Hello everyone,
I am using CytoTRACE2 to analyze single-cell RNA sequencing data in R, and I encountered an issue where the potency score results contradict established biological knowledge. I would appreciate any advice or suggestions to resolve this problem.
Here is Code Used:
cytotrace2_result <- cytotrace2(seurat, is_seurat = TRUE, slot_type = "counts", species = 'human', seed = 0220)
annotation <- data.frame(phenotype = seurat@meta.data$celltype) %>% set_rownames(., colnames(seurat))
plots <- plotData(cytotrace2_result = cytotrace2_result,
annotation = annotation,
is_seurat = TRUE)
and I found Potency Score of Naive B Cells Lower Than Memory B and Plasmablasts in the result. I think it is contract to biological knowledge, but I have no idea how to figure it. Thank you in advance for your help! Any insights or suggestions would be greatly appreciated.
I think expression-based estimations like "potency score" and the broader concept of trajectories are prone to being affected by the rate of metabolism and relatively quiescent populations. IIRC Cytotrace estimates stemness potential by measuring transcriptional diversity - if a metabolically quiet cell is just turning over then you assume it will have relatively narrow scope of transcription. I believe Cytoscape in general tries to overcome this limitation but I suppose it is still an inference-based approach and will be prone to limitations.
Assuming there is nothing off about your data specifically, the most basic sanity check you can perform is to measure whether your naive B cell population has a lower median feature count per cell than your memory and plasmablasts. You could also benchmark against a published dataset of similar populations.
Thanks a lot for your response! I will have a try.