阅读： 2022/4/6 10:06:11
今天做一下《journal of extracellular vesicles》IF:25上的一个图，其实图不是重点，重要的是这个思路。
图形如下，是差异分析的结果，只不过这个展示的是两个组的差异结果。我们利用一个更加通俗的例子来说明这个图的意义和思路，就不套用这个文章的结果了。假设我们有某个癌症的组织样本，cancer vs normal得到癌变后差异表达蛋白，而同样的。我们也去取血液的样本，检测血液中蛋白的变化，找到差异蛋白，这个时候就可以将这两个数据合在一起比较，比如某些基因在癌组织和血液中变化一致，可以认为他们是正常相关的，通过这样的思路我们可以确定某些标志物，也就是血液能够代替活检的相关蛋白筛选。大概意思就是这样，具体我们可以用下面的图呈现。
做这个图需要的数据是两组差异比较的差异倍数和FDR，这里我们首先用ggplot画一个散点图。
library(ggplot2)
library(ggrepel)
ggplot(A, aes(x=MeanRatio_X, y=MeanRatio_Y)) +
geom_hline(yintercept= c(-0.6, 0.6), color = "black", size=1) +#添加横线
geom_vline(xintercept=c(-0.6, 0.6), color = "black", size=1)+
geom_point()
可以看到x轴y轴范围和文章不一样，这里不清楚作者为什么这么设置，暂且按照它的数据吧。接下来需要修饰坐标轴，标记不同象限的点。
ggplot(A, aes(x=MeanRatio_X, y=MeanRatio_Y)) +
geom_hline(yintercept= c(-0.6, 0.6), color = "black", size=1) +
geom_vline(xintercept=c(-0.6, 0.6), color = "black", size=1)+
geom_point(size=3)+
geom_point(data = A[A$MeanRatio_X>0.6 | A$MeanRatio_X < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_Y>0.6 | A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_X>0.6 & A$MeanRatio_Y>0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y< -0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y > 0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(data = A[A$MeanRatio_X > 0.6 & A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(size = 3,shape=21)+
xlim(-4,4)+
ylim(-3.2, 4.1)
接下来就是修改ggplot主题了。
ggplot(A, aes(x=MeanRatio_X, y=MeanRatio_Y)) +
geom_hline(yintercept= c(-0.6, 0.6), color = "black", size=1) +
geom_vline(xintercept=c(-0.6, 0.6), color = "black", size=1)+
geom_point(size=3)+
geom_point(data = A[A$MeanRatio_X>0.6 | A$MeanRatio_X < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_Y>0.6 | A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_X>0.6 & A$MeanRatio_Y>0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y< -0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y > 0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(data = A[A$MeanRatio_X > 0.6 & A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(size = 3,shape=21)+
xlim(-4,4)+
ylim(-3.2, 4.1)+
labs(x = "Mean Ratio Log2(AD F3 EVs vs NC F3 EVs)",
y = "Mean Ratio Log2(AD BH vs NC BH)", title = "") +
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black', size = 1),
panel.background = element_blank(),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 14, hjust = 0.5),
axis.text = element_text(size = 14, color = 'black'),
axis.title = element_text(size = 14, color = 'black'))+
theme(legend.position = "right")
添加标签，原文中作者是选择了一些miRNA呈现，但是呈现效果我认为很差（可能没想着让看），都重叠在一起看不出来，我这里选择一部分显示标签。
miRNA <- A[which(A$FDR_Y<0.1 | A$FDR_X<0.1),]
miRNA <- miRNA[which(abs(miRNA$MeanRatio_Y) >1 |abs(miRNA$MeanRatio_X)>1),]
ggplot(A, aes(x=MeanRatio_X, y=MeanRatio_Y)) +
geom_hline(yintercept= c(-0.6, 0.6), color = "black", size=1) +
geom_vline(xintercept=c(-0.6, 0.6), color = "black", size=1)+
geom_point(size=3)+
geom_point(data = A[A$MeanRatio_X>0.6 | A$MeanRatio_X < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_Y>0.6 | A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="grey")+
geom_point(data = A[A$MeanRatio_X>0.6 & A$MeanRatio_Y>0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y< -0.6, ], size=3, shape=16, color="#66CC33")+
geom_point(data = A[A$MeanRatio_X< -0.6 & A$MeanRatio_Y > 0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(data = A[A$MeanRatio_X > 0.6 & A$MeanRatio_Y < -0.6, ], size=3, shape=16, color="#CC3300")+
geom_point(size = 3,shape=21)+
xlim(-4,4)+
ylim(-3.2, 4.1)+
labs(x = "Mean Ratio Log2(AD F3 EVs vs NC F3 EVs)",
y = "Mean Ratio Log2(AD BH vs NC BH)", title = "") +
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black', size = 1),
panel.background = element_blank(),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 14, hjust = 0.5),
axis.text = element_text(size = 14, color = 'black'),
axis.title = element_text(size = 14, color = 'black'))+
geom_text_repel(data=miRNA, aes(label=rownames(miRNA)), color="black", size=4, fontface="italic",
point.padding = 0.3, segment.color = 'black', segment.size = 0.3, force = 1, max.iter = 3e3)
这个图还是很有意义的，显示的信息也挺多。可以结合自己的数据，看能不能有应用的地方。
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