Supporting data for "Stardust: improving spatial transcriptomics data analysis through space aware modularity optimization based clustering."

Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. <br>We propose a new clustering method, <i>Stardust</i>, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. We evaluated <i>Stardust</i> results by analyzing ST datasets available on the 10X Genomics website. and comparing clustering performances with state-of-the-art approaches by measuring the spots stability in the clusters. Stability is defined by the tendency of each point to remain clustered with the same neighbours when perturbations are applied. <br><i>Stardust</i> is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.