cover
写在前面
【这图怎么画】系列的图都来自VIP群里同学的提问。推文只是对图片的复现，不代表作者对图片展现形式的认同。欢迎同学们在群里分析有意思的图片。
本期图片
❝「Title：」Typing characteristics of metabolism-related genes in osteoporosis
「Journal：」Front. Pharmacol.
❞
读图
❝The expression difference box plot represents the difference in expression levels of GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9 genes among the three isoforms.
❞
没有什么特殊。之前画过的箱线图：
- 跟着 Cell 学作图 | 3.箱线图+散点+差异显著性检验
- 跟着Nat Commun学作图 | 1.批量箱线图+散点+差异分析
- 跟着Nat Commun学作图 | 4.配对箱线图+差异分析
- R实战 | 对称云雨图 + 箱线图 + 配对散点 + 误差棒图 +均值连线
- 跟着Nature学作图 | 质控箱线图
- 跟着 Cell 学作图 | 箱线图+散点(组间+组内差异分析)
复现结果
row1
all
绘图
# loda data ana preprocess mRNA <- read.csv("All_mRNA_FPKM.csv",header=T,row.names=1) #log2 bar_mat <- t(log2(mRNA+1)) # group info anno <- read.csv("sample_index.csv",header=T,row.names=1) anno$type2 <- anno$Type anno <- anno[rownames(bar_mat),] bar_mat <- bar_mat[rownames(anno),] bar_mat<- as.data.frame(bar_mat) bar_mat$sam <- anno$Type ## plot library(RColorBrewer) library(ggpubr) library(ggplot2) bar_mat$sam<-factor(bar_mat$sam,levels=c("C1","C2","C3","C4")) # comparisons my_comparisons <- list(c("C1", "C2"), c("C1", "C3"), c("C1", "C4")) # gene list gc <- head( colnames(bar_mat), -1) #开始批量绘制 plist<-list() for (i in 1:length(gc)){ bar_tmp<-bar_mat[,c(gc[i],"sam")] colnames(bar_tmp)<-c("Expression","sam") pb1<- ggplot(data = bar_tmp,aes(x = sam, y = Expression , fill = sam))+ scale_fill_manual(values = mycol[c(7,5,3,1)]) + geom_violin(alpha = 0.4, position = position_dodge(width = .75), size = 0.8, color="black") + geom_boxplot(notch = TRUE, outlier.size = -1, color="black", lwd=0.8, alpha = 0.7) + geom_point(shape = 21, size=2, position = position_jitterdodge(), color="black", alpha = 1) + theme_bw() + ylab("Log12(FPKM+1)") + xlab(gc[i]) + theme(axis.text.x = element_text(size = 12, color = "black"), axis.ticks = element_line(size=0.2, color="black"), axis.ticks.length = unit(0.2, "cm"), legend.position = "none", panel.background = element_blank(), panel.grid = element_blank(), axis.title = element_text(size = 12), axis.text = element_text(size = 12)) + stat_compare_means(method="t.test",hide.ns = F,comparisons =my_comparisons,label="p.signif") plist[[i]]<-pb1 } # cowplot library(cowplot) p <- plot_grid(plotlist = plist, ncol = 5) ## save ggsave("boxplot1208.pdf",width = 14,height = 20)
往期内容
- CNS图表复现|生信分析|R绘图 资源分享&讨论群！
- 这图怎么画| 有点复杂的散点图
- 这图怎么画 | 相关分析棒棒糖图
- 组学生信| Front Immunol |基于血清蛋白质组早期诊断标志筛选的简单套路
- (免费教程+代码领取)|跟着Cell学作图系列合集
- Q&A | 如何在论文中画出漂亮的插图？
- 跟着 Cell 学作图 | 桑葚图(ggalluvial)
- R实战 | Lasso回归模型建立及变量筛选
- 跟着 NC 学作图 | 互作网络图进阶(蛋白+富集通路)(Cytoscape)
- R实战 | 给聚类加个圈圈(ggunchull)
- R实战 | NGS数据时间序列分析(maSigPro)
- 跟着 Cell 学作图 | 韦恩图(ggVennDiagram)
