Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms
Despite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.
Authors
- Ahmed Mahfouz (ORCID: https://orcid.org/0000-0001-8601-2149)
- Laura H. Mariani (ORCID: https://orcid.org/0000-0002-6055-764X)
- Fadhl Alakwaa (ORCID: https://orcid.org/0000-0001-5349-7960)
- Christine P. Limonte (ORCID: https://orcid.org/0000-0002-9162-7428)
- Chirag R. Parikh (ORCID: https://orcid.org/0000-0001-9051-7385)
- Siddharth Madapoosi (ORCID: https://orcid.org/0000-0002-6858-0449)
- Abhijit S. Naik (ORCID: https://orcid.org/0000-0003-1602-9425)
- Matthias Kretzler (ORCID: https://orcid.org/0000-0003-4064-0582)
- Rajasree Menon (ORCID: https://orcid.org/0000-0001-6280-9639)
- Sylvia E. Rosas (ORCID: https://orcid.org/0000-0002-9903-4002)
- Stephen Blough
- Sean Eddy (ORCID: https://orcid.org/0000-0001-8578-3443)
- Adam Tisch
- Jan Rosa
- Philip Mccown
Institutions
- Joslin Diabetes Center (US)
- Harvard University (US)
- Johns Hopkins University (US)
- University of Washington (US)
- University of Michigan (US)
- Johns Hopkins Medicine (US)
- Delft University of Technology (NL)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-26
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e38567
- Citations
- 3
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases