I am currently conducting research on a HCC (Hepatocellular Carcinoma) cohort using Stereo-seq (BGI/MGI) technology. Given CalicoST's superior performance in inferring allele-specific CNAs and reconstructing tumor phylogeography, I am highly interested in applying it to my dataset.
Questions:
Compatibility: Has CalicoST been tested on Stereo-seq data? Since Stereo-seq provides sub-cellular resolution, are there any specific considerations compared to the 10x Visium or Slide-tags data mentioned in the paper?
Preprocessing Pipeline: My data is processed via the SAW (Stereo-seq Analysis Workflow), which generates standard BAM files. I plan to use cellsnp-lite to extract germline heterozygous SNP counts. Does the CalicoST team have any specific recommendations for handling the unique coordinate system or the higher density of spots in Stereo-seq?
Binning Strategy: Stereo-seq is often analyzed at different "Bin" levels (e.g., Bin 20, Bin 50). To ensure sufficient SNP coverage for informative BAF values , is there a recommended bin size or a minimum number of SNP-covering UMIs per spot/bin that you would suggest for optimal HMRF performance? HMRF and Spatial Coherence: Given the high resolution and potential cell-mixing in HCC tissues, are there specific tips for tuning the spatial coherence parameters in the HMRF model to avoid over-smoothing while maintaining biological relevance? I believe CalicoST could provide invaluable insights into the spatial evolution of HCC clones in our cohort. Thank you for developing this powerful tool and for your time in addressing these questions!
Best regards,
Ziyu
I am currently conducting research on a HCC (Hepatocellular Carcinoma) cohort using Stereo-seq (BGI/MGI) technology. Given CalicoST's superior performance in inferring allele-specific CNAs and reconstructing tumor phylogeography, I am highly interested in applying it to my dataset.
Questions:
Compatibility: Has CalicoST been tested on Stereo-seq data? Since Stereo-seq provides sub-cellular resolution, are there any specific considerations compared to the 10x Visium or Slide-tags data mentioned in the paper?
Preprocessing Pipeline: My data is processed via the SAW (Stereo-seq Analysis Workflow), which generates standard BAM files. I plan to use cellsnp-lite to extract germline heterozygous SNP counts. Does the CalicoST team have any specific recommendations for handling the unique coordinate system or the higher density of spots in Stereo-seq?
Binning Strategy: Stereo-seq is often analyzed at different "Bin" levels (e.g., Bin 20, Bin 50). To ensure sufficient SNP coverage for informative BAF values , is there a recommended bin size or a minimum number of SNP-covering UMIs per spot/bin that you would suggest for optimal HMRF performance? HMRF and Spatial Coherence: Given the high resolution and potential cell-mixing in HCC tissues, are there specific tips for tuning the spatial coherence parameters in the HMRF model to avoid over-smoothing while maintaining biological relevance? I believe CalicoST could provide invaluable insights into the spatial evolution of HCC clones in our cohort. Thank you for developing this powerful tool and for your time in addressing these questions!
Best regards,
Ziyu