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Canada-0-REFLEXOLOGISTS Azienda Directories
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Azienda News:
- Spatial information matters: are traditional imputation methods . . .
Abstract Recent advancements in spatially resolved transcriptomics (SRT) have enabled near single-cell resolution, providing rich spatial context crucial for uncovering biological insights However, high-resolution SRT datasets remain sparse and prone to dropout events that may impede accurate interpretation
- The interpretable multimodal dimension reduction framework SpaHDmap . . .
Tang, Chen, Qian et al present a multimodal, interpretable dimension reduction framework called SpaHDmap, which leverages histology images and enhances the resolution of spatial transcriptomics
- Unlocking single-cell level and continuous whole-slide . . . - Nature
Here we introduce PanoSpace, a computational framework that integrates low-resolution spatial transcriptomics with high-resolution histology and matched single-cell RNA sequencing to reconstruct a
- STAHD: a scalable and accurate method to detect spatial domains in high . . .
Spatial transcriptomics (ST) enables the study of spatial heterogeneity in tissues However, current methods struggle with large-scale, high-resolution data, leading to reduced efficiency and accuracy in detecting spatial domains A scalable, precise solution is urgently needed
- Revealing high-resolution spatial metagenes from spatial transcriptomics
We present SpaHDmap, a deep learning framework that integrates histology images with spatial transcriptomic data to derive high-resolution and interpretable spatial metagenes We demonstrate that
- Graph contrastive learning of subcellular-resolution spatial . . .
Identifying and categorizing diverse cell types within a mixed population is a crucial objective in single-cell analysis This task is particularly challenging in spatial transcriptomics (iST) due to the low number of measured genes or high dropout rates Deep learning techniques [10–18] have been increasingly applied to tackle these challenges
- Spatial transcriptomics maps host–gut microbiome biogeography at high . . .
Here we report a method for broad spatial sampling of microbiome–host interactions in the gut at high resolution (1 µm)
- Integration tools for scRNA-seq data and spatial transcriptomics . . .
Abstract Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries
- Systematic benchmarking of high-throughput subcellular spatial . . .
Spatial transcriptomics has undergone remarkable advancements, with commercial platforms now achieving subcellular resolution and high-throughput gene detection
- SD2: spatially resolved transcriptomics deconvolution through . . .
Unveiling the heterogeneity in the tissues is crucial to explore cell–cell interactions and cellular targets of human diseases Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis Therefore, deconvolution of ST
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