气球图

气泡图

可以利用圆圈的大小和颜色呈现不同的信息。

颜色=大小

上图可以很明显的比较不同癌症中，重点基因的表达上调，下调情况。

输入数据：

CancerType	SEMA3A	SEMA3B	SEMA3C	SEMA3D	SEMA3E	SEMA3F	SEMA3G
ACC	0.3058794	0.6749167	1.553152	0.5609269	0.04308815	2.26759	1.071968
BLCA	0.2604646	2.000748	2.540686	3.206074	0.1042546	5.011131	0.973269
BRCA	0.670601057	4.377172833	4.029076017	0.737759733	0.91107385	5.417881198	2.049093333
CESC	0.030040274	3.10372984	0.856697707	0.003915114	0	5.456114412	0.132489277
CHOL	0.095912868	4.07200434	1.314131662	0.600759078	1.992685462	2.314930492	0.987326396
COAD	0.873017985	3.451703237	3.127341383	0.013969328	0.005579384	4.008015946	0.209814414
DLBC	0.272125554	0.219965593	1.397671738	0.109106107	0	1.996505875	0.975258625
ESCA	0.544115639	3.507967898	2.424222105	0.020936575	1.483539393	4.889857263	1.820995419
GBM	1.94317517	2.684062356	0.839038003	0.259588685	0.846527302	1.794908944	1.009469544
HNSC	2.311874186	2.07577583	4.365968393	2.789724274	0.255179005	4.492226761	0.936592175
KICH	0.1803919	0.7990171	2.33629	0.2293381	0.008582394	6.087315	3.024843
KIRC	0.244401766	1.558844062	1.753992572	1.399060231	0.008017802	5.122464213	1.920018423
KIRP	0.181453169	4.41545663	2.743989829	0.075370437	0.162205209	3.06193522	1.411410981
LAML	0.061109013	0.236436959	1.292188066	0	0.005767489	1.742797077	0.25603614
LGG	0.45521916	5.099739943	1.511859575	1.471356421	0.31653094	0.425500578	2.050405054
LIHC	0.04540965	1.999732	0.0717533	0.02480912	0	2.804368	0.7499197
LUAD	2.447074057	4.575235422	2.617218951	0.422363027	0.268892634	2.660777231	1.455641511
LUSC	0.594260148	0.639515371	2.319055716	0.07517063	0.062451757	4.202372562	0.811142418
MESO	0.29529343	4.576696133	5.579111428	0.300853327	0.463107847	2.96484522	0.673253843
OV	0.607179331	4.786856448	3.853027384	0.181927986	0.116899632	4.669398164	1.765830259
PAAD	0.7162406	0.8992736	0.6019803	0.04994482	0.472778	2.207573	2.329929
PCPG	0.094462773	2.028825356	1.256253813	0.08269041	0.012272302	2.188914513	3.088547101
PRAD	0.3908217	1.142047	5.156465	2.39914	1.583892	2.966339	1.002553
READ	0.720759497	2.831257616	2.899446575	0	0	5.247167593	1.308290664
SARC	0.488146647	3.577916624	4.32198378	1.120329044	0.008985208	2.731882694	3.413901154
SKCM	2.638949	4.913505	5.401904	0.293977	0	1.845956	1.084495
STAD	0.47690858	4.297823133	3.304884099	0.080311232	0.065753996	3.166858468	0.611160518
TGCT	1.259307349	2.52271991	0.67248451	0.169443112	0.529362333	3.156383791	1.186455429
THCA	0.138214622	2.021548245	0.757462935	0.12121475	2.141452093	3.390107498	1.68488299
THYM	0.498055011	1.428231405	1.69062676	3.148596065	2.048798365	7.224941304	0.948677739
UCEC	0.083812554	1.639466861	2.145916633	0.153562717	0.494608775	4.628178397	0.53350085
UCS	2.917264867	2.397795149	4.81695624	0.191347885	0.818735876	5.247746402	0.213337442
UVM	0.021517288	2.868286214	4.65984434	0.007181645	0.046549024	0.945125876	0.361792439

代码：

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library(ggpubr)                 inputFile <- "input.txt"           # 输入文件 outFile <- "ggballoonplot.pdf"     # 输出文件 setwd("")       # 读取文件 data <- read.table(inputFile, header = T, sep = "\t", check.names = F, row.names = 1)   # 绘制气泡图 p <- ggballoonplot(data, fill = 'value') +  # 按“值”的大小填充颜色   gradient_fill(c("blue", "white", "red"))  # 更改渐变颜色   # 输出图片文件 pdf(file = outFile, width = 8, height = 7) print(p) dev.off()

但是由于上图中颜色和大小都是以“value”来定义的，所以只能显示基因表达量的信息，而不能看出各基因差异表达的P值。

我们可以分别指定圆圈颜色和大小所对应的值，就可以展示更多的信息：

颜色≠大小

输入数据：（同上）

CancerType	SEMA3A	SEMA3B	SEMA3C	SEMA3D	SEMA3E	SEMA3F	SEMA3G
ACC	0.3058794	0.6749167	1.553152	0.5609269	0.04308815	2.26759	1.071968
BLCA	0.2604646	2.000748	2.540686	3.206074	0.1042546	5.011131	0.973269
BRCA	0.670601057	4.377172833	4.029076017	0.737759733	0.91107385	5.417881198	2.049093333
CESC	0.030040274	3.10372984	0.856697707	0.003915114	0	5.456114412	0.132489277
CHOL	0.095912868	4.07200434	1.314131662	0.600759078	1.992685462	2.314930492	0.987326396
COAD	0.873017985	3.451703237	3.127341383	0.013969328	0.005579384	4.008015946	0.209814414
DLBC	0.272125554	0.219965593	1.397671738	0.109106107	0	1.996505875	0.975258625
ESCA	0.544115639	3.507967898	2.424222105	0.020936575	1.483539393	4.889857263	1.820995419
GBM	1.94317517	2.684062356	0.839038003	0.259588685	0.846527302	1.794908944	1.009469544
HNSC	2.311874186	2.07577583	4.365968393	2.789724274	0.255179005	4.492226761	0.936592175
KICH	0.1803919	0.7990171	2.33629	0.2293381	0.008582394	6.087315	3.024843
KIRC	0.244401766	1.558844062	1.753992572	1.399060231	0.008017802	5.122464213	1.920018423
KIRP	0.181453169	4.41545663	2.743989829	0.075370437	0.162205209	3.06193522	1.411410981
LAML	0.061109013	0.236436959	1.292188066	0	0.005767489	1.742797077	0.25603614
LGG	0.45521916	5.099739943	1.511859575	1.471356421	0.31653094	0.425500578	2.050405054
LIHC	0.04540965	1.999732	0.0717533	0.02480912	0	2.804368	0.7499197
LUAD	2.447074057	4.575235422	2.617218951	0.422363027	0.268892634	2.660777231	1.455641511
LUSC	0.594260148	0.639515371	2.319055716	0.07517063	0.062451757	4.202372562	0.811142418
MESO	0.29529343	4.576696133	5.579111428	0.300853327	0.463107847	2.96484522	0.673253843
OV	0.607179331	4.786856448	3.853027384	0.181927986	0.116899632	4.669398164	1.765830259
PAAD	0.7162406	0.8992736	0.6019803	0.04994482	0.472778	2.207573	2.329929
PCPG	0.094462773	2.028825356	1.256253813	0.08269041	0.012272302	2.188914513	3.088547101
PRAD	0.3908217	1.142047	5.156465	2.39914	1.583892	2.966339	1.002553
READ	0.720759497	2.831257616	2.899446575	0	0	5.247167593	1.308290664
SARC	0.488146647	3.577916624	4.32198378	1.120329044	0.008985208	2.731882694	3.413901154
SKCM	2.638949	4.913505	5.401904	0.293977	0	1.845956	1.084495
STAD	0.47690858	4.297823133	3.304884099	0.080311232	0.065753996	3.166858468	0.611160518
TGCT	1.259307349	2.52271991	0.67248451	0.169443112	0.529362333	3.156383791	1.186455429
THCA	0.138214622	2.021548245	0.757462935	0.12121475	2.141452093	3.390107498	1.68488299
THYM	0.498055011	1.428231405	1.69062676	3.148596065	2.048798365	7.224941304	0.948677739
UCEC	0.083812554	1.639466861	2.145916633	0.153562717	0.494608775	4.628178397	0.53350085
UCS	2.917264867	2.397795149	4.81695624	0.191347885	0.818735876	5.247746402	0.213337442
UVM	0.021517288	2.868286214	4.65984434	0.007181645	0.046549024	0.945125876	0.361792439

 data1：

代码：

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library(ggpubr)                 inputFile <- "input.txt"            # 输入文件 outFile <- "ggballoonplot1.pdf"     #输出文件 setwd("")     # 读取文件并按需修改 data <- read.table(inputFile, header = T, sep = "\t", check.names = F, row.names = 1) data1 <- data data1$type <- rownames(data) data1 <- data1[,c(8,1:7)] data1 <- melt(data1, id.vars = "type") data1$P.value <- runif(231,0,0.07)  # 随机生成P值，用于举例 colnames(data1) <- c("Type","Gene","Value", "P.value") data1$Type <- factor(data1$Type) data1$Type <- factor(data1$Type, levels = rev(levels(data1$Type)))   # 绘制气泡图 p1 <- ggballoonplot(data1,                     x = "Gene", y = "Type",  # x轴和y轴的意义                     fill = "P.value",       # 按“P.value”的值填充颜色                     size = "Value") +   # 按“Value”的值设置圆圈的大小   gradient_fill(c("blue", "white", "red"))  # 更改渐变颜色   # 输出图形文件 pdf(file = outFile, width = 8, height = 7) print(p1) dev.off()

 

 

两张图放在一起对比一下：