Entering edit mode
12 months ago
odi
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10
I am still relatively new to bioinformatics analysis.... I was wondering in Seurat could it be possible to find what percentage of cells in each cluster are PC vs IC cells? I would like to get a spreadsheet of this information.
Imagine, my PC genes and IC genes list are and i have already SCtransformed my Seurat object:
IC <- c("Kit", "Atp6v1c2", "Atp6v0d2", "Foxi1")
PC <- c("Kcne1", "Hsd11b2", "Aqp2", "Fxyd4")
I hope this makes sense because i don't have any code just my thoughts.
What does it mean for a cell to be PC or IC? (and what do PC and IC stand for?) Do you have an established criteria for this? If you were to take these genes, and extract the expression values from each cell and make a heat map would cells easily partition into expressing more of one set than the other?
PC - PRINCIPAL CELLS IC- INTERCALATED CELLS.
The established criteria is based off previous lab research.
To answer your last question, most likely! But i don't know that is why i am trying to figure out how to find which percentage of cells in each cluster are PC vs IC cells
I get that. What I mean, specifically, is a working definition of this observation. i.e. For a cell to be PC it must contain at least one read (count) from each of the PC genes, and none from the IC genes. Make sense? You have 8 genes in two categories. What calculation was done on these genes in each cell to define a cell as PC or IC? If you know that, you can do that calculation, and then count how many cells in each category you have.