التفاصيل البيبلوغرافية
| العنوان: |
Single-cell Mayo Map ( scMayoMap ): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis. |
| المؤلفون: |
Yang L, Ng YE, Sun H, Li Y, Chini LCS, LeBrasseur NK, Chen J, Zhang X |
| المصدر: |
BioRxiv : the preprint server for biology [bioRxiv] 2023 May 04. Date of Electronic Publication: 2023 May 04. |
| نوع المنشور: |
Preprint |
| اللغة: |
English |
| بيانات الدورية: |
Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| مستخلص: |
Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap , an easy-to-use single cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. scMayoMap performs better than the currently available annotation tools on all the datasets tested. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way. |
| التعليقات: |
Update in: BMC Biol. 2023 Oct 20;21(1):223. (PMID: 37858214) |
| تواريخ الأحداث: |
Date Created: 20230519 Latest Revision: 20231027 |
| رمز التحديث: |
20231215 |
| مُعرف محوري في PubMed: |
PMC10187171 |
| DOI: |
10.1101/2023.05.03.538463 |
| PMID: |
37205463 |
| قاعدة البيانات: |
MEDLINE |
