In eckartbindewald/bfxapps2: R Bioinformatics Applications

knitr::opts_chunk$set(echo = TRUE)

Computing sample diversity

# install.packages("corrplot")
library(corrplot)
library(vegan)
remote_dir="https://raw.githubusercontent.com/PacktPublishing/R-Bioinformatics-Cookbook/master"
biom_file <- file.path(remote_dir, "datasets", "ch5", "rich_high_count_otu.biom")
taxdata <- metacoder::parse_qiime_biom(biom_file)
otu_table <- taxdata$data$otu_table[, paste0("Sample", 1:6)]
# A tibble: 5 x 6
#Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
#1      24   15010   22426   20482    4574   13424
#2     872       0   16811    5174    4280    8042
#3       0       7     100      50      25      19
#4     875   16654   31109    2410       0   13815
#5    1270   12857    7267    3675    4159       0

alpha_diversity <- diversity(otu_table, MARGIN=2, index="simpson")
#  Sample1   Sample2   Sample3   Sample4   Sample5   Sample6 
#0.6605111 0.6631140 0.7009386 0.5393156 0.6674042 0.6503205 

Bar Plot

barplot(alpha_diversity)

between_sample <- vegdist(t(otu_table), index = "bray")
between_sample_m <- as.matrix(between_sample, ncol = 6)
#Sample1   Sample2   Sample3   Sample4   Sample5   Sample6
#Sample1 0.0000000 0.9088062 0.9246848 0.8253904 0.7305803 0.9076185
#Sample2 0.9088062 0.0000000 0.3629306 0.4470053 0.6963485 0.3173824
#Sample3 0.9246848 0.3629306 0.0000000 0.4193637 0.7126643 0.3752931
#Sample4 0.8253904 0.4470053 0.4193637 0.0000000 0.4399161 0.3731797
#Sample5 0.7305803 0.6963485 0.7126643 0.4399161 0.0000000 0.6328768
#Sample6 0.9076185 0.3173824 0.3752931 0.3731797 0.6328768 0.0000000

Visualization

corrplot::corrplot(between_sample_m, method="circle", type = "upper", diag = FALSE )

eckartbindewald/bfxapps2 documentation built on Feb. 6, 2025, 3:22 a.m.