Figure 1 Top 50 median abundance

Load ampvis

library(ampvis)

MiDAS data

data(MiDAS_1.20)

Subset to top 20 plants

top20 <- as.data.frame(as.matrix(sample_data(MiDAS_1.20))) %>%
  group_by(Plant) %>%
  summarise(Count = n()) %>% 
  filter(Plant != "Fredericia") %>%
  arrange(desc(Count)) %>%
  filter(row_number() <= 20)

d20 <- subset_samples(MiDAS_1.20, Plant %in% top20$Plant) %>%
   transform_sample_counts(function(x) x / sum(x) * 100)

Median abundance

p <- amp_rabund(data =d20, 
           tax.aggregate = "Genus",
           tax.add = "Phylum", 
           tax.class = "p__Proteobacteria", 
           sort.by = "Median",
           tax.show = 50, 
           scale.seq = 100) +
     scale_y_log10(breaks = c(0.01,0.1, 1, 10)) +
     xlab("") +  
  theme(axis.ticks.length = unit(1, "mm"),
          axis.ticks = element_line(color = "black"),
          text = element_text(size = 14, color = "black"),
          axis.text = element_text(color = "black"), 
          plot.margin = unit(c(0,0,0,0), "mm"),
          legend.key.width = unit(3, "mm"),
          legend.key.height = unit(3, "mm"),
          legend.key = element_blank(),
          panel.grid.minor = element_blank(),
          panel.grid.major.x = element_line(color = "grey90"), 
          panel.background = element_blank(),
          axis.line = element_line(color = "black")
          )

p

## Warning: Removed 29 rows containing non-finite values (stat_boxplot).

Save the plot

ggsave(plot = p, filename = "plots/top50.png", width = 9, height = 12, dpi = 600)

Mediance abundance of top100 OTUs per. sample

t100 <- amp_heatmap(d20, group = c("Plant","Date"), tax.aggregate = "OTU", tax.show = 100, output = "complete", scale.seq = 100)

t100_median <- group_by(t100$data, Group) %>%
  summarise(Total = sum(Abundance)) %>%
  summarise(Count = n(), 
            Mean = mean(Total), 
            Median = median(Total), 
            sd = sd(Total))

print(t100_median,  rownames = F)

## Source: local data frame [1 x 4]
## 
##   Count     Mean   Median       sd
## 1   396 50.44073 50.95951 7.447305

Mediance abundance of top50 Genera per. sample

t50g <- amp_heatmap(d20, group = c("Plant","Date"), tax.aggregate = "Genus", tax.show = 50, output = "complete", scale.seq = 100)

t50g_median <- group_by(t50g$data, Group) %>%
  summarise(Total = sum(Abundance)) %>%
  summarise(Count = n(), 
            Mean = mean(Total), 
            Median = median(Total), 
            sd = sd(Total))

print(t50g_median, rownames = F)

## Source: local data frame [1 x 4]
## 
##   Count     Mean   Median       sd
## 1   396 55.62127 55.76875 6.717296