I appreciate your efforts in developing such an effective tool for spatial transcriptomics analysis! I have been exploring the capabilities of stLearn with Xenium data and have encountered a specific issue that I hope you can help me address.
Regarding the st.tl.cci.run_cci function in stLearn, I was able to successfully process this function for two out of four datasets. However, despite using a high-specification computer, the computation did not progress beyond 0% even after 24 hours for the remaining two datasets, which are larger in size. I am considering the introduction of a supercomputer, although the application process might be time-consuming. As an alternative, I am contemplating using Seurat to divide the data, such as with BuildNicheAssay(object = xenium.obj, fov = "crop", group.by = "predicted.celltype", niches.k = 5, neighbors.k = 30), and then running stLearn on the segmented data. Would this approach compromise the accuracy of the stLearn analysis?
Dear stLearn Team,
I appreciate your efforts in developing such an effective tool for spatial transcriptomics analysis! I have been exploring the capabilities of stLearn with Xenium data and have encountered a specific issue that I hope you can help me address.
Regarding the st.tl.cci.run_cci function in stLearn, I was able to successfully process this function for two out of four datasets. However, despite using a high-specification computer, the computation did not progress beyond 0% even after 24 hours for the remaining two datasets, which are larger in size. I am considering the introduction of a supercomputer, although the application process might be time-consuming. As an alternative, I am contemplating using Seurat to divide the data, such as with BuildNicheAssay(object = xenium.obj, fov = "crop", group.by = "predicted.celltype", niches.k = 5, neighbors.k = 30), and then running stLearn on the segmented data. Would this approach compromise the accuracy of the stLearn analysis?