Cargando…
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches...
Autores principales: | Zeng, Zexian, Li, Yawei, Li, Yiming, Luo, Yuan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951701/ https://www.ncbi.nlm.nih.gov/pubmed/35337374 http://dx.doi.org/10.1186/s13059-022-02653-7 |
Ejemplares similares
-
Statistical and machine learning methods for spatially resolved transcriptomics with histology
por: Hu, Jian, et al.
Publicado: (2021) -
Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics
por: Qiu, Zhihua, et al.
Publicado: (2022) -
Benchmarking cell-type clustering methods for spatially resolved transcriptomics data
por: Cheng, Andrew, et al.
Publicado: (2022) -
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
por: Shen, Rongbo, et al.
Publicado: (2022) -
Computational challenges and opportunities in spatially resolved transcriptomic data analysis
por: Atta, Lyla, et al.
Publicado: (2021)