  Spatial transcriptomic (ST) clustering employs spatial and transcription
information to group spots spatially coherent and transcriptionally similar
together into the same spatial domain. Graph convolution network (GCN) and
graph attention network (GAT), fed with spatial coordinates derived adjacency
and transcription profile derived feature matrix are often used to solve the
problem. Our proposed method STGIC (spatial transcriptomic clustering with
graph and image convolution) utilizes an adaptive graph convolution (AGC) to
get high quality pseudo-labels and then resorts to dilated convolution
framework (DCF) for virtual image converted from gene expression information
and spatial coordinates of spots. The dilation rates and kernel sizes are set
appropriately and updating of weight values in the kernels is made to be
subject to the spatial distance from the position of corresponding elements to
kernel centers so that feature extraction of each spot is better guided by
spatial distance to neighbor spots. Self-supervision realized by KL-divergence,
spatial continuity loss and cross entropy calculated among spots with high
confidence pseudo-labels make up the training objective of DCF. STGIC attains
state-of-the-art (SOTA) clustering performance on the benchmark dataset of
human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of
depicting fine structures of other tissues from other species as well as
guiding the identification of marker genes. Also, STGIC is expandable to
Stereo-seq data with high spatial resolution.
