scRNA-data中的R值

武艺晴小朋友你好2024-09-07 9:10

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当我们测序拿得到各个样本中基因的表达值，就可以用基因表达值来表征样本间的相关性

代码如下：

#样本间相似性：R值相关性捕获到的基因在两个样本间表达趋势一致性

exp_RNA <- AverageExpression(fasting_memory,

group.by = "Sample",layer = "data") #CPM值来自data图层

exp_RNA <- as.data.frame(exp_RNA)

colnames(exp_RNA) <- c("fed","health","memory_10d","memory_35d","memory_66d")

library(ArchR)

library(viridis)

head(exp_RNA)

df<-exp_RNA $,c(1,5)$ #依次计算各个组

head(df)

#为了提高数据质量和准确性，使用两组间表达值都非0的基因用于R值的计算

df<-subset.data.frame(df,df$fed!=0)

df<-subset.data.frame(df,df$memory_66d!=0)

cor(df $,2$ ,df $,1$ )

library(ggrepel)

df$gene <- rownames(df)

df $slope \<- df$ memory_66d/df$fed #斜率代表在66d组中跟fed组间的表达差别很大

head(df)

label <- subset.data.frame(df,df$slope>1000)

head(label)

ggPoint(x = df $fed,y = df$ memory_66d,size=1,

title = "r=0.41",

colorDensity = TRUE,

continuousSet = "solarExtra",

ylabel = "memory_66d:log2(CPM+1)",

xlabel = "fed:log2(CPM+1)",

xlim = c(0,170),

ylim = c(0,170))+ mytheme+

geom_hline(yintercept = 40, lty = "dashed")+

geom_vline(xintercept = 40, lty = "dashed")

#图的样子：