R/tax_circlize.R

In microbiota/amplicon: Statistics and visualization for microbiome data

# 样本或组的物种组成弦图 Circlize of taxonomy for samples and groups
#
# This is the function named 'tax_circlize'
# which draw circle, and return a circlize object
#
#' @title Plotting circlize of taxonomy for groups or samples
#' @description Input taxonomy composition, and metadata (SampleID and groupID). Then select top N high abundance taxonomy and group other low abundance. When Select samples can draw sample composition by facet groups. If used group can show mean of each group. Finally, return a ggplot2 object.
#' @param tax_sum composition matrix, like OTU table and rowname is taxonomy, typical output of usearch -sintax_summary;
#' @param metadata matrix or dataframe, including sampleID and groupID;
#' @param topN Top N taxonomy to show, default 8, alternative 4, 6, 10 ...;
#' @param groupID column name for groupID;
#' @param style group or sample, default group
#' @param sorted Legend sorted type, default abundance, alternative alphabet
#' @details
#' By default, returns top 8 taxonomy and group mean stackplot
#' The available style include the following:
#' \itemize{
#' \item{group: group mean circlize}
#' \item{sample: each sample circlize}
#' }
#' @return ggplot2 object.
#' @author Contact: Yong-Xin Liu \email{metagenome@@126.com}
#' @references
#'
#' Yong-Xin Liu, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo & Yang Bai.
#' A practical guide to amplicon and metagenomic analysis of microbiome data.
#' Protein Cell, 2020, DOI: \url{https://doi.org/10.1007/s13238-020-00724-8}
#'
#' Jingying Zhang, Yong-Xin Liu, Na Zhang, Bin Hu, Tao Jin, Haoran Xu, Yuan Qin, Pengxu Yan, Xiaoning Zhang, Xiaoxuan Guo, Jing Hui, Shouyun Cao, Xin Wang, Chao Wang, Hui Wang, Baoyuan Qu, Guangyi Fan, Lixing Yuan, Ruben Garrido-Oter, Chengcai Chu & Yang Bai.
#' NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice.
#' Nature Biotechnology, 2019(37), 6:676-684, DOI: \url{https://doi.org/10.1038/s41587-019-0104-4}
#'
#' @seealso tax_circlize
#' @examples
#' # Taxonomy table in phylum level, rownames is Phylum, colnames is SampleID
#' data(tax_phylum)
#' # metadata, include SampleID, Group and Site
#' data(metadata)
#' # Set 4 parameters: set top 5 taxonomy, group by "Group"
#' tax_circlize(tax_sum = tax_phylum, metadata, topN = 5, groupID = "Group")
#' @export
tax_circlize <- function(tax_sum, metadata, topN = 5, groupID = "Group") {

  # 依赖关系检测与安装
  p_list = c("ggplot2", "reshape2", "circlize")
  for(p in p_list){
    if (!requireNamespace(p)){install.packages(p)}
    suppressPackageStartupMessages(library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE))
  }

  # 测试默认参数
  # library(amplicon)
  # tax_sum = tax_phylum
  # topN = 5
  # groupID = "Group"

  # 交叉筛选
  idx = rownames(metadata) %in% colnames(tax_sum)
  metadata = metadata[idx,,drop=F]
  tax_sum = tax_sum[, rownames(metadata)]

  # 提取样品组信息,默认为group可指定
  sampFile = as.data.frame(metadata[, groupID],row.names = row.names(metadata))
  colnames(sampFile)[1] = "group"

#----按丰度降序排序#----
  mean_sort = as.data.frame(tax_sum[(order(-rowSums(tax_sum))), ])
  # 筛选前N类，其它归为Other，可设置不同组数
  other = colSums(mean_sort[topN:dim(mean_sort)[1], ])
  mean_sort = mean_sort[1:(topN - 1), ]
  mean_sort = rbind(mean_sort,other)
  rownames(mean_sort)[topN] = c("Other")
  # 保存变量备份，并输出至文件
  merge_tax=mean_sort

#----按组合并求均值#----

    # 转置样品名添加组名，并去除多余的两个样品列
    mat_t = t(merge_tax)
    mat_t2 = merge(sampFile, mat_t, by="row.names")
    mat_t2 = mat_t2[,c(-1)]

    # 按组求均值，转置，再添加列名
    mat_mean = aggregate(mat_t2[,-1], by=mat_t2[1], FUN=mean) # mean
    # df = do.call(rbind, mat_mean)[-1,]
    df = t(mat_mean[,-1])
    geno = mat_mean$group
    colnames(df) = mat_mean$group

#----默认参数绘图-颜色随机#----

    # 设置图片文件名、长宽和字体大小
    pdf(file="circlize.pdf", width=89/25.4, height=89/25.4, pointsize=8)
    # 上方绘图和图例代码
    chordDiagram(df)
    # 绘图结束后写入文件
    dev.off()

#----指定颜色绘图+图例#----
    # 颜色设定
    library(RColorBrewer)
    grid.col = NULL
    # 分类学颜色，最多12种
    grid.col[rownames(df)] = brewer.pal(dim(df)[1], "Set1")
    # 定义分组颜色
    grid.col[colnames(df)] = brewer.pal(dim(df)[2], "Accent")

    pdf(file="circlize_legend.pdf", width=183/25.4, height=89/25.4, pointsize=8)
    # 上方绘图和图例代码
    chordDiagram(df, directional = TRUE,diffHeight = 0.03, grid.col = grid.col, transparency = 0.5)
    # 添加行-物种图例
    legend("left",pch=20,legend=rownames(df),col=grid.col[rownames(df)],bty="n",cex=1,pt.cex=3,border="black")
    # 添加列-分组图例
    legend("right",pch=20,legend=colnames(df),col=grid.col[colnames(df)],bty="n",cex=1,pt.cex=3,border="black")
    # 绘图结束后写入文件
    dev.off()
}
# 输出结果存在AI中编辑缺失字体问题？AdobePiStd