Distinguishing socioeconomic and racial/ethnic variation of the human gut microbiome by re-analysis of studies from around the world

Ifeanyi Kalu

Objectives of the study

To assess commonality or heterogeneity in socioeconomic and ethnic patterning of the fecal microbiome in studies undertaken in various parts of the world, through cluster analysis of results on differentially abundant taxa.

Data Analysis Plan

  1. creating frequency tables of how many signatures each microbial taxon (species, genus, or other rank) has been reported – by race/ethnicity separately, by SES separately, and for all studies combined.

  2. cluster analysis of the signatures. Similarity between all pairs of signatures will be calculated using Jaccard Distance, to create a distance matrix. This distance matrix will be used for hierarchical clustering, to identify subgroups of similar signatures. This analysis will be performed separately for signatures of increased abundance and of decreased abundance (in low SES and non-white/Caucasian ethnicity) repeated using Semantic Similarity as an alternative measure of signature similarity, in sensitivity analysis.

  3. Common and distinct effects of SES and race/ethnicity on the gut microbiome will be identified using meta-regression for the most identified taxa. Specifically, the signature will be the unit of analysis, with the presence or absence of the taxon in each study coded as a 0/1 binary variable in a regression analysis.

Install Packages

This is not evaluated, run manually if needed.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install()
BiocManager::version()
BiocManager::install("remotes", dependencies = TRUE)
BiocManager::install("waldronlab/bugSigSimple")
BiocManager::install("curatedMetagenomicData")
devtools::install_github("waldronlab/BugSigDBStats")
devtools::install_github("waldronlab/bugsigdbr")

Import data from Bugsigdb database 

dat <- bugsigdbr::importBugSigDB(version = 'devel', cache = FALSE) #11/27/2022
dim(dat)
## [1] 7115   50

Subset Signatures by Curator and Conditions 

dat.sub <- subsetByCurator(dat, "Kaluifeanyi101") %>%
  dplyr::filter(`Body site` == "Feces") %>%
  mutate(`Body site` = tolower(`Body site`)) %>%
  mutate(`Condition` = tolower(`Condition`))
table(dat.sub[,"Condition"])
## 
##                                ethnic group 
##                                          30 
##     ethnic group,gut microbiome measurement 
##                                           1 
## ethnic group,place of residence measurement 
##                                           2 
##           ethnic group,socioeconomic status 
##                                           2 
##                  gut microbiome measurement 
##                                           4 
##         seasonal gut microbiome measurement 
##                                           2 
##                        socioeconomic status 
##                                          23

Table of studies 

bugSigSimple::createStudyTable(dat.sub) %>%
  kbl() %>%
  kable_styling()
Study Condition Cases Controls Study Design
Amaruddin 2020 gut microbiome measurement 74 66 cross-sectional observational, not case-control
Ang 2021 ethnic group 22 24 cross-sectional observational, not case-control
Balakrishnan 2021 ethnic group,gut microbiome measurement 30 30 cross-sectional observational, not case-control
Bowyer 2019 socioeconomic status 342 457 cross-sectional observational, not case-control
Brooks 2018 ethnic group 88 1237 cross-sectional observational, not case-control
Carson 2018 ethnic group 47 33 cross-sectional observational, not case-control
Chong 2015 socioeconomic status 20 24 cross-sectional observational, not case-control
De 2010 ethnic group,place of residence measurement 14 15 cross-sectional observational, not case-control
Dwiyanto 2021 ethnic group 54 65 cross-sectional observational, not case-control
Escobar 2014 ethnic group 30 54 cross-sectional observational, not case-control
Greenhill 2015 socioeconomic status 86 29 cross-sectional observational, not case-control
He 2018 socioeconomic status 7009 NA cross-sectional observational, not case-control
Kortekangas 2020 seasonal gut microbiome measurement NA NA prospective cohort
Lapidot 2021 socioeconomic status 70 69 cross-sectional observational, not case-control
Levin 2016 gut microbiome measurement 168 130 prospective cohort
Lewis 2021 socioeconomic status NA NA cross-sectional observational, not case-control
Lin 2013 ethnic group,socioeconomic status 6 4 cross-sectional observational, not case-control
Mello 2016 socioeconomic status 100 30 cross-sectional observational, not case-control
Miller 2016 socioeconomic status NA 44 cross-sectional observational, not case-control
Ramos 2020 socioeconomic status NA NA cross-sectional observational, not case-control
kowiak 2012 socioeconomic status 44 31 cross-sectional observational, not case-control

Taxon Frequency Table by SES 

dat.sub_SES <- filter(dat.sub, dat.sub$Condition == "socioeconomic status") 
dim(dat.sub_SES)
## [1] 23 50
bugSigSimple::createTaxonTable(dat.sub_SES)  %>%
  kbl() %>%
  kable_styling()
Taxon Name Taxonomic Level total_signatures increased_signatures decreased_signatures Binomial Test pval kingdom phylum class order family genus species n_signatures metaphlan_name
Bacteroidota phylum 4 4 0 0.12 Bacteria Bacteroidota NA NA NA NA NA 16 k__Bacteria|p__Bacteroidota
Prevotella genus 4 1 3 0.63 Bacteria Bacteroidota Bacteroidia Bacteroidales Prevotellaceae Prevotella NA 4 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
Rikenellaceae family 4 3 1 0.63 Bacteria Bacteroidota Bacteroidia Bacteroidales Rikenellaceae NA NA 5 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Rikenellaceae
Eubacterium genus 3 2 1 1.00 Bacteria Bacillota Clostridia Eubacteriales Eubacteriaceae Eubacterium NA 3 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
Ruminococcus genus 3 2 1 1.00 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Ruminococcus NA 3 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
Bacteroides genus 3 1 2 1.00 Bacteria Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides NA 4 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
Deltaproteobacteria class 2 2 0 0.50 Bacteria Deltaproteobacteria NA NA NA NA NA 2 k__Bacteria|c__Deltaproteobacteria
Bacillota phylum 2 2 0 0.50 Bacteria Bacillota NA NA NA NA NA 18 k__Bacteria|p__Bacillota
Eubacteriales order 2 1 1 1.00 Bacteria Bacillota Clostridia Eubacteriales NA NA NA 12 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales
Clostridium perfringens species 2 0 2 0.50 Bacteria Bacillota Clostridia Eubacteriales Clostridiaceae Clostridium Clostridium perfringens 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium perfringens

Taxon Frequency Table by Race-ethnicity 

dat.sub_ethnic <- dat.sub %>% filter(dat.sub$Condition == "ethnic group") 
dim(dat.sub_ethnic)
## [1] 30 50
bugSigSimple::createTaxonTable(dat.sub_ethnic)  %>%
  kbl() %>%
  kable_styling()
Taxon Name Taxonomic Level total_signatures increased_signatures decreased_signatures Binomial Test pval kingdom phylum class order family genus species n_signatures metaphlan_name
Eubacteriales order 7 2 5 0.450 Bacteria Bacillota Clostridia Eubacteriales NA NA NA 9 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales
Coriobacteriaceae family 6 2 4 0.690 Bacteria Actinomycetota Coriobacteriia Coriobacteriales Coriobacteriaceae NA NA 6 k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
Christensenellaceae family 6 1 5 0.220 Bacteria Bacillota Clostridia Christensenellales Christensenellaceae NA NA 6 k__Bacteria|p__Bacillota|c__Clostridia|o__Christensenellales|f__Christensenellaceae
Peptococcaceae family 6 3 3 1.000 Bacteria Bacillota Clostridia Eubacteriales Peptococcaceae NA NA 6 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Peptococcaceae
Veillonella genus 6 6 0 0.031 Bacteria Bacillota Negativicutes Veillonellales Veillonellaceae Veillonella NA 6 k__Bacteria|p__Bacillota|c__Negativicutes|o__Veillonellales|f__Veillonellaceae|g__Veillonella
Odoribacteraceae family 6 3 3 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Odoribacteraceae NA NA 6 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae
Odoribacter genus 6 3 3 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Odoribacteraceae Odoribacter NA 6 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
Rikenellaceae family 6 2 4 0.690 Bacteria Bacteroidota Bacteroidia Bacteroidales Rikenellaceae NA NA 6 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Rikenellaceae
Victivallaceae family 6 4 2 0.690 Bacteria Lentisphaerota Lentisphaeria Victivallales Victivallaceae NA NA 6 k__Bacteria|p__Lentisphaerota|c__Lentisphaeria|o__Victivallales|f__Victivallaceae
Verrucomicrobiaceae family 6 0 6 0.031 Bacteria Verrucomicrobiota Verrucomicrobiia Verrucomicrobiales Verrucomicrobiaceae NA NA 6 k__Bacteria|p__Verrucomicrobiota|c__Verrucomicrobiia|o__Verrucomicrobiales|f__Verrucomicrobiaceae

Taxon Frequency Table by all studies (SES and Ethnicity) combined. 

bugSigSimple::createTaxonTable(dat.sub, n = 50) %>%
  kbl() %>%
  kable_styling()
Taxon Name Taxonomic Level total_signatures increased_signatures decreased_signatures Binomial Test pval kingdom phylum class order family genus species n_signatures metaphlan_name
Prevotella genus 12 7 5 0.770 Bacteria Bacteroidota Bacteroidia Bacteroidales Prevotellaceae Prevotella NA 13 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
Eubacteriales order 10 4 6 0.750 Bacteria Bacillota Clostridia Eubacteriales NA NA NA 32 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales
Rikenellaceae family 10 5 5 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Rikenellaceae NA NA 12 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Rikenellaceae
Bacteroides genus 9 3 6 0.510 Bacteria Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides NA 14 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
Coriobacteriaceae family 7 2 5 0.450 Bacteria Actinomycetota Coriobacteriia Coriobacteriales Coriobacteriaceae NA NA 9 k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
Bacillota phylum 7 3 4 1.000 Bacteria Bacillota NA NA NA NA NA 47 k__Bacteria|p__Bacillota
Ruminococcus genus 7 4 3 1.000 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Ruminococcus NA 7 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
Veillonella genus 7 7 0 0.016 Bacteria Bacillota Negativicutes Veillonellales Veillonellaceae Veillonella NA 7 k__Bacteria|p__Bacillota|c__Negativicutes|o__Veillonellales|f__Veillonellaceae|g__Veillonella
Odoribacter genus 7 4 3 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Odoribacteraceae Odoribacter NA 7 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
Christensenellaceae family 6 1 5 0.220 Bacteria Bacillota Clostridia Christensenellales Christensenellaceae NA NA 6 k__Bacteria|p__Bacillota|c__Clostridia|o__Christensenellales|f__Christensenellaceae
Clostridium genus 6 4 2 0.690 Bacteria Bacillota Clostridia Eubacteriales Clostridiaceae Clostridium NA 8 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
Eubacterium genus 6 4 2 0.690 Bacteria Bacillota Clostridia Eubacteriales Eubacteriaceae Eubacterium NA 6 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
Peptococcaceae family 6 3 3 1.000 Bacteria Bacillota Clostridia Eubacteriales Peptococcaceae NA NA 6 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Peptococcaceae
Odoribacteraceae family 6 3 3 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Odoribacteraceae NA NA 7 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae
Victivallaceae family 6 4 2 0.690 Bacteria Lentisphaerota Lentisphaeria Victivallales Victivallaceae NA NA 6 k__Bacteria|p__Lentisphaerota|c__Lentisphaeria|o__Victivallales|f__Victivallaceae
Verrucomicrobiaceae family 6 0 6 0.031 Bacteria Verrucomicrobiota Verrucomicrobiia Verrucomicrobiales Verrucomicrobiaceae NA NA 6 k__Bacteria|p__Verrucomicrobiota|c__Verrucomicrobiia|o__Verrucomicrobiales|f__Verrucomicrobiaceae
Bacteroidota phylum 5 5 0 0.062 Bacteria Bacteroidota NA NA NA NA NA 41 k__Bacteria|p__Bacteroidota
Bifidobacterium genus 4 3 1 0.630 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium NA 8 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium
Bifidobacterium longum species 4 3 1 0.630 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium longum 4 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium longum
Oscillospiraceae family 4 4 0 0.120 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae NA NA 19 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
Faecalibacterium genus 4 3 1 0.630 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Faecalibacterium NA 7 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
Faecalibacterium prausnitzii species 4 2 2 1.000 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Faecalibacterium Faecalibacterium prausnitzii 4 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium|s__Faecalibacterium prausnitzii
Bifidobacterium adolescentis species 3 1 2 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium adolescentis 3 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium adolescentis
Bifidobacterium bifidum species 3 2 1 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium bifidum 3 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium bifidum
Lactobacillus genus 3 2 1 1.000 Bacteria Bacillota Bacilli Lactobacillales Lactobacillaceae Lactobacillus NA 4 k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
Lachnospiraceae family 3 2 1 1.000 Bacteria Bacillota Clostridia Lachnospirales Lachnospiraceae NA NA 12 k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae
Blautia genus 3 2 1 1.000 Bacteria Bacillota Clostridia Lachnospirales Lachnospiraceae Blautia NA 4 k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Blautia
Bacteroides fragilis species 3 1 2 1.000 Bacteria Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides Bacteroides fragilis 3 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides fragilis
Bacteroides thetaiotaomicron species 3 0 3 0.250 Bacteria Bacteroidota Bacteroidia Bacteroidales Bacteroidaceae Bacteroides Bacteroides thetaiotaomicron 3 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides thetaiotaomicron
Parabacteroides genus 3 3 0 0.250 Bacteria Bacteroidota Bacteroidia Bacteroidales Tannerellaceae Parabacteroides NA 3 k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Parabacteroides
Enterobacteriaceae family 3 1 2 1.000 Bacteria Pseudomonadota Gammaproteobacteria Enterobacterales Enterobacteriaceae NA NA 7 k__Bacteria|p__Pseudomonadota|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae
Deltaproteobacteria class 2 2 0 0.500 Bacteria Deltaproteobacteria NA NA NA NA NA 2 k__Bacteria|c__Deltaproteobacteria
Actinomycetota phylum 2 2 0 0.500 Bacteria Actinomycetota NA NA NA NA NA 21 k__Bacteria|p__Actinomycetota
Bifidobacterium breve species 2 1 1 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium breve 2 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium breve
Bifidobacterium catenulatum species 2 1 1 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium catenulatum 2 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium catenulatum
Bifidobacterium pseudocatenulatum species 2 1 1 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium pseudocatenulatum 2 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium pseudocatenulatum
Bifidobacterium ruminantium species 2 1 1 1.000 Bacteria Actinomycetota Actinomycetes Bifidobacteriales Bifidobacteriaceae Bifidobacterium Bifidobacterium ruminantium 2 k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium ruminantium
Atopobiaceae family 2 1 1 1.000 Bacteria Actinomycetota Coriobacteriia Coriobacteriales Atopobiaceae NA NA 2 k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Atopobiaceae
Slackia genus 2 2 0 0.500 Bacteria Actinomycetota Coriobacteriia Eggerthellales Eggerthellaceae Slackia NA 2 k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Slackia
Staphylococcus genus 2 1 1 1.000 Bacteria Bacillota Bacilli Bacillales Staphylococcaceae Staphylococcus NA 3 k__Bacteria|p__Bacillota|c__Bacilli|o__Bacillales|f__Staphylococcaceae|g__Staphylococcus
Enterococcus genus 2 2 0 0.500 Bacteria Bacillota Bacilli Lactobacillales Enterococcaceae Enterococcus NA 2 k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Enterococcaceae|g__Enterococcus
Lactococcus genus 2 1 1 1.000 Bacteria Bacillota Bacilli Lactobacillales Streptococcaceae Lactococcus NA 2 k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Lactococcus
Clostridiaceae family 2 0 2 0.500 Bacteria Bacillota Clostridia Eubacteriales Clostridiaceae NA NA 11 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
Clostridium perfringens species 2 0 2 0.500 Bacteria Bacillota Clostridia Eubacteriales Clostridiaceae Clostridium Clostridium perfringens 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium perfringens
Anaerotruncus genus 2 1 1 1.000 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Anaerotruncus NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Anaerotruncus
Oscillospira genus 2 1 1 1.000 Bacteria Bacillota Clostridia Eubacteriales Oscillospiraceae Oscillospira NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Oscillospira
butyrate-producing bacterium M21/2 species 2 1 1 1.000 Bacteria Bacillota Clostridia Eubacteriales butyrate-producing bacterium M21/2 NA NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|s__butyrate-producing bacterium M21/2
Clostridiales bacterium species 2 1 1 1.000 Bacteria Bacillota Clostridia Eubacteriales Clostridiales bacterium NA NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|s__Clostridiales bacterium
Anaerostipes genus 2 2 0 0.500 Bacteria Bacillota Clostridia Lachnospirales Lachnospiraceae Anaerostipes NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Anaerostipes
Butyrivibrio genus 2 2 0 0.500 Bacteria Bacillota Clostridia Lachnospirales Lachnospiraceae Butyrivibrio NA 2 k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Butyrivibrio

Cluster Analysis for signatures of increased abundance and of decreased abundance in low SES calculated using Jaccard Distance to create a distance matrix. 

allsigs <- bugsigdbr::getSignatures(dat.sub_SES , tax.id.type = "taxname")
allsigs <- allsigs[sapply(allsigs, length) > 1] #require length > 1
length(allsigs)
## [1] 17
mydists <- BugSigDBStats::calcPairwiseOverlaps(allsigs)
dim(mydists)
## [1] 24  8

Visualize the distribution of the signature lengths 

library(ggplot2)
siglengths <- sapply(allsigs, length)
siglengths.df <- data.frame(siglengths = siglengths)
ggplot(siglengths.df, aes(x=siglengths)) +
  geom_bar()

table(siglengths)
## siglengths
## 2 3 4 5 6 7 8 
## 1 5 1 3 2 2 3

Create a matrix of Jaccard similarities (0 for no overlap, 1 for 100% overlap) 

jmat <- BugSigDBStats::calcJaccardSimilarity(allsigs)

##Create a Clustered heatmap

library(ComplexHeatmap)

ha <- HeatmapAnnotation(`Signature Length` = anno_barplot(siglengths))
hr <- rowAnnotation(
  `Signature Length` = anno_barplot(siglengths)
  )
hm <- Heatmap(
  jmat,
  top_annotation = ha,
#  left_annotation = hr,
#  column_names_max_height = unit(23, "cm"),
  column_names_rot = 45,
#  row_names_max_width = unit(15, "cm"),
#  get rid of study labels
  row_labels = sub("bsdb:", "", sub("_.+", "", rownames(jmat)), fixed = TRUE),  
  column_labels = sub("bsdb:", "", sub("_.+", "", colnames(jmat)), fixed = TRUE)
)

hm

Create a wide format dataframe for Regression Analysis 

dat_withsigs <- filter(dat.sub_SES , !is.na(dat.sub_SES$`NCBI Taxonomy IDs`))
sigs <- bugsigdbr::getSignatures(dat_withsigs, tax.id.type = "taxname")
cmat <- t(safe::getCmatrix(sigs, as.matrix = TRUE, min.size = 0, prune = FALSE))
## WARNING: rows are sorted elements of keyword.list
## 23 categories formed
cdf <- data.frame(cmat, stringsAsFactors = FALSE, check.names = FALSE)
cdf <- cbind(dat_withsigs, cdf)
dim(cdf)
## [1]  23 117

An arbitrary example of meta-regression:

fit <-
  glm(
    Prevotella ~ `Location of subjects` + `Sequencing type` + `Abundance in Group 1`,
    family = binomial(link = "logit"),
    data = cdf
  )
summary(fit)
## 
## Call:
## glm(formula = Prevotella ~ `Location of subjects` + `Sequencing type` + 
##     `Abundance in Group 1`, family = binomial(link = "logit"), 
##     data = cdf)
## 
## Coefficients:
##                                                  Estimate Std. Error z value
## (Intercept)                                    -1.989e+01  1.183e+04  -0.002
## `Location of subjects`China                     2.087e+01  1.183e+04   0.002
## `Location of subjects`Finland,Malawi            1.953e+01  2.048e+04   0.001
## `Location of subjects`Israel                    2.087e+01  1.183e+04   0.002
## `Location of subjects`Malaysia                  1.950e-08  1.448e+04   0.000
## `Location of subjects`Mexico                    2.087e+01  1.183e+04   0.002
## `Location of subjects`Papua New Guinea          1.276e+00  2.131e+04   0.000
## `Location of subjects`United Kingdom            1.949e-08  1.672e+04   0.000
## `Location of subjects`United States of America  1.953e+01  1.183e+04   0.002
## `Sequencing type`WMS                           -1.953e+01  1.183e+04  -0.002
## `Abundance in Group 1`increased                -1.954e+00  1.543e+00  -1.267
##                                                Pr(>|z|)
## (Intercept)                                       0.999
## `Location of subjects`China                       0.999
## `Location of subjects`Finland,Malawi              0.999
## `Location of subjects`Israel                      0.999
## `Location of subjects`Malaysia                    1.000
## `Location of subjects`Mexico                      0.999
## `Location of subjects`Papua New Guinea            1.000
## `Location of subjects`United Kingdom              1.000
## `Location of subjects`United States of America    0.999
## `Sequencing type`WMS                              0.999
## `Abundance in Group 1`increased                   0.205
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 21.254  on 22  degrees of freedom
## Residual deviance: 10.956  on 12  degrees of freedom
## AIC: 32.956
## 
## Number of Fisher Scoring iterations: 19

Create another heatmap on correlations of presence/absence of taxa. 

sigcors <- cor(t(cmat))
siglengths <- sapply(sigs, length)
ha <- HeatmapAnnotation(`Signature Length` = anno_barplot(siglengths))
hr <- rowAnnotation(`Signature Length` = anno_barplot(siglengths))
hm <- Heatmap(
  sigcors,
  top_annotation = ha, left_annotation = hr,
  row_names_max_width = unit(.05, "cm"),
  column_names_max_height = unit(.1, "cm"),
 # row_labels = sub(".+:", "", rownames(sigcors)), ##removing study just to make signature names legible
  column_labels = sub(".+:", "", colnames(sigcors))
)
hm

Cluster Analysis for signatures of increased abundance and of decreased abundance in non-white/caucasions calculated using Jaccard Distance to create a distance matrix. 

allsige <- bugsigdbr::getSignatures(dat.sub_ethnic , tax.id.type = "taxname")
allsige <- allsige[sapply(allsige, length) > 1] #require length > 1
length(allsige )
## [1] 14
mydistse <- BugSigDBStats::calcPairwiseOverlaps(allsige)
dim(mydistse)
## [1] 51  8

Visualize the distribution of the signature lengths 

library(ggplot2)
siglengthse <- sapply(allsige, length)
siglengthse.df <- data.frame(siglengthse = siglengthse)
ggplot(siglengthse.df, aes(x=siglengthse)) +
  geom_bar()

table(siglengthse)
## siglengthse
## 2 4 5 6 8 9 
## 4 1 4 2 2 1

Create a matrix of Jaccard similarities (0 for no overlap, 1 for 100% overlap) 

jmate <- BugSigDBStats::calcJaccardSimilarity(allsige)

Create a Clustered heatmap 

library(ComplexHeatmap)

hae <- HeatmapAnnotation(`Signature Length` = anno_barplot(siglengthse))
hre <- rowAnnotation(`Signature Length` = anno_barplot(siglengthse))
hme <- Heatmap(
  jmate,
  top_annotation = hae, left_annotation = hre,
  row_names_max_width = unit(.10,"cm"),
  column_names_max_height = unit(1.0, "cm"),
#  row_labels = sub(".+:", "", rownames(jmate)),  #get rid of study labels
  column_labels = sub(".+:", "", colnames(jmate))
)

hme

Create a wide format dataframe 

dat_withsigse <- filter(dat.sub_ethnic , !is.na(dat.sub_ethnic$`NCBI Taxonomy IDs`))
sigse <- bugsigdbr::getSignatures(dat_withsigse, tax.id.type = "taxname")
cmate <- t(safe::getCmatrix(sigse, as.matrix = TRUE, min.size = 0, prune = FALSE))
## WARNING: rows are sorted elements of keyword.list
## 30 categories formed
cdfe <- data.frame(cmate, stringsAsFactors = FALSE, check.names = FALSE)
cdfe <- cbind(dat_withsigse, cdfe)
colnames(cdfe)[1:25]
##  [1] "BSDB ID"               "Study"                 "Study design"         
##  [4] "PMID"                  "DOI"                   "URL"                  
##  [7] "Authors list"          "Title"                 "Journal"              
## [10] "Year"                  "Keywords"              "Experiment"           
## [13] "Location of subjects"  "Host species"          "Body site"            
## [16] "UBERON ID"             "Condition"             "EFO ID"               
## [19] "Group 0 name"          "Group 1 name"          "Group 1 definition"   
## [22] "Group 0 sample size"   "Group 1 sample size"   "Antibiotics exclusion"
## [25] "Sequencing type"

Create another heatmap on correlations of presence/absence of taxa. 

sigcorse <- cor(t(cmate))
siglengthse <- sapply(sigse, length)
hae <- HeatmapAnnotation(`Signature Length` = anno_barplot(siglengthse))
hre <- rowAnnotation(`Signature Length` = anno_barplot(siglengthse))
hme <- Heatmap(
  sigcorse,
  top_annotation = hae, left_annotation = hre,
  row_names_max_width = unit(.05, "cm"),
  column_names_max_height = unit(.1, "cm"),
 # row_labels = sub(".+:", "", rownames(sigcors)), ##removing study just to make signature names legible
  column_labels = sub(".+:", "", colnames(sigcorse))
)
hme