Workflow from Scientific Research
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Given multi-slice omics profiles ({{{\bf{X}}}}) and spatial location ({{{\bf{S}}}}) data as input, stClinic learns batch-corrected latent features ({{{\bf{z}}}}) using a dynamically evolving graph, guided by a Mixture-of-Gaussians prior through Kullback-Leibler (KL) divergence regularization.
#Workflow#Illustration#Heatmap#Network#Pathway#Density Plot#Multi-Slice Omics#Spatial Location#Latent Features#Graph Learning#Batch Correction
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