Reusability report: Exploring the transferability of self-supervised learning models from single-cell to spatial transcriptomics
- 21 August 2025
- journal article
- research article
- Published by Springer Nature in Nature Machine Intelligence
- Vol. 7 (9) , 1414-1428
Abstract
No abstract availableKeywords
This publication has 50 references indexed in Scilit:
- Integrative spatial analysis reveals a multi-layered organization of glioblastomaCell, 2024
- Mapping the transcriptome: Realizing the full potential of spatial data analysisCell, 2023
- Spatial transcriptomics: Technologies, applications and experimental considerationsGenomics, 2023
- High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathwaysiScience, 2022
- Computational challenges and opportunities in spatially resolved transcriptomic data analysisNature Communications, 2021
- Comprehensive Integration of Single-Cell DataCell, 2019
- Dimensionality reduction for visualizing single-cell data using UMAPNature Biotechnology, 2018
- In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse HippocampusNeuron, 2016
- Computational and analytical challenges in single-cell transcriptomicsNature Reviews Genetics, 2015
- mRNA-Seq whole-transcriptome analysis of a single cellNature Methods, 2009