Automated Author ProfileYu, Nanze
Chinese Academy of Medical Sciences & Peking Union Medical College
Yu, Nanze
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: 10.0 (sum of 14 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Additional file 1: Table S1. Clinical information of the subjects subjected to scRNA-seq and sequencing quality metrics of the scRNA-seq data.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 2: Table S2. Molecular signature for each cell type or cluster.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 2: Table S2. Molecular signature for each cell type or cluster.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 3: Table S3. Differentially expressed genes detected for each major cell type between the SVF from nonlesional sites of patients with LoS and healthy donors.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 4: Table S4. Dysregulated pathways in ASCs from nonlesional sites of patients with LoS detected by gene set enrichment analysis.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 5: Table S5. Gene-coexpression modules identified in ASCs through hdWGCNA.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 6: Table S6. Differentially expressed genes between CD55high and CD55low ASCs.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 6: Table S6. Differentially expressed genes between CD55high and CD55low ASCs.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 7: Table S7. Intercellular communications in the SVF from patients with LoS and healthy donors inferred by Cellchat.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao
Additional file 4: Table S4. Dysregulated pathways in ASCs from nonlesional sites of patients with LoS detected by gene set enrichment analysis.
Authors
- Liu, Xuanyu ;
- Li, Zhujun ;
- Wang, Hayson Chenyu ;
- Yuan, Meng ;
- Chen, Jie ;
- Huang, Jiuzuo ;
- Yu, Nanze ;
- Zhou, Zhou ;
- Long, Xiao