Hello crew! @mlist and I were discussing the fact that 2nd-generation deconvolution could in principle unlock the quantification of closely related human immune cell types (e.g. T cell subtypes).
To test this, we could set up a simplified simulation-based benchmarking using the same single-cell data for signature building and pseudobulk data simulation. Maybe even without mRNA bias.
In this simplified scenario, we could consider some fine-grained classification of T cell subtypes (e.g. Th1/Th2, or naive/activated/exhausted) to assess the methods.
Maybe @kathireinisch could run this evaluation for her project? With the support of @alex-d13 with what regards the new simulator, of course.
Hello crew! @mlist and I were discussing the fact that 2nd-generation deconvolution could in principle unlock the quantification of closely related human immune cell types (e.g. T cell subtypes).
To test this, we could set up a simplified simulation-based benchmarking using the same single-cell data for signature building and pseudobulk data simulation. Maybe even without mRNA bias. In this simplified scenario, we could consider some fine-grained classification of T cell subtypes (e.g. Th1/Th2, or naive/activated/exhausted) to assess the methods. Maybe @kathireinisch could run this evaluation for her project? With the support of @alex-d13 with what regards the new simulator, of course.
I do not have any dataset in mind for Th1/Th2, but this is a nice one with annotated T cell subtypes including exhausted T cells: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179994
Any other ideas?
Cheers, Francesca