers, human brain cells and peripheral blood mononuclear cells (PBMC). The database will be frequently updated with datasets from new studies. Using SCANNER, we identified a larger proportion of cancer-associated fibroblasts cells and more active fibroblast growth-related genes in melanoma tissues in female patients compared to male patients. Moreover, we found ACE2 is mainly expressed in lung pneumocytes, secretory cells and ciliated cells and differentially expressed in lungs of smokers and never smokers.
Digital Object Identifier (DOI)
Publication Info
Published in Database-the Journal of Biological Databases and Curation, Volume 2022, 2022, pages baab086-.
Rights
© The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ((https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
APA Citation
Cai, G., Yu, X., Youn, C., Zhou, J., & Xiao, F. (2022). SCANNER: a web platform for annotation, visualization and sharing of single cell RNA-seq data. Database, 2022. https://doi.org/10.1093/database/baab086