R可视化：使用R的circlize包绘制样本-物种丰度关联弦状图

作者: 生信学习者2 | 来源:发表于2020-11-01 14:25 被阅读0次

R作图-使用circlize绘制样本物种丰度关联弦状图

circlize图也即是弦图能较好展示样本物种丰度关联关系，先前从lyao222lll博客下载过该教程，但是最近刘老师关闭了博客，现有链接无法访问。根据刘老师的教程，只需要替换对应的文本即可以画出该图。更多知识分享请到 https://zouhua.top/。

文件准备

首先准备3个表格，分别为：

物种分类信息文件（phylum_count.csv）。第一列为物种ID（此处为OTU，在circos图的中间区域展示），第二列为OTU的“门水平”分类信息（用于绘制circos外圈OTU分类），第三列为OTU分类详细信息（用于在图例中展示）。此外在后面的绘图过程中，会使用该文件确定OTU的排列顺序，所以请事先根据想要展示的顺序给该文件中的OTU进行排序。
样本分组信息文件（phenotype.csv）。第一列为样本ID（此处为Case1、Control2等共计6个样本，在circos图的中间区域展示），第二列为样本分组信息（用于绘制circos外圈样本分组，此处共计2个分组Case、Control）。此外在后面的绘图过程中，会使用该文件确定样本的排列顺序，所以请事先根据想要展示的顺序给该文件中的样本进行排序。
物种丰度表格文件（family_count.csv）。每一列为样本，每一行为物种（此处为OTU），交叉区为个OTU在各样本中的丰度（用于计算）。因OTU及样本的绘图顺序由前述2个文件确定，所以在该文件中无需纠结样本或OTU的排序。

load data

phen <- read.csv("phenotype.csv")
phylum <- read.csv("phylum_count.csv") %>%
  column_to_rownames("tax")
family <- read.csv("family_count.csv") %>%
  column_to_rownames("tax")

head(phen)

##   SampleID Group
## 1    Case1  Case
## 2    Case2  Case
## 3    Case3  Case
## 4    Case4  Case
## 5    Case5  Case
## 6    Case6  Case

head(phylum)

##                    Case1 Case2 Case3 Case4 Case5 Case6 Control1 Control2 Control3 Control4 Control5 Control6
## p_Actinobacteriota   557   361   250   323   365   809      176      195      175      357      311      275
## p_Bacteroidota      9672 21555 24904 32516 13872 11036    31353    33061    33657    33529    15822    30801
## p_Campilobacterota   111   438    48   177   325    89      888      363      161      341      139      176
## p_Cyanobacteria       34    30   201    15    34     6       22       13       48       32      104       36
## p_Deferribacterota     0     5     0     0     0     0        0        0        0        7        8        0
## p_Desulfobacterota   103   231    87   130    90    99      168      679      287      255      224      310

head(family)

##                                                                              Case1 Case2 Case3 Case4 Case5 Case6 Control1
## p_Actinobacteriota.c_Actinobacteria.o_Bifidobacteriales.f_Bifidobacteriaceae     6     0    30    30     0     0        2
## p_Actinobacteriota.c_Actinobacteria.o_Micrococcales.f_Micrococcaceae             0     3     0     0     4     7        0
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Atopobiaceae          109    12    25    23    72    21       39
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Coriobacteriaceae      92    27    10    35    41   161       55
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Eggerthellaceae       254   306   185   139   196   605       61
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_uncultured             96    13     0    96    50     0       19
##                                                                              Control2 Control3 Control4 Control5 Control6
## p_Actinobacteriota.c_Actinobacteria.o_Bifidobacteriales.f_Bifidobacteriaceae        3        6        0       11        6
## p_Actinobacteriota.c_Actinobacteria.o_Micrococcales.f_Micrococcaceae                0        0        0        0        0
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Atopobiaceae              27       14       35       50       22
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Coriobacteriaceae         15       45      125       70       50
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_Eggerthellaceae          133       75      162      160      169
## p_Actinobacteriota.c_Coriobacteriia.o_Coriobacteriales.f_uncultured                17       35       35       20       28

get 5 most abundant phylum

num <- 5
merge_phy <- inner_join(phen %>% select(SampleID, Group),
                   phylum %>% t() %>% 
                        data.frame() %>%
                        rownames_to_column("SampleID"),
                  by = "SampleID") 
merge_phy_top <-  merge_phy %>%
      select(-c("SampleID", "Group")) %>%
      summarise_each(mean) %>%
      tidyr::gather(key="tax", value="value") %>%
      arrange(desc(value)) %>%
      slice(c(1:num)) %>%
      mutate(tax=as.character(tax))
head(merge_phy_top)

##                  tax      value
## 1       p_Firmicutes 39684.5000
## 2     p_Bacteroidota 24314.8333
## 3    p_Spirochaetota  4497.5833
## 4   p_Proteobacteria   950.1667
## 5 p_Actinobacteriota   346.1667

group information

group_info <- phen %>% select(SampleID, Group) 
head(group_info)

##   SampleID Group
## 1    Case1  Case
## 2    Case2  Case
## 3    Case3  Case
## 4    Case4  Case
## 5    Case5  Case
## 6    Case6  Case

function: get profile and circos

### get tax: taxname;phylum;detail and tax profile(rowname:tax, colnames:sampleid)
get_tax_prf <- function(prof=family, num=20, class=4){
  
  prof=family
  num=20
  class=4
  
  prf <- prof %>% rownames_to_column("tax") %>%
    group_by(tax) %>%
    mutate(phylum=unlist(strsplit(tax, ".", fixed = TRUE))[1],
           otu=unlist(strsplit(tax, ".", fixed = TRUE))[class]) %>%
    ungroup()
  
  prf_cln <- prf %>% filter(phylum%in%merge_phy_top$tax) %>%
              slice(-grep("uncultured", otu)) #%>%
              # slice(-grep("_$", otu))
  
  mdat <- inner_join(phen %>% select(SampleID, Group),
                          prf_cln %>% select(-c("tax", "phylum")) %>%
                             column_to_rownames("otu") %>%
                             t() %>% data.frame() %>%
                             rownames_to_column("SampleID"),
                          by = "SampleID") 
  
  mdat_mean <-  mdat %>%
        select(-c("SampleID", "Group")) %>%
        summarise_each(mean) %>%
        tidyr::gather(key="tax", value="value") %>%
        arrange(desc(value)) %>%
        slice(c(1:num)) %>%
        mutate(tax=as.character(tax))
  
  prf_final <- prf_cln %>% filter(otu%in%mdat_mean$tax) %>%
    mutate()
  
  otu_table <- prf_final %>% select(-c("tax", "phylum")) %>%
    rename("OTU_ID"="otu") %>%
    mutate(OTU_ID=factor(OTU_ID, levels = mdat_mean$tax))
  
  tax_table <- prf_final %>% select(c("tax", "phylum", "otu")) %>%
    setNames(c("detail", "phylum", "OTU_ID")) %>%
    mutate(detail=gsub("\\.", ";", detail)) %>%
    mutate(phylum=factor(phylum, levels = merge_phy_top$tax))
  
  res <- list(otutab=otu_table,
              taxtab=tax_table)
  return(res)
}

### circos
circos_fun <- function(mdat_table=family_table, tag="family"){
  
  # mdat_table=family_table
  # tag="family"
  
  taxonomy <- mdat_table$taxtab
  otu_table <- mdat_table$otutab
  tax_phylum <- unique(taxonomy$phylum)
  all_group <- unique(group_info$Group)
  # merge table 
  otutab <- inner_join(taxonomy, otu_table, by="OTU_ID") %>%
    arrange(phylum, OTU_ID) %>%
    column_to_rownames("OTU_ID") %>%
    select(group_info$SampleID)
  
  # extra parameters 
  all_otu <- rownames(otutab)
  all_sample <- group_info$SampleID
  all_ID <- c(all_otu, all_sample)
  accum_otu <- rowSums(otutab)
  accum_sample <- colSums(otutab)
  all_ID_xlim <- cbind(rep(0, length(all_ID)), data.frame(c(accum_otu, accum_sample)))
  
  # inter parameters 
  otutab$otu_ID <- all_otu
  plot_data <- otutab %>% tidyr::gather(key="SampleID", value="value", -"otu_ID") %>%
    mutate(otu_ID=factor(otu_ID, levels = all_otu),
           SampleID=factor(SampleID, levels = all_sample)) %>%
    arrange(otu_ID, SampleID)

  plot_data <- plot_data[c(2, 1, 3, 3)]
  
  names(color_otu) <- all_otu
  names(color_sample) <- all_sample
  
  ### output PDF file
  name <- paste0(tag, "_circlize_plot.pdf")
  pdf(name, width = 20, height = 8)
  circle_size = unit(1, "snpc")
  
  gap_size <- c(rep(3, length(all_otu) - 1), 6, rep(3, length(all_sample) - 1), 6)
  circos.par(cell.padding = c(0, 0, 0, 0), start.degree = 270, gap.degree = gap_size)
  circos.initialize(factors = factor(all_ID, levels = all_ID), xlim = all_ID_xlim)
  
  circos.trackPlotRegion(
    ylim = c(0, 1), track.height = 0.03, bg.border = NA, 
    panel.fun = function(x, y) {
        sector.index = get.cell.meta.data("sector.index")
        xlim = get.cell.meta.data("xlim")
        ylim = get.cell.meta.data("ylim")
    } )
  
  for (i in 1:length(tax_phylum)) {
    tax_OTU <- {subset(taxonomy, phylum == tax_phylum[i])}$OTU_ID
    highlight.sector(tax_OTU, track.index = 1, col = color_phylum[i], 
                     text = tax_phylum[i], cex = 0.5, text.col = "black",
                     niceFacing = FALSE)
  }
  
  for (i in 1:length(all_group)) {
    group_sample <- {subset(group_info, Group == all_group[i])}$SampleID
    highlight.sector(group_sample, track.index = 1, col = color_group[i], 
                     text = all_group[i], cex = 0.7, text.col = 'black', 
                     niceFacing = FALSE)
  }
  

  circos.trackPlotRegion(
    ylim = c(0, 1), track.height = 0.05, bg.border = NA, 
    panel.fun = function(x, y) {
        sector.index = get.cell.meta.data('sector.index')
        xlim = get.cell.meta.data('xlim')
        ylim = get.cell.meta.data('ylim')
    } )
  
  circos.track(
    track.index = 2, bg.border = NA, 
    panel.fun = function(x, y) {
        xlim = get.cell.meta.data('xlim')
        ylim = get.cell.meta.data('ylim')
        sector.name = get.cell.meta.data('sector.index')
        xplot = get.cell.meta.data('xplot')
        
        by = ifelse(abs(xplot[2] - xplot[1]) > 30, 0.25, 1)
        for (p in c(0, seq(by, 1, by = by))) circos.text(p*(xlim[2] - xlim[1]) + xlim[1], mean(ylim) + 0.4, paste0(p*100, '%'), cex = 0.4, adj = c(0.5, 0), niceFacing = FALSE)
        
        circos.lines(xlim, c(mean(ylim), mean(ylim)), lty = 3)
    } )
  
  circos.trackPlotRegion(
    ylim = c(0, 1), track.height = 0.03, 
    bg.col = c(color_otu, color_sample), 
    bg.border = NA, track.margin = c(0, 0.01),
    panel.fun = function(x, y) {
        xlim = get.cell.meta.data('xlim')
        sector.name = get.cell.meta.data('sector.index')
        circos.axis(h = 'top', labels.cex = 0.4, 
                    major.tick.percentage = 0.4, labels.niceFacing = FALSE)
        circos.text(mean(xlim), 0.2, sector.name, cex = 0.4, 
                    niceFacing = FALSE, adj = c(0.5, 0))
    } )
  
  circos.trackPlotRegion(ylim = c(0, 1), track.height = 0.03, track.margin = c(0, 0.01))
  
  for (i in seq_len(nrow(plot_data))) {
    circos.link(
        plot_data[i,2], c(accum_otu[plot_data[i,2]], 
                          accum_otu[plot_data[i,2]] - plot_data[i,4]),
        plot_data[i,1], c(accum_sample[plot_data[i,1]], 
                          accum_sample[plot_data[i,1]] - plot_data[i,3]),
        col = paste0(color_otu[plot_data[i,2]], '70'), border = NA )
    
    circos.rect(accum_otu[plot_data[i,2]], 0, 
                accum_otu[plot_data[i,2]] - plot_data[i,4], 1, 
                sector.index = plot_data[i,2], 
                col = color_sample[plot_data[i,1]], border = NA)
    circos.rect(accum_sample[plot_data[i,1]], 0, 
                accum_sample[plot_data[i,1]] - plot_data[i,3], 1, 
                sector.index = plot_data[i,1], 
                col = color_otu[plot_data[i,2]], border = NA)
    
    accum_otu[plot_data[i,2]] = accum_otu[plot_data[i,2]] - plot_data[i,4]
    accum_sample[plot_data[i,1]] = accum_sample[plot_data[i,1]] - plot_data[i,3]
  }
  
  otu_legend <- Legend(
        at = all_otu, labels = taxonomy$detail, labels_gp = gpar(fontsize = 8),    
        grid_height = unit(0.5, 'cm'), grid_width = unit(0.5, 'cm'), 
        type = 'points', pch = NA, background = color_otu)
  
  pushViewport(viewport(x = 0.85, y = 0.5))
  grid.draw(otu_legend)
  upViewport()
        
  circos.clear()
  dev.off()
}

family levels circos

family_table <- get_tax_prf(prof = family, num = 15, class=4)
circos_fun(mdat_table = family_table, tag = "family")

该circos图展示了21种OTU在12个样本中的丰度关系。其中，21种OTU可划分为3个门，12个样本可划分为2个分组。

左侧为circlize做图结果，分为5圈。
第一圈，各OTU的门水平分类以及各样本的分组信息；
第二圈，OTU相对丰度的百分比信息；
第三圈，OTU及样本主区块，以不同颜色和标签区分，区块外周的刻度为OTU的绝对丰度信息；
第四圈，OTU及样本副区块，与主区块（第三圈）对应，展示了各OTU在各样本中的丰度，以及各样本所含每种OTU的丰度信息；
第五圈，与OTU及样本副区块（第四圈）相对应，连线展示OTU、样本关联信息。

version

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    
## system code page: 936
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ComplexHeatmap_2.4.3 circlize_0.4.10      tibble_3.0.3         dplyr_1.0.0         
## 
## loaded via a namespace (and not attached):
##  [1] pillar_1.4.6        compiler_4.0.2      RColorBrewer_1.1-2  tools_4.0.2         digest_0.6.25       jsonlite_1.7.1     
##  [7] evaluate_0.14       lifecycle_0.2.0     clue_0.3-57         pkgconfig_2.0.3     png_0.1-7           rlang_0.4.7        
## [13] cli_2.1.0           rstudioapi_0.11     yaml_2.2.1          parallel_4.0.2      xfun_0.18           stringr_1.4.0      
## [19] knitr_1.30          cluster_2.1.0       generics_0.0.2      GlobalOptions_0.1.2 vctrs_0.3.2         tidyselect_1.1.0   
## [25] glue_1.4.1          R6_2.5.0            GetoptLong_1.0.4    fansi_0.4.1         rmarkdown_2.5       tidyr_1.1.2        
## [31] purrr_0.3.4         magrittr_1.5        ellipsis_0.3.1      htmltools_0.5.0     assertthat_0.2.1    shape_1.4.5        
## [37] colorspace_1.4-1    utf8_1.1.4          stringi_1.5.3       crayon_1.3.4        rjson_0.2.20

引用

作图-使用circlize绘制样本物种丰度关联弦状图

参考文章如引起任何侵权问题，可以与我联系，谢谢。

R可视化：使用R的circlize包绘制样本-物种丰度关联弦状图

R作图-使用circlize绘制样本物种丰度关联弦状图

文件准备

load data

get 5 most abundant phylum

group information

function: get profile and circos

family levels circos

version

引用

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