(A) Comparison of the results of identifying the laminar structure of the olfactory bulb using SpaMWGDA and seven state-of-the-art methods (Scanpy, stlearn, SpaGCN, SEDR, STAGATE, GraphST, and Spatial-MGCN).

(A) Comparison of the results of identifying the laminar structure of the olfactory bulb using SpaMWGDA and seven state-of-the-art methods (Scanpy, stlearn, SpaGCN, SEDR, STAGATE, GraphST, and Spatial-MGCN).

<p><b>(B)</b> Analysis results of differentially expressed genes (DEGs) between the coronal tissue layers of the mouse olfactory bulb identified by SpaMWGDA.</p>

Tallennettuna:
Bibliografiset tiedot
Päätekijä: Lin Yuan (46306) (author)
Muut tekijät: Boyuan Meng (22607954) (author), Qingxiang Wang (2637916) (author), Chunyu Hu (12464817) (author), Cuihong Wang (429362) (author), De-Shuang Huang (396909) (author)
Julkaistu: 2025
Aiheet:
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
level attention mechanism
fixed similarity metric
analyse tissue structure
spatial domain identification
identifying spatial domains
achieved impressive results
div >< p
combining data augmentation
view gcn encoder
data augmentation
spatial transcriptomics
spatial information
experimental results
weighted fusion
trajectory inference
spot features
source code
rapid development
large number
key features
introduce noise
gene features
deep learning
contrastive learning
cannot efficiently
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