(A) The performance comparison of spatial domain identification of SpaMWGDA and seven state-of-the-art methods (Scanpy, stlearn, SpaGCN, SEDR, STAGATE, GraphST, and Spatial-MGCN) on human breast cancer dataset.

(A) The performance comparison of spatial domain identification of SpaMWGDA and seven state-of-the-art methods (Scanpy, stlearn, SpaGCN, SEDR, STAGATE, GraphST, and Spatial-MGCN) on human breast cancer dataset.

<p><b>(B)</b> Results of differentially expressed genes analysis among different clusters identified by SpaMWGDA. <b>(C)</b> Results of differentially expressed genes analysis between cluster 3 (IDC) and cluster 13 (DCIS/LCIS).</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
also show
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