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

The main volatility function does all the heavy lifting here. It expects two objects:

• counts, a microbiome feature count table, with columns as samples and rows and features.
  • The vola_genus_table object is an example of an appropriately formatted count table.

vola_genus_table[4:10,1:2]

##                               Validation_Pre_Control_1 Validation_Pre_Control_2
## Atopobiaceae_Olsenella                               0                        0
## Coriobacteriaceae_Collinsella                        0                        0
## Eggerthellaceae_DNF00809                           102                       47
## Eggerthellaceae_Enterorhabdus                       53                      114
## Eggerthellaceae_Parvibacter                         21                       20
## Bacteroidaceae_Bacteroides                         616                      453
## Marinifilaceae_Odoribacter                         780                      915

• 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)

##                  sample_ID     cohort timepoint treatment ID
## 1 Validation_Pre_Control_1 Validation       Pre   Control  1
## 2 Validation_Pre_Control_2 Validation       Pre   Control  2
## 3 Validation_Pre_Control_3 Validation       Pre   Control  3
## 4 Validation_Pre_Control_4 Validation       Pre   Control  4
## 5 Validation_Pre_Control_5 Validation       Pre   Control  5
## 6 Validation_Pre_Control_6 Validation       Pre   Control  6

Basic use

vola_out <- volatility(counts = vola_genus_table, metadata = vola_metadata$ID)

head(vola_out)

##   ID volatility
## 1  1  14.146575
## 2 10  15.753087
## 3 11  16.299301
## 4 13  16.311881
## 5 14   8.803906
## 6 15   9.784708

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 %>%
  #Merge the volatilty output with the rest of the initial metadata using the shared "ID" column
  left_join(vola_metadata[vola_metadata$timepoint == "Pre",], "ID") %>%
  
  #Pipe into ggplot
  ggplot(aes(x = treatment, y = volatility, 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)")