我有一个 data.frame X，其中包含点/样本坐标 X1 和 X2:
> head(X)
X1 X2 Cluster Timepoint Transcripts MEF ESC
Drop_6_6A_0_TACCTAATCTAC 169.3437 20.18623 2 Day 0 49688 0.4366071 0.3260743
Drop_6_6A_0_TCAGCTTGTCAC 155.8880 -16.69927 3 Day 0 47365 0.4554254 0.3350818
Drop_6_6A_0_TCGCAATAAGAT 168.4270 36.50967 2 Day 0 44881 0.4114934 0.2595030
Drop_6_6A_0_AATCTACCAATC 164.3964 -27.17404 3 Day 0 44640 0.4748225 0.3525822
Drop_6_6A_0_GGATTAAGTTCA 162.2900 -24.10504 3 Day 0 36822 0.4723676 0.3391785
Drop_6_6A_0_TGATCTAGTGTC 155.4231 -19.18974 3 Day 0 35889 0.4664174 0.3408899
我想将选定的标记作为列添加到 X 并根据关联的表达式值调整散点图上的点的大小。
NANOG = t(data['NANOG',rownames(X)])
SAL4 = t(data['SAL4',rownames(X)])
COL5A2 = t(data['COL5A2',rownames(X)])
ESRRB = t(data['ESRRB',rownames(X)])
ELN = t(data['ELN',rownames(X)])
POU5f1 = t(data['POU5F1',rownames(X)])
PTN = t(data['PTN',rownames(X)])
CXCL5 = t(data['CXCL5',rownames(X)])
Z = cbind(X, NANOG, SAL4, POU5f1, ESRRB, COL5A2, ELN, PTN, CXCL5)
绑定(bind)此数据后，新的 data.frame Z 看起来像这样:
> head(Z)
X1 X2 Cluster Timepoint Transcripts MEF ESC NANOG NA POU5F1 ESRRB COL5A2 ELN PTN CXCL5
Drop_6_6A_0_TACCTAATCTAC 169.3437 20.18623 2 Day 0 49688 0.4366071 0.3260743 0.0000000 NA 0 0 5.113106 0 1.004522 0.2645434
Drop_6_6A_0_TCAGCTTGTCAC 155.8880 -16.69927 3 Day 0 47365 0.4554254 0.3350818 0.2763494 NA 0 0 3.068572 0 1.309109 1.0395819
Drop_6_6A_0_TCGCAATAAGAT 168.4270 36.50967 2 Day 0 44881 0.4114934 0.2595030 0.0000000 NA 0 0 5.264248 0 0.000000 0.0000000
Drop_6_6A_0_AATCTACCAATC 164.3964 -27.17404 3 Day 0 44640 0.4748225 0.3525822 0.0000000 NA 0 0 3.554919 0 1.592698 0.2916205
Drop_6_6A_0_GGATTAAGTTCA 162.2900 -24.10504 3 Day 0 36822 0.4723676 0.3391785 0.0000000 NA 0 0 3.838676 0 1.536569 1.9954283
Drop_6_6A_0_TGATCTAGTGTC 155.4231 -19.18974 3 Day 0 35889 0.4664174 0.3408899 0.0000000 NA 0 0 4.029014 0 6.187616 0.0000000
现在，我可以绘制单个散点图，其中点的大小对应于相应的表达式值(如下所示)，但我不确定如何在一个 facet_wrap 图中执行此操作。
library(gridExtra)
g = arrangeGrob(
ggplot(Z, aes(X1, X2, color=NANOG)) + ggtitle("NANOG") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='red') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.nanog.expression.no.noise.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=SAL4)) + ggtitle("SAL4") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='red') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.SAL4.expression.no.noise.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=POU5f1)) + ggtitle("POU5F1") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='red') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.pou5f1.expression.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=ESRRB)) + ggtitle("ESRRB") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='red') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.ESRRB.expression.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=COL5A2)) + ggtitle("COL5A2") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='green') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.col5a2.expression.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=ELN)) + ggtitle("ELN") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='green') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.eln.expression.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=PTN)) + ggtitle("PTN") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='green') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.ptn.expression.pdf"), height=pdf_height, width=pdf_width+5),
ggplot(Z, aes(X1, X2, color=CXCL5)) + ggtitle("CXCL5") +
geom_point() +
xlab(paste0("TSNE1")) +
ylab(paste0("TSNE2")) +
theme_bw() + theme(axis.line = element_line(colour = "black"), panel.grid.minor = element_blank(), panel.background = element_blank()) + scale_colour_gradient(low='light blue', high='green') +
ggsave(paste0(outdir, timepoint, ".tsne.",lab,".density.clustered.all.genes.TSNE1.TSNE2.cxcl5.expression.pdf"), height=pdf_height, width=pdf_width+5),
nrow=2, ncol=4
)
上面的代码按预期工作，但非常冗长并且不足以处理大量的选择标记，比如 100 个。我假设我必须以某种方式融化 Z data.frame ？任何帮助将不胜感激。
最佳答案
正如 OP 所建议的，一种方法是融化原始数据框 Z:
library(reshape2)
d <- melt(Z, id = 1:5, measure = 6:ncol(Z))
其中 id 可以是 id 变量的整数向量(列索引)或字符串(列名)，measure 是一个向量，给出各种度量的位置(标记在这种情况下)。然后调用 ggplot:
library(ggplot2)
ggplot(d, aes(x = X1, y = X2, size = value)) +
geom_point() +
facet_wrap(~ variable)
根据需要添加标签和其他装饰。使用 OP 从 Z 中提取的输出:
关于r - ggplot facet_wrap 选定的 data.frame 列？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/39402715/