R语言~处理FT ICR MS质谱数据

使用R包处理傅立叶变换离子回旋共振质谱数据！本示例数据基于软件Formularity (Tolić et al, 2017 - Anal. Chem.)生成的报告文件

加载工具包

#devtools::install_github("EMSL-Computing/ftmsRanalysis")
#devtools::install_github("clauswilke/ggisoband")
# package found at https://github.com/EMSL-Computing/ftmsRanalysis
suppressPackageStartupMessages(require(ftmsRanalysis)) 
suppressPackageStartupMessages(require(reshape2))
suppressPackageStartupMessages(require(ggpubr))
suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(RColorBrewer))
suppressPackageStartupMessages(require(scales))
suppressPackageStartupMessages(require(ggisoband))

将质谱峰的化合物预测信息与丰度信息拆分开

report = read.csv('../00_data/TXT/Report.csv')
head(report)
emeta = report[, c("Mass", "C", "H", "O", "N", "C13", "S", "P", "Na", "El_comp", "Class", "NeutralMass", "Error_ppm", "Candidates")]
edata = report[,-which(colnames(report) %in% names(emeta)[-1])]

#导入样本分组信息#
fdata = read.table('./map.txt', header = T)

#格式转换，类似phyloseq对象#
peak_icr = as.peakData(e_data = edata, f_data = fdata, e_meta = emeta, edata_cname = "Mass", mass_cname = "Mass", fdata_cname = "SampleID", c_cname = "C", h_cname = "H", o_cname = "O", n_cname = "N", s_cname = "S", p_cname = "P")

#计算参数#
peak_icr = compound_calcs(peak_icr)

#化合物注释#
peak_icr = assign_class(peak_icr, boundary_set = "bs1")
peak_icr = assign_class(peak_icr, boundary_set = "bs2")
peak_icr = assign_class(peak_icr, boundary_set = "bs3")

#质控过滤#
filter_obj = mass_filter(peak_icr)
peak_icr = applyFilt(filter_obj, peak_icr, min_mass = 150, max_mass = 900)

#输出结果#
write.csv(peak_icr$e_data, "Processed_MS_Data.csv", quote = F, row.names = F)
write.csv(peak_icr$e_meta, "Processed_MolInfor.csv", quote = F, row.names = F)

初步可视化

#自定义配色方案#
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))

#van Krevelen diagram
vK_plot = ggplot(peak_icr$e_meta, aes(OtoC_ratio,HtoC_ratio,color = bs2_class))+
  geom_point()+
  scale_color_manual(values = col_vector[-4])+
  theme_bw()+
  geom_density_bands(aes(fill = stat(density)), color = "NA", alpha = 0.7, size = 0.2) +
  scale_fill_gradientn(colours = c('white','yellow','red','black')) +
  guides(color = guide_legend(override.aes = list(size = 5)))+
  labs(x = 'O:C',y = 'H:C', color = 'Classes')+
  theme(axis.title = element_text(size = 20,color = 'black'),
        axis.text.y = element_text(size = 15,color = 'black'),
        axis.text.x = element_text(size = 15,color = 'black',angle = 90, vjust = 0.5),
        axis.ticks.length = unit(0.3,'cm'),
        legend.text = element_text(size = 20,color = 'black'),
        legend.title = element_text(size = 20,color = 'black'),
        strip.text = element_text(size = 20,color = 'black'),
        panel.grid = element_blank())

vKplot.jpg

各参数概览

head(emeta)
emeta_sub = peak_icr$e_meta[16:24] %>% unique.data.frame()
emeta_sub = melt(emeta_sub)
emeta_plot = ggplot(emeta_sub, aes(value, ..scaled..,fill = variable)) +
  geom_density(show.legend = FALSE) +
  facet_wrap(.~variable,scales = 'free',nrow = 2) +
  theme_bw()+
  theme(axis.title = element_text(size = 20,color = 'black'),
        axis.text.y = element_text(size = 15,color = 'black'),
        axis.text.x = element_text(size = 15,color = 'black',angle = 90, vjust = 0.5),
        axis.ticks.length = unit(0.3,'cm'),
        strip.text = element_text(size = 20,color = 'black'),
        panel.grid = element_blank())

emata_plot.jpg

对质谱峰化合物信息进行丰度统计，生成类似OTU表和物种分类表

############### Summary generation ############### 
summary = TRUE
if(summary == T){
  # Setting peak_icr objects and row names
  edata = peak_icr$e_data
  emeta = peak_icr$e_meta
  
  row.names(edata) = edata$Mass; edata = edata[,-which(colnames(edata) %in% "Mass")]
  row.names(emeta) = emeta$Mass; emeta = emeta[,-which(colnames(emeta) %in% "Mass")]
  
  #### Compound class summary
  # Finding unique compound classes
  uniq.comp = unique(peak_icr$e_meta$bs1_class)
  
  # Looping through each sample to obtain some summary categoreies
  classes = matrix(nrow = ncol(edata), ncol = length(uniq.comp)) # Creating empty matrix to store stats
  colnames(classes) = uniq.comp
  row.names(classes) = colnames(edata)
  
  name.temp = NULL
  
  for(i in 1:ncol(edata)){
    temp = edata[which(edata[,i] > 0), i, drop = F] # Need to keep names, looking at columns
    temp = emeta[row.names(temp),]
    
    for(j in 1:length(uniq.comp)){
      classes[i,j] = length(which(temp$bs1 %in% uniq.comp[j]))
    }
    
    name.temp = c(name.temp, colnames(edata)[i])
  } # I'm not sure how to do this without the for-loop, but I'm simply just finding the mean/median for peak stats
  
  classes = as.data.frame(classes)
  
  write.csv(classes, "Compound_Class_Summary.csv", quote = F)
  
  
  #### Compound composition summary
  # Finding unique compound composition
  uniq.composition = unique(peak_icr$e_meta$El_comp)
  # Looping through each sample to obtain some summary compositions
  compostion = matrix(nrow = ncol(edata), ncol = length(uniq.composition)) # Creating empty matrix to store stats
  colnames(compostion) = uniq.composition
  row.names(compostion) = colnames(edata)
  
  name.temp = NULL
  
  for(i in 1:ncol(edata)){
    temp = edata[which(edata[,i] > 0), i, drop = F] # Need to keep names, looking at columns
    temp = emeta[row.names(temp),]
    
    for(j in 1:length(uniq.composition)){
      compostion[i,j] = length(which(temp$El_comp %in% uniq.composition[j]))
    }
    
    name.temp = c(name.temp, colnames(edata)[i])
  } # I'm not sure how to do this without the for-loop, but I'm simply just finding the mean/median for peak stats
  
  compostion = as.data.frame(compostion)
  
  write.csv(compostion, "Compound_Compostion_Summary.csv", quote = F)
  
  
  #### Characteristics summary
  # Looping through each sample to obtain some summary stats of the peaks
  characteristics = data.frame(AI.mean = rep(NA, length(colnames(edata))), AI.median = NA, AI.sd = NA,
                               AI_Mod.mean = NA, AI_Mod.median = NA, AI_Mod.sd = NA,
                               DBE.mean = NA, DBE.median = NA, DBE.sd = NA,
                               DBE_O.mean = NA, DBE_O.median = NA, DBE_O.sd = NA,
                               KenMass.mean = NA, KenMass.median = NA, KenMass.sd = NA,
                               KenDef.mean = NA, KenDef.median = NA, KenDef.sd = NA,
                               NOSC.mean = NA, NOSC.median = NA, NOSC.sd = NA,
                               Gibbs.mean = NA, Gibbs.median = NA, Gibbs.sd = NA, 
                               row.names = colnames(edata))
  
  for(i in 1:ncol(edata)){
    temp = edata[which(edata[,i] > 0), i, drop = F] # Need to keep names, looking at columns
    temp = emeta[row.names(temp),]
    
    # AI
    characteristics$AI.mean[i] = mean(temp$AI, na.rm = T)
    characteristics$AI.median[i] = median(temp$AI, na.rm = T)
    characteristics$AI.sd[i] = sd(temp$AI, na.rm = T)
    
    # AI_Mod
    characteristics$AI_Mod.mean[i] = mean(temp$AI_Mod, na.rm = T)
    characteristics$AI_Mod.median[i] = median(temp$AI_Mod, na.rm = T)
    characteristics$AI_Mod.sd[i] = sd(temp$AI_Mod, na.rm = T)
    
    # DBE
    characteristics$DBE.mean[i] = mean(temp$DBE, na.rm = T)
    characteristics$DBE.median[i] = median(temp$DBE, na.rm = T)
    characteristics$DBE.sd[i] = sd(temp$DBE, na.rm = T)
    
    # DBE-O
    characteristics$DBE_O.mean[i] = mean(temp$DBE_O, na.rm = T)
    characteristics$DBE_O.median[i] = median(temp$DBE_O, na.rm = T)
    characteristics$DBE_O.sd[i] = sd(temp$DBE_O, na.rm = T)
    
    # Kendrick Mass
    characteristics$KenMass.mean[i] = mean(temp$kmass, na.rm = T)
    characteristics$KenMass.median[i] = median(temp$kmass, na.rm = T)
    characteristics$KenMass.sd[i] = sd(temp$kmass, na.rm = T)
    
    # Kendrick Defect
    characteristics$KenDef.mean[i] = mean(temp$kdefect, na.rm = T)
    characteristics$KenDef.median[i] = median(temp$kdefect, na.rm = T)
    characteristics$KenDef.sd[i] = sd(temp$kdefect, na.rm = T)
    
    # NOSC
    characteristics$NOSC.mean[i] = mean(temp$NOSC, na.rm = T)
    characteristics$NOSC.median[i] = median(temp$NOSC, na.rm = T)
    characteristics$NOSC.sd[i] = sd(temp$NOSC, na.rm = T)
    
    # Gibbs Free Energy
    characteristics$Gibbs.mean[i] = mean(temp$GFE, na.rm = T)
    characteristics$Gibbs.median[i] = median(temp$GFE, na.rm = T)
    characteristics$Gibbs.sd[i] = sd(temp$GFE, na.rm = T)
    
  } # I'm not sure how to do this without the for-loop, but I'm simply just finding the mean/median for peak stats
  
  write.csv(characteristics, "MolInfo_Summary.csv", quote = F)
  
  rm(i,j,name.temp,summary,uniq.comp,temp)
}

自定义函数绘制累积图

cal_avg_abundance = function(dat,relative,legend.position){
  
  if(relative == T){
    dat = dat/rowSums(dat)
  }
  
  df = as.data.frame(t(dat))
  
  df$taxa = rownames(df)
  
  df = df[order(df$taxa,decreasing = F),c('taxa',as.character(rownames(classes)))] 
  
  
  link_dat <- df %>% 
    mutate_if(is.numeric, cumsum) %>%
    as.data.frame()
  
  bar.width <- 0.7
  
  link_dat <- link_dat[, c(1:2,rep(3:(ncol(link_dat)-1),each=2),ncol(link_dat))]
  
  link_dat <- data.frame(y=t(matrix(t(link_dat[,-1]), nrow=2)))
  link_dat$x.1 <- 1:(ncol(df)-2) + bar.width/2
  link_dat$x.2 <- 1:(ncol(df)-2) + (1-bar.width/2)
  
  plot_df = melt(df)
  plot_df$taxa = as.factor(plot_df$taxa)

  plot_df$taxa = factor(plot_df$taxa, levels = rev(levels(plot_df$taxa)))
  plot_df$variable = factor(plot_df$variable, levels = levels(plot_df$variable))
  # 
  ifelse(relative == T,
         p <- ggplot(data = plot_df, aes(x = variable, y = value, fill = taxa)) + 
           
           theme_classic()+
           
           labs(x= NULL,y ='Relative Abundance',fill = NULL) +
           guides(fill=guide_legend(nrow=5)) +
           
           geom_bar(stat = "identity", width=bar.width, col='black')  + 
           # 
           geom_segment(data = link_dat, aes(x = x.1, xend = x.2, y = y.1, yend = y.2), inherit.aes = F) +
           
           scale_y_continuous(labels = percent,expand = c(0,0)) +  
           scale_fill_manual(values = col_vector) +
           
           theme(axis.title = element_text(size = 20,color = 'black'),
                 axis.text.y = element_text(size = 15,color = 'black'),
                 axis.text.x = element_text(size = 15,color = 'black',angle = 90, vjust = 0.5),
                 axis.ticks.length = unit(0.3,'cm'),
                 legend.text = element_text(size = 20),
                 # legend.title = element_text(size = 10),
                 legend.position = legend.position,
                 panel.grid = element_blank(),
                 panel.border = element_rect(fill = "transparent", color = "transparent"),
                 plot.background = element_rect(fill = "transparent", color = "transparent"),
                 panel.background = element_rect(fill = "transparent", color = "transparent")),
         
         p <- ggplot(data = plot_df, aes(x = variable, y = value, fill = taxa)) + 
           
           theme_classic(）+
           labs(x= NULL,y ='Absolute Abundance',fill = NULL) +
           guides(fill=guide_legend(nrow=5)) +
           geom_bar(stat = "identity", width=bar.width, col='black')  + 
           geom_segment(data = link_dat, aes(x = x.1, xend = x.2, y = y.1, yend = y.2), inherit.aes = F) +
           scale_y_continuous(expand = c(0,0)) + 
           scale_fill_manual(values = col_vector) +
           
           theme(axis.title = element_text(size = 20,color = 'black'),
                 axis.text.y = element_text(size = 15,color = 'black'),
                 axis.text.x = element_text(size = 15,color = 'black',angle = 90, vjust = 0.5),
                 axis.ticks.length = unit(0.3,'cm'),
                 legend.text = element_text(size = 20),
                 # legend.title = element_text(size = 10),
                 legend.position = legend.position,
                 panel.grid = element_blank(),
                 panel.border = element_rect(fill = "transparent", color = "transparent"),
                 plot.background = element_rect(fill = "transparent", color = "transparent"),
                 panel.background = element_rect(fill = "transparent", color = "transparent")))

  return(p)
}

预测化合物的累积图

ggsave(cal_avg_abundance(classes,relative = TRUE,legend.position = 'bottom'),filename = 'classes_relative.jpg', width = 15,height = 10,dpi = 300)
ggsave(cal_avg_abundance(classes,relative = FALSE,legend.position = 'bottom'),filename = 'classes_absolute.jpg', width = 15,height = 10,dpi = 300)

classes_absolute.jpg

classes_relative.jpg

预测元素组成的累积图

ggsave(cal_avg_abundance(compostion,relative = TRUE,legend.position = 'right'),filename = 'compostion_relative.jpg', width = 15,height = 10,dpi = 300)
ggsave(cal_avg_abundance(compostion,relative = 
FALSE,legend.position = 'right'),filename = 'compostion_absolute.jpg', width = 15,height = 10,dpi = 300)

compostion_absolute.jpg

compostion_relative.jpg