Annotating cells in single-cell gene expression data is a challenging task due to the continuous nature of gene expression levels and the complex relationship between gene expression and cellular function. To address this challenge, researchers can utilize cell marker databases and web resources to identify cell types in various tissues.
The databases contain marker genes for various cell types and offer user-friendly web interfaces for browsing, searching, visualizing, and downloading cell type and gene marker associations. For example, CellMarker 2.0 is a valuable database containing experimentally supported biomarkers of various cell types in human and mouse tissues. It includes information about 656 tissues, 2,578 cell types, and 26,915 cell markers. The database offers six web tools such as cell annotation, cell clustering, cell malignancy, and cell differentiation, for comprehensive analysis and visualization of single-cell data, making it beneficial for various search applications, including disease identification and prognostic biomarkers.
Additionally, other databases such as PanglaoDB and SingleCellBase offer access to curated datasets and lists of cell-type markers for various tissues. These resources provide tools for manual cell annotation and the exploration of marker genes for specific cell types, making them accessible for researchers without advanced scripting or programming skills. For instance, SingleCellBase offers single-cell annotation across multiple species through user-friendly web interfaces for browsing and visualizing data. Additionally, there are web servers such as the Annotation of Cell Types (ACT) that provide convenient tools for annotating cell types using single-cell expression data and cell type annotation information from the Human Cell Atlas.
In conclusion, though there may be some limitations such as completeness and consistency of marker information, the use of cell marker databases, web servers, and advanced cell annotation tools plays a crucial role in the identification and annotation of cell types in single-cell gene expression data. These resources not only facilitate the identification of cell types in different tissues but also support various research applications. As the field continues to evolve, the development of advanced tools and models is further enhancing the accuracy and efficiency of cell type annotation in single-cell sequencing data.
If you have questions about these resources or other bioinformatics analysis tools, contact the HSLS Molecular Biology Information Service.
~Sri Chaparala