文件在单细胞\5_GC_py\1_single_cell\3.AUCell.Rmd
1.基因
rm(list = ls())
load("g.Rdata")
2.AUCell
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897923
IF: NA NA NA
用这个文章里的方法，将单细胞亚群的marker基因与ros相关基因取交集，用作AUCell的基因集
The intersection of marker genes was selected based on strong population specificity (adj_p < 0.05 & |avg_log2FoldChange| > 1.5 & pct.1 > 0.5 & pct.2 < 0.5) from each cell subgroup and factors related to OS responses.
seu.obj是seurat+singleR得出的对象，allmarkers是findallmarkers得到的数据框。
library(Seurat)
library(tidyverse)
load("seu.obj.Rdata")
DotPlot(seu.obj,features = g,cols = "RdYlBu")+RotatedAxis()
AUCell用于计算每个细胞中特定基因集的活性程度。用上面的gi作为基因集来计算。
AUCell的三个步骤:
Build the rankings：矩阵中的每个细胞里，给基因进行排序。
Calculate the Area Under the Curve (AUC)：计算每个细胞的AUC值
Set the assignment thresholds：计算活性区分的阈值
library(GSEABase)
geneSets <- GeneSet(g, setName="pyroptosis")
geneSets
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