Automated Author ProfileZhu, Bo
University of Texas MD Anderson Cancer Center0000-0003-2439-0026
Zhu, Bo
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains single-cell RNA sequencing (scRNA-seq) and 10x Genomics Visium spatial transcriptomics data analyzed in our manuscript submitted to Nature Cell Biology. The scRNA-seq data includes high-quality transcriptomic profiles of cells isolated from mouse lung tissue across different stages of carcinogenesis. The Visium spatial transcriptomics data captures spatial gene expression patterns from matched tissue sections, enabling integrative spatial and single-cell analysis of the tumor microenvironment.The dataset supports analyses presented in the manuscript, including spatial domain detection, cell-type mapping, spatial niche detection, and integration with scRNA-seq profiles.
Authors
- Aminu, Muhammad ;
- Zhu, Bo ;
- Wu, Jia ;
- Zhang, Jianjun
This dataset contains single-cell RNA sequencing (scRNA-seq) and 10x Genomics Visium spatial transcriptomics data analyzed in our manuscript submitted to Nature Cell Biology. The scRNA-seq data includes high-quality transcriptomic profiles of cells isolated from mouse lung tissue across different stages of carcinogenesis. The Visium spatial transcriptomics data captures spatial gene expression patterns from matched tissue sections, enabling integrative spatial and single-cell analysis of the tumor microenvironment.The dataset supports analyses presented in the manuscript, including spatial domain detection, cell-type mapping, spatial niche detection, and integration with scRNA-seq profiles.
Authors
- Aminu, Muhammad ;
- Zhu, Bo ;
- Wu, Jia ;
- Zhang, Jianjun