绘制Alpha多样性线箱图
绘图和统计全部为R语言,建议复制代码,在Rstuido中运行,并设置工作目录为存储之前分析结果文件的result目录
# 运行前,请在Rstudio中菜单栏选择“Session - Set work directory -- Choose directory”,弹窗选择之前分析目录中的result文件夹 # 安装相关软件包,如果末安装改为TRUE运行即可安装 if (FALSE){ source("https://bioconductor.org/biocLite.R") biocLite(c("ggplot2")) } # 加载相关软件包 library("ggplot2") # load related packages # 读入实验设计和Alpha多样性值 design = read.table("design.txt", header=T, row.names= 1, sep="\t") alpha = read.table("alpha.txt", header=T, row.names= 1, sep="\t") # 以Observed OTU为例进行可视化和统计分析,其它指数将observed_otus替换为shannon, chao1, PD_whole_tree即可计算 # 合并Alpha指数与实验设计 index = cbind(alpha, design[match(rownames(alpha), rownames(design)), ]) # 绘图代码、预览、保存PDF p = ggplot(index, aes(x=genotype, y=observed_otus, color=genotype))+ geom_boxplot(alpha=1, outlier.size=0, size=0.7, width=0.5, fill="transparent") + geom_jitter( position=position_jitter(0.17), size=1, alpha=0.7)+ labs(x="Groups", y="observed_otus index") p ggsave(paste("alpha_observed_otus.pdf", sep=""), p, width = 5, height = 3) # 统计组间是否显著差异 # anova对指数与分组统计 observed_otus_stats <- aov(observed_otus ~ genotype, data = index) # 使用TukeyHSD对组间进行检验,效正pvalue Tukey_HSD_observed_otus <- TukeyHSD(observed_otus_stats, ordered = FALSE, conf.level = 0.95) # 结果中提取需要的结果 Tukey_HSD_observed_otus_table <- as.data.frame(Tukey_HSD_observed_otus$genotype) # 预览结果 Tukey_HSD_observed_otus_table # 保存结果到文件,按Pvaule值由小到大排序 write.table(Tukey_HSD_observed_otus_table[order(Tukey_HSD_observed_otus_table$p, decreasing=FALSE), ], file="alpha_observed_otus_stats.txt",append = FALSE, quote = FALSE, sep="\t",eol = "\n", na = "NA", dec = ".", row.names = TRUE,col.names = TRUE)
Observed OTU多样性箱线图
各组间的统计结果如下:主要看最后一列p adj(Adjust P-value)是否显著,本文数据不显著
diff lwr upr p adj OE-KO -7.52380952380952 -24.480725165752 9.43310611813294 0.515429907536906 WT-KO -6.11111111111111 -21.9728532782553 9.75063105603303 0.604309699204896 WT-OE 1.4126984126984 -15.5442172292441 18.3696140546409 0.976169656924344
时间: 2024-12-20 03:01:30