SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
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Inconsistent results in SVGs identification compared to tutorial.ipynb #69
I followed the tutorial.ipynb to identify SVGs in my dataset. However, I noticed that I am getting only 2 SVG solutions instead of the expected 5 as mentioned in the tutorial. A similar issue occurs when identifying meta genes.
Here are the steps:
Preprocessed the data according to the tutorial instructions.
Ran the SVG identification code from tutorial.ipynb.
Expected to obtain 5 SVG solutions, but only received 2.
I also noticed that when I run the tutorial in ez mode, the results are consistent with the expected solutions. It seems that there might be a discrepancy in the result.h5ad file used in tutorial.ipynb compared to the one provided for download.
I have attached a screenshot of the code and the obtained results for reference. Any insights into why I am experiencing these discrepancies and suggestions for resolving the issue would be greatly appreciated.
Dear Dr. Hu,
I followed the tutorial.ipynb to identify SVGs in my dataset. However, I noticed that I am getting only 2 SVG solutions instead of the expected 5 as mentioned in the tutorial. A similar issue occurs when identifying meta genes.
Here are the steps: Preprocessed the data according to the tutorial instructions. Ran the SVG identification code from tutorial.ipynb. Expected to obtain 5 SVG solutions, but only received 2. I also noticed that when I run the tutorial in ez mode, the results are consistent with the expected solutions. It seems that there might be a discrepancy in the result.h5ad file used in tutorial.ipynb compared to the one provided for download.
I have attached a screenshot of the code and the obtained results for reference. Any insights into why I am experiencing these discrepancies and suggestions for resolving the issue would be greatly appreciated.
Screenshot:
Thank you for your help!