Fig 1.
(A) MVST uses SCANPY to perform routine data preprocessing operations such as quality control and highly variable gene screening on spatial transcriptome gene expression data. PCA is used to downscale highly variable gene expression data to obtain a relatively low-dimensional feature representation of spot gene expression data. (B) The graph construction process of MVST reflects its multi-perspective feature, constructing graphs based on distance similarity, histopathological image similarity, and gene expression similarity. The spatial adjacency, histological image similarity, and gene expression similarity networks are obtained through this process. (C) The multi-view graph convolution model of MVST includes a multi-view graph convolution encoder with an attention mechanism and a coherent embedding encoder, in which the multi-view graph convolution encoder learns the spot features from each of the three views, and the coherent embedding encoder integrates the features of the spot in each view to obtain the final clustered embedding.
Fig 2.
MVST improves spatial domain recognition of human breast cancer tissues.
(A) Manual annotation of the spatial domain of the human breast cancer dataset by dividing the tissue slices into 20 regions. (B) Histogram of the performance of spatial domain recognition of human breast cancer dataset using MVST and existing state-of-the-art algorithms (ConGI, STMGCN, SpaGCN, STAGATE, SEDR, and BayesSpace), with the X-axis showing the names of the algorithms and the Y-axis showing the ARI value of the spatial domain recognition results of each algorithm. This is used to compare the results of the predicted spatial domains of each algorithm to the similarity of manually annotated regions. (C) ARI values and visualizations of spatial domain recognition results from MVST and other algorithms for the human breast cancer dataset.
Fig 3.
MVST enhances spatial domain recognition of human dorsolateral prefrontal cortex tissue slice 151509.
(A) Manual annotation of the spatial domain of human dorsolateral prefrontal cortex tissue slice 151509 dataset by dividing the tissue slice into seven layers. (B) Histogram of the performance of spatial domain recognition of human dorsolateral prefrontal cortex tissue slices 151509 dataset using MVST and existing state-of-the-art algorithms (ConGI, STMGCN, SpaGCN, STAGATE, SEDR, and BayesSpace), with the X-axis showing the names of the algorithms, and the Y-axis showing the ARI of the spatial domain recognition results value of each algorithm, which is used to compare the similarity between the spatial domains predicted by each algorithm and the manually annotated layers. (C) ARI values and visual presentation of the spatial domain recognition results of MVST and other algorithms for the human dorsolateral prefrontal cortex tissue slice 151509 dataset.
Fig 4.
MVST enhances spatial domain recognition of human dorsolateral prefrontal cortex tissue slice 151510.
(A) Manual annotation of the spatial domain of human dorsolateral prefrontal cortex tissue slice 151510 dataset by dividing the tissue slice into seven layers. (B) Histogram of the performance of spatial domain recognition of the human dorsolateral prefrontal cortex tissue slices 151510 dataset using MVST and existing state-of-the-art algorithms (ConGI, STMGCN, SpaGCN, STAGATE, SEDR, and BayesSpace), with the X-axis showing the name of each algorithm, and the Y-axis showing the ARI of the spatial domain recognition results of each algorithm value, which is used to compare the similarity between the spatial domains predicted by each algorithm and the manually annotated layers. (C) ARI values and visual presentation of spatial domain recognition results from MVST and other algorithms for the human dorsolateral prefrontal cortex tissue slice 151510 dataset.
Fig 5.
MVST improves the spatial domain recognition of mouse anterior brain tissue slices.
(A) Manual annotation of the spatial domains of the mouse anterior brain tissue slice dataset by dividing the tissue slices into 52 regions. (B) Histogram of the performance of spatial domain recognition of the mouse anterior brain tissue slices dataset using MVST and existing state-of-the-art algorithms (ConGI, STMGCN, SpaGCN, STAGATE, SEDR, and BayesSpace), with the X-axis showing the names of the algorithms, and the Y-axis showing the ARI value of the spatial domain recognition results of each algorithm, which is used to compare the similarity between the spatial domains predicted by each algorithm and the manually annotated layers. (C) ARI values and visual presentation of spatial domain recognition results of MVST and other algorithms on the mouse anterior brain tissue slice dataset.
Fig 6.
Performance comparison between MVST and six comparison methods on simulated data.
(A) Performance comparison between MVST and six comparison methods when facing different sparsities of gene expression matrix; the X-axis represents different sparsities of gene expression matrix and the Y-axis represents ARI values. (B) Performance comparison between MVST and the six comparative methods when facing simulated data with different levels of added noise. The X-axis represents the level of added noise and the Y-axis represents the ARI value.
Fig 7.
Comparison of the performance of MVST with its two variants (MVST_view1&3 and MVST_view2&3) on the human dorsolateral prefrontal cortex, mouse anterior cerebral slice, and human breast cancer datasets, with the X-axis representing the different datasets and the Y-axis representing the ARI values.