In thomazbastiaanssen/Volatility: Microbiome-oriented tool to compute degree of compositional change over time

knitr::opts_chunk$set(echo = TRUE)
options(tidyverse.quiet = TRUE)
library(volatility)

Introduction

Volatility refers to the degree of instability (or change over time) in the microbiome. High volatility, ie an unstable microbiome, has been associated with an exaggerated stress response and conditions like IBS.

This library provides a basic framework to calculate volatility for timepoint microbiome data.

If you use this software, please cite our work.

Volatility as a Concept to Understand the Impact of Stress on the Microbiome

Thomaz F.S Bastiaanssen, Anand Gururajan, Marcel van de Wouw, Gerard M Moloney, Nathaniel L Ritz, Caitriona M Long-Smith, Niamh C Wiley, Amy B Murphy, Joshua M Lyte, Fiona Fouhy, Catherine Stanton, Marcus J Claesson, Timothy G Dinan, John F Cryan

Psychoneuroendocrinology, 2021 https://doi.org/10.1016/j.psyneuen.2020.105047

Setup

OK, now let's get started. We'll load a complementary training dataset using data(volatility_data). This loads a curated snippet from the dataset described in more detail here: https://doi.org/10.1016/j.psyneuen.2020.105047

The method presented here is differs only incidentally from the one used there (namely in terms of how zeroes were imputed before CLR-transformation).

#install and load volatility library
#devtools::install_github("thomazbastiaanssen/volatility")
library(volatility)

#load tidyverse to wrangle and plot results.
library(tidyverse)

#load example data + metadata from the volatility study.
data(volatility_data)

Input data

In order to compute volatility, we need a feature (count) table, which contains our microbiome data and a metadata object, which denotes at least which samples should be paired.

vola_genus_table[4:10,1:2]

• metadata, a vector in the same order as the count table, denoting which samples are from the same source.
  • The column ID in vola_metadata is appropriate for this.

head(vola_metadata)

Data preparation

Before we compute pairwise distances between samples, it's a good idea to transform microbial count data, to help deal with the compositional nature. Here, we will use a CLR transformation.

#CLR-transform
vola_genus_table <- clr_c(vola_genus_table)

#Compute distance
vola.dist <- dist(t(vola_genus_table))

Basic use

vola_out <- get_pairwise_distance(x = vola.dist, metadata = vola_metadata, g = "ID")

The output of the main volatility function is a data.frame with two columns. ID corresponds to the pairs of samples passed on in the metadata argument, whereas volatility shows the measured volatility between those samples in terms of Aitchison distance (Euclidean distance of CLR-transformed counts).

Plot the results

vola_out %>%

  #Pipe into ggplot
  ggplot() +

  #Define aesthetics
  aes(x = treatment, y = dist, fill = treatment) + 

  #Define geoms, boxplots overlayed with data points in this case
  geom_boxplot(alpha = 1/2)+
  geom_point(shape = 21) +

  #Split the plot by cohort
  facet_wrap(~cohort) +

  #Tweak appearance 
  scale_fill_manual(values = c("Control" = "#3690c0", "Stress"  = "#cb181d")) +
  theme_bw() +
  xlab("") +
  ylab("Volatility (Aitchison distance)")

thomazbastiaanssen/Volatility documentation built on July 13, 2024, 9:09 a.m.