This study employed spatial transcriptomics and the BayesSpace method to investigate melanoma, enabling accurate classification of tissue cells based on their location. The BayesSpace method, combined with clustering analysis, effectively examined spatial transcriptome data. Data preprocessing and PCA were conducted to reduce dimensionality, followed by clustering using k-means, GMM, and BayesSpace. Among these methods, BayesSpace proved to be the optimal clustering method. Marker gene staining verified the clustering results, demonstrating high accuracy and enabling precise identification of cell types. This study highlights the superiority of the BayesSpace method in spatial transcriptomic analysis and its potential for annotating cell types in biomedical research. The findings contribute to melanoma diagnosis and treatment through the identification of characteristic cells, marker genes, and therapeutic targets.

Jingyao Huo. A Comparison of Spatial Transcriptomics Clustering Methods Based on Three Different Approaches. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 123-129. https://doi.org/10.25236/AJCIS.2024.070516.

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