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Possible to use multiple single cell datasets in Tangram? #103
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Hi @boyangzhang1993, Thank you for using Tangram! The recommendation for the Tangram is to use single cell data and spatial data coming from the same tissue for the most meaningful results. However, we have used Tangram in situations where single cell and spatial data come from different tissue sections but ideally from the same species and tissue type. While the mapping result will vary if you're using a combined dataset, I think using a combined single cell dataset will help in predicting gene expressions (incase of gene imputation). So depending on your use case:
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Thank you for your detailed response regarding the use of Tangram with single-cell datasets. In my specific case, I have multiple single-cell datasets representing liver tissue with cancer and liver tissue without cancer, and spatial transcriptomic data. My primary goal is to use Tangram to predict whether a specific voxel in the spatial data represents cancerous or non-cancerous liver cells. Given this context:
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Hi @boyangzhang1993, In your case, since you want to be able to analyze if a voxel has cancerous/ non-cancerous cells, combining single cell data with cancerous and non-cancerous cells would help! |
Thanks for developing Tangram, and it is very much appreciated.
Is it possible to use multiple single-cell datasets, such as merged two datasets, for predictions? Would you recommend making predictions one dataset by one or combining them together? Please feel free to share any advice.
Thanks in advance.
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