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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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The covariance environment defines cellular niches for spatial inference
Doron Haviv1,2, Mohamed Gatie3, Anna-Katerina Hadjantonakis3
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Biorxiv : the Preprint Server for Biology
|May 3, 2023
Summary
New computational methods like COVET and ENVI represent cellular niches using gene covariation. ENVI integrates spatial and single-cell RNA-seq data, improving gene expression imputation and spatial context inference.
Area of Science:
- Computational biology
- Genomics
- Bioinformatics
Background:
- Multiplexed spatial profiling technologies generate complex biological data.
- Extracting biological insights requires robust computational methods for cellular niche representation.
Conclusions:
- COVET provides a powerful new representation for cellular niches in spatial genomics.
- ENVI advances the integration of spatial and single-cell genomics data, enabling deeper biological discovery.
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