Revigo，高大上地可视化GO富集结果

只要有规则的地方，就有不公平。

1. 使用TBtools做GO富集，会得到以下三个文件

2. 使用Excel打开其中一个，比如比较经常使用的Biological Process，复制p_adjust小于等于0.05的GO term对应的p_adjust值和GO号

   

3. 黏贴到revigo网站，http://revigo.irb.hr/

4. 点击START REVIGO，并等待运行结果

5. 在结果窗口中，基于个人生物学知识，调整展示和不展示GO term信息

6. 以上即可输出可用图片，但也可以直接下载对应的R语言代码

# A plotting R script produced by the REVIGO server at http://revigo.irb.hr/# If you found REVIGO useful in your work, please cite the following reference:# Supek F et al. "REVIGO summarizes and visualizes long lists of Gene Ontology# terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021800# --------------------------------------------------------------------------# If you don't have the ggplot2 package installed, uncomment the following line:# install.packages( "ggplot2" );library( ggplot2 );# --------------------------------------------------------------------------# If you don't have the scales package installed, uncomment the following line:# install.packages( "scales" );library( scales );# --------------------------------------------------------------------------# Here is your data from REVIGO. Scroll down for plot configuration options.revigo.names <- c("term_ID","description","frequency_%","plot_X","plot_Y","plot_size","log10_p_value","uniqueness","dispensability");revigo.data <- rbind(c("GO:0006952","defense response", 0.568,-6.246,-0.606, 4.863,-4.8239,0.784,0.000),c("GO:0007389","pattern specification process", 0.147, 2.510, 6.231, 4.274,-3.2291,0.753,0.000),c("GO:0009813","flavonoid biosynthetic process", 0.016,-0.869, 0.192, 3.317,-4.0097,0.944,0.000),c("GO:0043086","negative regulation of catalytic activity", 0.400,-4.048,-6.307, 4.710,-1.4966,0.914,0.000),c("GO:0050896","response to stimulus",12.210,-3.069, 6.021, 6.195,-3.5436,0.981,0.000),c("GO:0071554","cell wall organization or biogenesis", 0.950,-3.397, 7.169, 5.086,-2.4392,0.969,0.000),c("GO:0009812","flavonoid metabolic process", 0.018,-1.358, 2.846, 3.355,-3.5229,0.971,0.026),c("GO:0045490","pectin catabolic process", 0.058, 2.542, 0.308, 3.875,-1.8497,0.944,0.028),c("GO:0071555","cell wall organization", 0.709, 5.224, 1.318, 4.959,-2.2657,0.897,0.034),c("GO:0019419","sulfate reduction", 0.041, 6.689,-0.514, 3.722,-2.5740,0.900,0.057),c("GO:0034219","carbohydrate transmembrane transport", 0.245, 1.831,-5.809, 4.497,-2.1476,0.767,0.065),c("GO:0019748","secondary metabolic process", 0.138, 0.766,-1.413, 4.247,-1.4512,0.922,0.109),c("GO:0072330","monocarboxylic acid biosynthetic process", 0.940, 6.450,-1.591, 5.081,-1.4465,0.872,0.150),c("GO:0040008","regulation of growth", 0.172,-3.857,-5.673, 4.343,-1.4374,0.926,0.212),c("GO:0009411","response to UV", 0.054,-5.490, 0.447, 3.837,-1.3321,0.828,0.291),c("GO:0006812","cation transport", 3.242, 2.882,-5.850, 5.619,-1.5714,0.829,0.303),c("GO:0009617","response to bacterium", 0.145,-5.863, 1.628, 4.271,-1.9954,0.769,0.315),c("GO:0009926","auxin polar transport", 0.005,-0.194,-5.934, 2.820,-2.0494,0.777,0.340),c("GO:0009607","response to biotic stimulus", 0.342,-6.842,-0.560, 4.643,-2.5022,0.806,0.340),c("GO:0009719","response to endogenous stimulus", 0.526,-6.370, 0.584, 4.829,-3.4868,0.800,0.354),c("GO:0010817","regulation of hormone levels", 0.161,-2.921,-5.829, 4.314,-1.8148,0.902,0.401),c("GO:0019761","glucosinolate biosynthetic process", 0.001, 6.353,-1.095, 2.093,-1.8164,0.863,0.406),c("GO:0042221","response to chemical", 3.071,-6.236,-0.067, 5.595,-4.4737,0.771,0.427),c("GO:0009628","response to abiotic stimulus", 0.571,-7.061, 0.333, 4.865,-1.5622,0.799,0.427),c("GO:0010033","response to organic substance", 0.900,-6.276, 0.979, 5.062,-4.2472,0.739,0.451),c("GO:0009605","response to external stimulus", 1.370,-6.741, 0.103, 5.245,-2.3436,0.785,0.475),c("GO:0010073","meristem maintenance", 0.006, 3.650, 5.427, 2.882,-1.9032,0.782,0.483),c("GO:0055085","transmembrane transport", 8.916, 2.473,-6.332, 6.058,-4.1952,0.830,0.489),c("GO:0045229","external encapsulating structure organization", 0.795, 1.786, 2.167, 5.009,-2.0656,0.952,0.498),c("GO:1902578","single-organism localization", 5.919, 3.065,-5.949, 5.881,-2.1126,0.801,0.525),c("GO:0042335","cuticle development", 0.012, 2.249, 5.817, 3.182,-2.8356,0.783,0.526),c("GO:0006811","ion transport", 5.344, 1.993,-6.420, 5.836,-1.5428,0.838,0.535),c("GO:0080134","regulation of response to stress", 0.337,-5.724,-1.777, 4.636,-2.2141,0.750,0.542),c("GO:0015696","ammonium transport", 0.148, 3.719,-5.195, 4.279,-1.5985,0.861,0.558),c("GO:0048364","root development", 0.029, 2.992, 5.614, 3.573,-1.6935,0.763,0.559),c("GO:0022622","root system development", 0.029, 3.011, 5.808, 3.573,-1.6935,0.771,0.559),c("GO:0006950","response to stress", 4.575,-6.453, 0.197, 5.769,-2.2626,0.763,0.562),c("GO:0048507","meristem development", 0.013, 1.901, 6.327, 3.232,-1.5983,0.814,0.567),c("GO:0009855","determination of bilateral symmetry", 0.037, 2.857, 5.904, 3.672,-2.4190,0.746,0.567),c("GO:0009684","indoleacetic acid biosynthetic process", 0.000,-0.921,-4.011, 1.716,-1.4543,0.834,0.576),c("GO:0099402","plant organ development", 0.055, 2.681, 6.220, 3.849,-1.4713,0.764,0.584),c("GO:0072488","ammonium transmembrane transport", 0.086, 3.469,-5.237, 4.045,-1.3820,0.859,0.584),c("GO:0044765","single-organism transport", 5.708, 2.272,-5.828, 5.865,-2.1999,0.753,0.599),c("GO:0009888","tissue development", 0.491, 3.129, 6.025, 4.799,-2.7375,0.783,0.605),c("GO:0009725","response to hormone", 0.335,-6.779, 1.129, 4.633,-4.3261,0.701,0.649),c("GO:0010411","xyloglucan metabolic process", 0.016, 4.155, 0.482, 3.321,-1.7273,0.892,0.675),c("GO:0009746","response to hexose", 0.031,-6.512, 2.044, 3.603,-1.7112,0.743,0.678),c("GO:0006810","transport",17.616, 2.506,-6.117, 6.354,-1.3249,0.818,0.684),c("GO:0009743","response to carbohydrate", 0.041,-6.655, 1.736, 3.726,-1.7512,0.750,0.692),c("GO:0009955","adaxial/abaxial pattern specification", 0.002, 3.028, 5.362, 2.407,-1.4499,0.777,0.695));one.data <- data.frame(revigo.data);names(one.data) <- revigo.names;one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );one.data$frequency <- as.numeric( as.character(one.data$frequency) );one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );#head(one.data);# --------------------------------------------------------------------------# Names of the axes, sizes of the numbers and letters, names of the columns,# etc. can be changed belowp1 <- ggplot( data = one.data );p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_size_area();p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_size_area();p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );ex <- one.data [ one.data$dispensability < 0.15, ]; p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");p1 <- p1 + theme(legend.key = element_blank()) ;one.x_range = max(one.data$plot_X) - min(one.data$plot_X);one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10);# --------------------------------------------------------------------------# Output the plot to screenp1;# Uncomment the line below to also save the plot to a file.# The file type depends on the extension (default=pdf).# ggsave("C:/Users/path_to_your_file/revigo-plot.pdf");

7. 打开R，并直接运行脚本，即可输出对应的R语言图

8. 修改其中着色代码，生成好看的图片

9. 使用AI或者coredraw等工具，打标签，即可得到类似下图