Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies

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Figure 4.
Figure 4.

Proust achieves distinct spatial domain detection with different protein channels and weights assigned to transcriptomics and proteomics on Visium SPG human inferior temporal cortex tissue slices from donor Br3880 and Br3854. (A) Immunofluorescence staining images of Aβ and pTau. (B) Proust clustering result using five protein channels (DAPI, Aβ, pTau, MAP2, and GFAP), top 30 PCs from reconstructed gene expression, top five PCs from reconstructed extracted image features, and k = 7 clusters. (C) Stacked violin plot of the distribution of marker genes (MOBP for oligodendrocytes/WM, SNAP25 for neurons/gray matter) in each spatial domain assigned by Proust. (D) Proust clustering result using two protein channels (Aβ and pTau). The first two columns show clustering results using transcriptomics only when k = 2 and k = 4 clusters, respectively. The last two columns show clustering results using a hybrid profile of transcriptomics and proteomics, with the top 10 PCs from reconstructed gene expression and the top 10 PCs from reconstructed extracted image features when k = 4 and k = 7 clusters, respectively.

This Article

  1. Genome Res. 35: 1621-1632

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