COVID-19 associated microbiome

Analysis objectives

Import, recode, and subset data from bugsigdb.org
Create a table of studies
Create a clustered heatmap showing similarity of signatures from independent studies
Calculate the frequency of appearance of each taxa in independent signatures, and identify the most frequently reported taxa
Estimate the probability of the most frequently identified taxa occuring so frequently by chance

Packages installation

Install packages (not evaluated in vignette)

install.packages(c("devtools", "tidyverse", "kableExtra", "gt", "glue"))
devtools::install_github("waldronlab/bugSigSimple")
devtools::install_github("waldronlab/BugSigDBStats")
devtools::install_github("waldronlab/bugsigdbr")

Data import, recoding, and subset

library(bugSigSimple)
dat <- bugsigdbr::importBugSigDB(cache = TRUE)

## Using cached version from 2025-01-30 21:57:36

dim(dat)

## [1] 5520   50

names(dat)

##  [1] "BSDB ID"                    "Study"                     
##  [3] "Study design"               "PMID"                      
##  [5] "DOI"                        "URL"                       
##  [7] "Authors list"               "Title"                     
##  [9] "Journal"                    "Year"                      
## [11] "Keywords"                   "Experiment"                
## [13] "Location of subjects"       "Host species"              
## [15] "Body site"                  "UBERON ID"                 
## [17] "Condition"                  "EFO ID"                    
## [19] "Group 0 name"               "Group 1 name"              
## [21] "Group 1 definition"         "Group 0 sample size"       
## [23] "Group 1 sample size"        "Antibiotics exclusion"     
## [25] "Sequencing type"            "16S variable region"       
## [27] "Sequencing platform"        "Statistical test"          
## [29] "Significance threshold"     "MHT correction"            
## [31] "LDA Score above"            "Matched on"                
## [33] "Confounders controlled for" "Pielou"                    
## [35] "Shannon"                    "Chao1"                     
## [37] "Simpson"                    "Inverse Simpson"           
## [39] "Richness"                   "Signature page name"       
## [41] "Source"                     "Curated date"              
## [43] "Curator"                    "Revision editor"           
## [45] "Description"                "Abundance in Group 1"      
## [47] "MetaPhlAn taxon names"      "NCBI Taxonomy IDs"         
## [49] "State"                      "Reviewer"

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

 efo <- bugsigdbr::getOntology("efo")

## Loading required namespace: ontologyIndex
## Using cached version from 2025-01-30 21:55:38

covid_all <- bugsigdbr::subsetByOntology(dat, column = "Condition", "COVID-19", efo) %>%
  mutate(studyexp = paste(Study, Experiment, sep = "_")) %>%
  mutate(
    site = recode(`Body site`,
      "Feces" = "Gut",
      "Rectum" = "Gut",
      "Nasopharynx" = "aURT",
      "Oropharynx" = "aURT",
      "Nasopharynx,Oropharynx" = "aURT",
      "Nasal cavity" = "aURT",
      "Surface of tongue" = "aURT",
      "Nasopharynx,Throat" = "aURT",
      "Throat" = "aURT",
      "Tongue" = "aURT",
      "Lung" = "LRT",
      "Sputum" = "LRT"
    )
  ) %>%
  mutate(comparison1 = paste(`Group 0 name`, `Group 1 name`, sep = " vs "))

Table of studies

bugSigSimple::createStudyTable(covid_all)

## # A tibble: 44 × 5
##    Study          Condition Cases Controls `Study Design`                       
##    <chr>          <chr>     <dbl>    <dbl> <chr>                                
##  1 Bellato 2023   COVID-19    156       36 cross-sectional observational, not c…
##  2 Braun 2021     COVID-19     26       29 cross-sectional observational, not c…
##  3 Cao 2021       COVID-19     13        8 case-control                         
##  4 Chen 2021      COVID-19     30       30 time series / longitudinal observati…
##  5 De 2020        COVID-19     18       22 cross-sectional observational, not c…
##  6 Engen 2021     COVID-19      9       10 cross-sectional observational, not c…
##  7 Gaibani 2021   COVID-19     24       24 case-control                         
##  8 Galperine 2023 COVID-19     57       43 time series / longitudinal observati…
##  9 Gao 2021       COVID-19     94       48 prospective cohort                   
## 10 Gu 2020        COVID-19     30       30 cross-sectional observational, not c…
## # ℹ 34 more rows

Taxon frequency tables by body site

library(dplyr)
gut_sigs <- filter(covid_all, 
                           site == "Gut") %>%
    drop_na(Source)

naso_sigs <- filter(covid_all, 
                           site == "aURT") %>%
    drop_na(Source)
  
resp_sigs <- filter(covid_all, 
                           site == "LRT") %>%
  drop_na(Source)

In this table, the Binomial Test p-value corresponds to the null hypothesis

H0: the proportion of signatures in which the taxon is reported increased or decreased, relative to the total number of signatures in which it is reported, is equal to 0.5

kableExtra::kbl(bugSigSimple::createTaxonTable(gut_sigs))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval	kingdom	phylum	class	order	family	genus	species	n_signatures	metaphlan_name
Faecalibacterium	genus	15	1	14	0.00098	Bacteria	Bacillota	Clostridia	Eubacteriales	Oscillospiraceae	Faecalibacterium	NA	21	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Oscillospiraceae\|g__Faecalibacterium
Anaerostipes	genus	15	4	11	0.12000	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Anaerostipes	NA	17	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Anaerostipes
Streptococcus	genus	12	10	2	0.03900	Bacteria	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	NA	18	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Streptococcaceae\|g__Streptococcus
Clostridium	genus	12	6	6	1.00000	Bacteria	Bacillota	Clostridia	Eubacteriales	Clostridiaceae	Clostridium	NA	18	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Clostridiaceae\|g__Clostridium
Roseburia	genus	12	0	12	0.00049	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Roseburia	NA	19	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Roseburia
Enterococcus	genus	11	11	0	0.00098	Bacteria	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	Enterococcus	NA	18	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Enterococcaceae\|g__Enterococcus
Ruminococcus	genus	10	3	7	0.34000	Bacteria	Bacillota	Clostridia	Eubacteriales	Oscillospiraceae	Ruminococcus	NA	20	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Oscillospiraceae\|g__Ruminococcus
Lactobacillaceae	family	9	5	4	1.00000	Bacteria	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	NA	NA	19	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Lactobacillaceae
Faecalibacterium prausnitzii	species	9	0	9	0.00390	Bacteria	Bacillota	Clostridia	Eubacteriales	Oscillospiraceae	Faecalibacterium	Faecalibacterium prausnitzii	9	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Oscillospiraceae\|g__Faecalibacterium\|s__Faecalibacterium prausnitzii
Anaerobutyricum hallii	species	9	3	6	0.51000	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Anaerobutyricum	Anaerobutyricum hallii	9	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Anaerobutyricum\|s__Anaerobutyricum hallii

kableExtra::kbl(bugSigSimple::createTaxonTable(naso_sigs))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval	kingdom	phylum	class	order	family	genus	species	n_signatures	metaphlan_name
Veillonella	genus	15	12	3	0.035	Bacteria	Bacillota	Negativicutes	Veillonellales	Veillonellaceae	Veillonella	NA	17	k__Bacteria\|p__Bacillota\|c__Negativicutes\|o__Veillonellales\|f__Veillonellaceae\|g__Veillonella
Prevotella	genus	12	6	6	1.000	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotella	NA	22	k__Bacteria\|p__Bacteroidota\|c__Bacteroidia\|o__Bacteroidales\|f__Prevotellaceae\|g__Prevotella
Haemophilus	genus	11	2	9	0.065	Bacteria	Pseudomonadota	Gammaproteobacteria	Pasteurellales	Pasteurellaceae	Haemophilus	NA	13	k__Bacteria\|p__Pseudomonadota\|c__Gammaproteobacteria\|o__Pasteurellales\|f__Pasteurellaceae\|g__Haemophilus
Streptococcus	genus	10	4	6	0.750	Bacteria	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	NA	15	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Streptococcaceae\|g__Streptococcus
Oribacterium	genus	10	2	8	0.110	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Oribacterium	NA	10	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Oribacterium
Rothia	genus	9	3	6	0.510	Bacteria	Actinomycetota	Actinomycetes	Micrococcales	Micrococcaceae	Rothia	NA	12	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Micrococcales\|f__Micrococcaceae\|g__Rothia
Neisseria	genus	9	1	8	0.039	Bacteria	Pseudomonadota	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA	13	k__Bacteria\|p__Pseudomonadota\|c__Betaproteobacteria\|o__Neisseriales\|f__Neisseriaceae\|g__Neisseria
Filifactor	genus	8	1	7	0.070	Bacteria	Bacillota	Clostridia	Peptostreptococcales	Filifactoraceae	Filifactor	NA	8	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Peptostreptococcales\|f__Filifactoraceae\|g__Filifactor
Alloprevotella	genus	8	4	4	1.000	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Prevotellaceae	Alloprevotella	NA	8	k__Bacteria\|p__Bacteroidota\|c__Bacteroidia\|o__Bacteroidales\|f__Prevotellaceae\|g__Alloprevotella
Bifidobacterium	genus	7	0	7	0.016	Bacteria	Actinomycetota	Actinomycetes	Bifidobacteriales	Bifidobacteriaceae	Bifidobacterium	NA	7	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Bifidobacteriales\|f__Bifidobacteriaceae\|g__Bifidobacterium

kableExtra::kbl(bugSigSimple::createTaxonTable(resp_sigs))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval	kingdom	phylum	class	order	family	genus	species	n_signatures	metaphlan_name
Rothia mucilaginosa	species	2	1	1	1.0	Bacteria	Actinomycetota	Actinomycetes	Micrococcales	Micrococcaceae	Rothia	Rothia mucilaginosa	2	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Micrococcales\|f__Micrococcaceae\|g__Rothia\|s__Rothia mucilaginosa
Streptococcus	genus	2	1	1	1.0	Bacteria	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	NA	3	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Streptococcaceae\|g__Streptococcus
Lachnoclostridium phytofermentans	species	2	1	1	1.0	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Lachnoclostridium	Lachnoclostridium phytofermentans	2	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Lachnoclostridium\|s__Lachnoclostridium phytofermentans
Porphyromonas	genus	2	0	2	0.5	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Porphyromonadaceae	Porphyromonas	NA	2	k__Bacteria\|p__Bacteroidota\|c__Bacteroidia\|o__Bacteroidales\|f__Porphyromonadaceae\|g__Porphyromonas
Tannerella	genus	2	0	2	0.5	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Tannerellaceae	Tannerella	NA	2	k__Bacteria\|p__Bacteroidota\|c__Bacteroidia\|o__Bacteroidales\|f__Tannerellaceae\|g__Tannerella
Capnocytophaga	genus	2	1	1	1.0	Bacteria	Bacteroidota	Flavobacteriia	Flavobacteriales	Flavobacteriaceae	Capnocytophaga	NA	2	k__Bacteria\|p__Bacteroidota\|c__Flavobacteriia\|o__Flavobacteriales\|f__Flavobacteriaceae\|g__Capnocytophaga
Neisseria	genus	2	0	2	0.5	Bacteria	Pseudomonadota	Betaproteobacteria	Neisseriales	Neisseriaceae	Neisseria	NA	2	k__Bacteria\|p__Pseudomonadota\|c__Betaproteobacteria\|o__Neisseriales\|f__Neisseriaceae\|g__Neisseria
Actinomyces	genus	1	0	1	1.0	Bacteria	Actinomycetota	Actinomycetes	Actinomycetales	Actinomycetaceae	Actinomyces	NA	2	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Actinomycetales\|f__Actinomycetaceae\|g__Actinomyces
Actinomyces pacaensis	species	1	1	0	1.0	Bacteria	Actinomycetota	Actinomycetes	Actinomycetales	Actinomycetaceae	Actinomyces	Actinomyces pacaensis	1	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Actinomycetales\|f__Actinomycetaceae\|g__Actinomyces\|s__Actinomyces pacaensis
Schaalia meyeri	species	1	1	0	1.0	Bacteria	Actinomycetota	Actinomycetes	Actinomycetales	Actinomycetaceae	Schaalia	Schaalia meyeri	1	k__Bacteria\|p__Actinomycetota\|c__Actinomycetes\|o__Actinomycetales\|f__Actinomycetaceae\|g__Schaalia\|s__Schaalia meyeri

gut microbiota analysis

Look specifically at case-control comparisons

healthy <- grepl(".*(healthy.*COVID|COVID.*healthy).*", gut_sigs$comparison1, ignore.case = TRUE)

cc_gut_sigs1 <- gut_sigs %>%
      filter(healthy == TRUE)

Overall frequencies of taxa increased in cases for case/control feces studies

Identifying a taxon reported consistently in 8 out of 8 studies is much more compelling that the FDR value here would suggest, since this taxon also passed a significance threshold in every one of those studies.

cc_gut_sigs1_taxontable <- bugSigSimple::createTaxonTable(cc_gut_sigs1) %>% 
  mutate(FDR =  p.adjust(p = `Binomial Test pval`, method="fdr")) %>%
  relocate(FDR, .after = `Binomial Test pval`)
kableExtra::kbl(cc_gut_sigs1_taxontable)

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval	FDR	kingdom	phylum	class	order	family	genus	species	n_signatures	metaphlan_name
Faecalibacterium	genus	9	0	9	0.0039	0.0156000	Bacteria	Bacillota	Clostridia	Eubacteriales	Oscillospiraceae	Faecalibacterium	NA	14	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Oscillospiraceae\|g__Faecalibacterium
Anaerostipes	genus	9	1	8	0.0390	0.0557143	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Anaerostipes	NA	10	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Anaerostipes
Roseburia	genus	9	0	9	0.0039	0.0156000	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Roseburia	NA	11	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Roseburia
Enterococcus	genus	8	8	0	0.0078	0.0156000	Bacteria	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	Enterococcus	NA	11	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Enterococcaceae\|g__Enterococcus
Lactobacillaceae	family	8	5	3	0.7300	0.7300000	Bacteria	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	NA	NA	12	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Lactobacillaceae
Streptococcus	genus	8	8	0	0.0078	0.0156000	Bacteria	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus	NA	10	k__Bacteria\|p__Bacillota\|c__Bacilli\|o__Lactobacillales\|f__Streptococcaceae\|g__Streptococcus
Clostridium	genus	8	3	5	0.7300	0.7300000	Bacteria	Bacillota	Clostridia	Eubacteriales	Clostridiaceae	Clostridium	NA	8	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Clostridiaceae\|g__Clostridium
Coprococcus	genus	8	0	8	0.0078	0.0156000	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Coprococcus	NA	13	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Coprococcus
Ruminococcus	genus	7	1	6	0.1300	0.1625000	Bacteria	Bacillota	Clostridia	Eubacteriales	Oscillospiraceae	Ruminococcus	NA	14	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Eubacteriales\|f__Oscillospiraceae\|g__Ruminococcus
Blautia obeum	species	7	0	7	0.0160	0.0266667	Bacteria	Bacillota	Clostridia	Lachnospirales	Lachnospiraceae	Blautia	Blautia obeum	7	k__Bacteria\|p__Bacillota\|c__Clostridia\|o__Lachnospirales\|f__Lachnospiraceae\|g__Blautia\|s__Blautia obeum

Monte-Carlo simulation for increased abundance taxa

Just for the increased cc_gut_sigs1 for now. I am inclined to skip this analysis in favor of the clustering and binomial test analysis.

library(bugSigSimple)
gut.sigs.increased <- filter(cc_gut_sigs1, `Abundance in Group 1` == "increased") %>% 
  bugsigdbr::getSignatures(tax.id.type = "taxname")
 my.siglengths.inc <- sapply(gut.sigs.increased, length)

getCriticalN(gut.sigs.increased, my.siglengths.inc)

## 95% 
##  10

# Compare to observed - enterococcus & streptococcus are the only taxa that equal the critical limit
frequencySigs(gut.sigs.increased)

##            Enterococcus           Streptococcus      Enterobacteriaceae 
##                       8                       8                       5 
## Enterocloster citroniae         Enterococcaceae        Lactobacillaceae 
##                       5                       5                       5 
##           Lactobacillus                  Rothia          Actinomycetota 
##                       5                       5                       4 
##               Atopobium 
##                       4

Overall frequencies of taxa decreased in cases for case/control feces studies

createTaxonTable(cc_gut_sigs1, n=40)

nasopharyngeal microbiota analysis

Look specifically at case-control comparisons

library(dplyr)
healthy <- grepl(".*(control.*COVID|COVID.*control).*", naso_sigs$comparison1, ignore.case = TRUE)

cc_naso_sigs1 <- naso_sigs %>%
      filter(healthy == TRUE) %>%
  subset(Study != "Study 458")

Cluster analysis

Note, this EDA should really be done before hypothesis testing.

First calculate pairwise overlaps for all signatures of length > 1:

allsigs <- bugsigdbr::getSignatures(covid_all, tax.id.type = "taxname")
allsigs <- allsigs[sapply(allsigs, length) > 1] #require length > 1
dim(allsigs)

## NULL

mydists <- BugSigDBStats::calcPairwiseOverlaps(allsigs)
dim(mydists)

## [1] 2722    8

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

jmat <- BugSigDBStats::calcJaccardSimilarity(allsigs)

library(ComplexHeatmap)

## Loading required package: grid

## ========================================
## ComplexHeatmap version 2.22.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
## 
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
##     genomic data. Bioinformatics 2016.
## 
## 
## The new InteractiveComplexHeatmap package can directly export static 
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
## 
## This message can be suppressed by:
##   suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================

siglengths <- sapply(allsigs, length)
ha <- HeatmapAnnotation(`Signature Length` = anno_barplot(siglengths))
hr <- rowAnnotation(`Signature Length` = anno_barplot(siglengths))
hm <- Heatmap(
  jmat,
  top_annotation = ha, left_annotation = hr,
  row_names_max_width = unit(20, "cm"),
  column_names_max_height = unit(20, "cm"),
  row_labels = sub(".+:", "", rownames(jmat)),
  column_labels = sub(".+:", "", colnames(jmat))
)

## The automatically generated colors map from the 1^st and 99^th of the
## values in the matrix. There are outliers in the matrix whose patterns
## might be hidden by this color mapping. You can manually set the color
## to `col` argument.
## 
## Use `suppressMessages()` to turn off this message.

hm

Use this interactively to make an interactive heatmap (not evaluated in vignette). Some expanding of the default size is required to see anything. Creating a sub-heatmap, then exporting it as a table, allows in-depth identification of the subgroups.

library(InteractiveComplexHeatmap)
hm <- draw(hm)
htShiny(hm)

hc <- hclust(as.dist(jmat))
plot(hc)

This tree can be cut to show the clusters, for example. The clusters of more than 1 signature but less than ~10 signatures are most likely to be something interesting.

clusts <- sort(cutree(hc, h = 0.05))
lapply(unique(clusts), function(i) names(clusts)[clusts == i])

## [[1]]
##  [1] "bsdb:428/1/1_COVID-19:COVID-19-cases_vs_Healthy-controls_UP"                                                                                       
##  [2] "bsdb:428/1/2_COVID-19:COVID-19-cases_vs_Healthy-controls_DOWN"                                                                                     
##  [3] "bsdb:428/3/1_COVID-19:Recovered-COVID-19-cases_vs_Healthy-controls_UP"                                                                             
##  [4] "bsdb:430/1/1_COVID-19:Patients-with-high-SARS-CoV-2-infectivity_vs_Patients-with-low-to-none-SARS-CoV-2-infectivity_DOWN"                          
##  [5] "bsdb:441/1/1_COVID-19:COVID-19-cases_vs_Healthy-controls_UP"                                                                                       
##  [6] "bsdb:441/3/2_COVID-19:COVID-19-patients_vs_H1N1-patients_DOWN"                                                                                     
##  [7] "bsdb:449/1/2_COVID-19:COVID-19-cases_vs_Healthy-controls_DOWN"                                                                                     
##  [8] "bsdb:453/3/1_COVID-19:Recovered-COVID-19-patients_vs_Non-infected-patients-with-unrelated-respiratory-medical-conditions_UP"                       
##  [9] "bsdb:453/3/2_COVID-19:Recovered-COVID-19-patients_vs_Non-infected-patients-with-unrelated-respiratory-medical-conditions_DOWN"                     
## [10] "bsdb:458/1/1_COVID-19:COVID-19-patients_vs_Non-intubated-patients-with-non-incubation-viral-pneumonia-or-non-incubation-non-infectious-diseases_UP"
## [11] "bsdb:459/1/2_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls_UP"                                                                 
## [12] "bsdb:464/1/2_COVID-19:Confirmed-COVID-19-patients_vs_Healthy-controls_UP"                                                                          
## [13] "bsdb:469/1/1_COVID-19:COVID-19-patients_vs_Non-COVID-19-controls_DOWN"                                                                             
## [14] "bsdb:474/1/2_COVID-19:COVID-19-patients_vs_Non-COVID-patients-with-URTI-or-COPD_DOWN"                                                              
## [15] "bsdb:479/2/2_COVID-19:Mild-moderate-COVID-19-patients_vs_Healthy-controls_UP"                                                                      
## [16] "bsdb:482/1/2_COVID-19:Severe-COVID-19-patients_vs_Mild-COVID-19-patients_UP"                                                                       
## [17] "bsdb:483/1/1_COVID-19:Antibiotic-naive-COVID-19-patients_vs_Uninfected-controls,-pneumonia-patients,-antibiotics-treated-COVID-19-patients_UP"     
## [18] "bsdb:483/3/1_COVID-19:Pneumonia-controls_vs_Uninfected-controls,-COVID-19-patients_DOWN"                                                           
## [19] "bsdb:484/1/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-patients-with-fever-and-cough_UP"                                            
## [20] "bsdb:485/1/1_COVID-19:COVID-19-patients-with-fever_vs_COVID-19-patients-without-fever_UP"                                                          
## [21] "bsdb:511/1/2_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                                                                  
## [22] "bsdb:512/3/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-healthcare-workers-(controls)_UP"                                            
## [23] "bsdb:513/3/1_COVID-19:COVID-19-positive-patients-with-antibiotic-use_vs_COVID-19-positive-patients-without-antibiotic-use_UP"                      
## [24] "bsdb:734/1/1_COVID-19:COVID-19-infection-cases._vs_Control-group_UP"                                                                               
## [25] "bsdb:734/1/2_COVID-19:COVID-19-infection-cases._vs_Control-group_DOWN"                                                                             
## [26] "bsdb:831/1/1_COVID-19:COVID-19-Patients_vs_Non-Covid-19-Patients---pneumonia-patients_UP"                                                          
## [27] "bsdb:940/1/2_COVID-19:Covid-19-group_vs_Control-group_DOWN"                                                                                        
## [28] "bsdb:940/2/1_COVID-19:Post-Covid-19-group_vs_Covid-19-group_UP"                                                                                    
## 
## [[2]]
##  [1] "bsdb:428/2/1_COVID-19:Recovered-COVID-19-cases_vs_Healthy-controls_UP"                                                         
##  [2] "bsdb:428/2/2_COVID-19:Recovered-COVID-19-cases_vs_Healthy-controls_DOWN"                                                       
##  [3] "bsdb:441/1/2_COVID-19:COVID-19-cases_vs_Healthy-controls_DOWN"                                                                 
##  [4] "bsdb:441/4/1_COVID-19:COVID-19-cases_vs_Healthy-controls_UP"                                                                   
##  [5] "bsdb:453/1/1_COVID-19:SARS-CoV-2-infected-patients_vs_Non-infected-patients-with-unrelated-respiratory-medical-conditions_UP"  
##  [6] "bsdb:464/2/2_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_UP"                                                      
##  [7] "bsdb:474/1/1_COVID-19:COVID-19-patients_vs_Non-COVID-patients-with-URTI-or-COPD_UP"                                            
##  [8] "bsdb:476/1/1_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_UP"                                                      
##  [9] "bsdb:478/1/2_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                                                
## [10] "bsdb:479/2/1_COVID-19:Mild-moderate-COVID-19-patients_vs_Healthy-controls_DOWN"                                                
## [11] "bsdb:481/1/1_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                                                
## [12] "bsdb:481/3/1_COVID-19:Severe-COVID-19-patients_vs_Mild-COVID-19-patients_UP"                                                   
## [13] "bsdb:487/1/1_COVID-19:Recovered-COVID-19-samples-(respiratory-negative)_vs_Infected-COVID-19-samples-(respiratory-positive)_UP"
## [14] "bsdb:498/1/2_COVID-19:Symptomatic,-mild-to-moderate-COVID-19-patients_vs_Asymptomatic-uninfected-controls_DOWN"                
## [15] "bsdb:511/3/2_COVID-19:COVID-19-patients-not-treated-with-antibiotics_vs_Healthy-controls_DOWN"                                 
## [16] "bsdb:512/2/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls_UP"                                             
## [17] "bsdb:828/3/1_COVID-19,Respiratory-failure-requiring-assisted-ventilation:Intubated_vs_Non-intubated_UP"                        
## [18] "bsdb:940/1/1_COVID-19:Covid-19-group_vs_Control-group_UP"                                                                      
## [19] "bsdb:940/3/1_COVID-19:Post-Covid-19-group_vs_Control-group_UP"                                                                 
## [20] "bsdb:940/5/2_COVID-19:Covid-19-C-group_vs_Control-group_DOWN"                                                                  
## 
## [[3]]
##  [1] "bsdb:428/3/2_COVID-19:Recovered-COVID-19-cases_vs_Healthy-controls_DOWN"                                                                             
##  [2] "bsdb:453/2/1_COVID-19:SARS-CoV-2-recovered-individuals_vs_SARS-CoV-2-infected-patients_DOWN"                                                         
##  [3] "bsdb:458/1/2_COVID-19:COVID-19-patients_vs_Non-intubated-patients-with-non-incubation-viral-pneumonia-or-non-incubation-non-infectious-diseases_DOWN"
##  [4] "bsdb:478/2/1_COVID-19:COVID-19-patients_vs_Flu-patients_DOWN"                                                                                        
##  [5] "bsdb:479/1/1_COVID-19:ICU-COVID-19-patients_vs_Healthy-controls_DOWN"                                                                                
##  [6] "bsdb:479/4/2_COVID-19:ICU-COVID-19-patients_vs_Patients-infected-with-other-human-coronaviruses_UP"                                                  
##  [7] "bsdb:481/2/1_COVID-19:Antibiotic-treated-COVID-19-patients_vs_Antibiotic-naive-COVID-19-patients_UP"                                                 
##  [8] "bsdb:485/2/2_COVID-19:COVID-19-patients-with-fever_vs_COVID-19-patients-without-fever_DOWN"                                                          
##  [9] "bsdb:487/2/1_COVID-19:Recovered-COVID-19-samples-(respiratory-negative)_vs_Healthy-controls_DOWN"                                                    
## [10] "bsdb:782/3/1_COVID-19:Feces-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_UP"                                                      
## [11] "bsdb:940/2/2_COVID-19:Post-Covid-19-group_vs_Covid-19-group_DOWN"                                                                                    
## [12] "bsdb:940/4/2_COVID-19:Covid-19-N-group_vs_Control-group_DOWN"                                                                                        
## 
## [[4]]
##  [1] "bsdb:428/4/1_COVID-19:COVID-19-patients-treated-with-antibiotics_vs_Healthy-controls_DOWN"                                     
##  [2] "bsdb:428/4/2_COVID-19:COVID-19-patients-treated-with-antibiotics_vs_Healthy-controls_UP"                                       
##  [3] "bsdb:430/1/2_COVID-19:Patients-with-high-SARS-CoV-2-infectivity_vs_Patients-with-low-to-none-SARS-CoV-2-infectivity_UP"        
##  [4] "bsdb:453/1/2_COVID-19:SARS-CoV-2-infected-patients_vs_Non-infected-patients-with-unrelated-respiratory-medical-conditions_DOWN"
##  [5] "bsdb:453/4/2_COVID-19:Positive-for-COVID-19-viral-RNA-in-feces_vs_Negative-for-COVID-19-viral-RNA-in-feces_DOWN"               
##  [6] "bsdb:464/2/1_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_DOWN"                                                    
##  [7] "bsdb:477/1/1_COVID-19:Severe-COVID-19-patients_vs_COVID-19-negative-controls_UP"                                               
##  [8] "bsdb:479/3/1_COVID-19:ICU-COVID-19-patients_vs_Mild-Moderate-COVID-19-patients_DOWN"                                           
##  [9] "bsdb:484/2/1_COVID-19:COVID-19-positive-patients_vs_Healthy-controls_UP"                                                       
## [10] "bsdb:498/1/1_COVID-19:Symptomatic,-mild-to-moderate-COVID-19-patients_vs_Asymptomatic-uninfected-controls_UP"                  
## [11] "bsdb:940/4/1_COVID-19:Covid-19-N-group_vs_Control-group_UP"                                                                    
## 
## [[5]]
##  [1] "bsdb:441/3/1_COVID-19:COVID-19-patients_vs_H1N1-patients_UP"                                           
##  [2] "bsdb:473/1/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls_DOWN"                   
##  [3] "bsdb:478/3/1_COVID-19:Flu-patients_vs_Healthy-controls_UP"                                             
##  [4] "bsdb:479/1/2_COVID-19:ICU-COVID-19-patients_vs_Healthy-controls_UP"                                    
##  [5] "bsdb:481/2/2_COVID-19:Antibiotic-treated-COVID-19-patients_vs_Antibiotic-naive-COVID-19-patients_DOWN" 
##  [6] "bsdb:484/4/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-patients-with-fever-and-cough_UP"
##  [7] "bsdb:500/1/1_COVID-19:COVID-19-patients_vs_COVID-19-negative-patients-with-pneumonia_UP"               
##  [8] "bsdb:511/2/1_COVID-19:COVID-19-patients-(severe/critical)_vs_COVID-19-patients-(mild/moderate)_DOWN"   
##  [9] "bsdb:831/1/2_COVID-19:COVID-19-Patients_vs_Non-Covid-19-Patients---pneumonia-patients_DOWN"            
## [10] "bsdb:940/5/1_COVID-19:Covid-19-C-group_vs_Control-group_UP"                                            
## 
## [[6]]
## [1] "bsdb:449/1/1_COVID-19:COVID-19-cases_vs_Healthy-controls_UP"                                                                 
## [2] "bsdb:453/2/2_COVID-19:SARS-CoV-2-recovered-individuals_vs_SARS-CoV-2-infected-patients_UP"                                   
## [3] "bsdb:459/1/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls_DOWN"                                         
## [4] "bsdb:478/5/1_COVID-19:Flu-patients_vs_Healthy-controls_DOWN"                                                                 
## [5] "bsdb:481/1/2_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                                            
## [6] "bsdb:486/1/2_COVID-19:Asymptomatic-COVID-19-patients_vs_Healthy-controls_DOWN"                                               
## [7] "bsdb:486/2/1_COVID-19:Asymptomatic-COVID-19-patients_vs_Healthy-controls_UP"                                                 
## [8] "bsdb:489/1/1_COVID-19:COVID-positive-patients-admitted-to-ICU-(i-COVID-19)_vs_Pneumonia-control-patients-(COVID-negative)_UP"
## [9] "bsdb:513/1/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls_UP"                                           
## 
## [[7]]
## [1] "bsdb:453/4/1_COVID-19:Positive-for-COVID-19-viral-RNA-in-feces_vs_Negative-for-COVID-19-viral-RNA-in-feces_UP"                                              
## [2] "bsdb:464/3/2_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                                                                             
## [3] "bsdb:476/1/2_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_DOWN"                                                                                 
## [4] "bsdb:489/2/1_COVID-19:COVID-positive-patients-admitted-to-ICU-(i-COVID-19)_vs_COVID-positive-patients-admitted-to-infectious-disease-ward-(w-COVID-19)_UP"  
## [5] "bsdb:489/2/2_COVID-19:COVID-positive-patients-admitted-to-ICU-(i-COVID-19)_vs_COVID-positive-patients-admitted-to-infectious-disease-ward-(w-COVID-19)_DOWN"
## [6] "bsdb:498/2/1_COVID-19:COVID-19-patients-with-high-viral-load_vs_COVID-19-patients-with-low-viral-load_UP"                                                   
## [7] "bsdb:546/1/2_COVID-19:COVID-19-cases_vs_Non-infected-controls_DOWN"                                                                                         
## 
## [[8]]
## [1] "bsdb:464/1/1_COVID-19:Confirmed-COVID-19-patients_vs_Healthy-controls_DOWN"                                                                    
## [2] "bsdb:478/4/2_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                                                                
## [3] "bsdb:479/3/2_COVID-19:ICU-COVID-19-patients_vs_Mild-Moderate-COVID-19-patients_UP"                                                             
## [4] "bsdb:483/2/1_COVID-19:Antibiotic-treated-COVID-19-patients_vs_Antibiotic-naive-COVID-19-patients,-pneumonia-patients,-uninfected-controls_DOWN"
## [5] "bsdb:485/1/2_COVID-19:COVID-19-patients-with-fever_vs_COVID-19-patients-without-fever_DOWN"                                                    
## [6] "bsdb:831/2/1_COVID-19:COVID-19-patients---Ventilated_vs_COVID-19-patients---Non-Ventilated_UP"                                                 
## 
## [[9]]
## [1] "bsdb:464/3/1_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                                                          
## [2] "bsdb:478/1/1_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                                                          
## [3] "bsdb:478/5/2_COVID-19:Flu-patients_vs_Healthy-controls_UP"                                                                                 
## [4] "bsdb:489/3/2_COVID-19:COVID-positive-patients-admitted-to-infectious-disease-ward-(w-COVID-19)_vs_Pneumonia-controls-(COVID-negative)_DOWN"
## [5] "bsdb:511/5/1_COVID-19:COVID-19-patients-not-treated-with-antibiotics_vs_Healthy-controls_UP"                                               
## [6] "bsdb:965/1/1_COVID-19:COVID-19-patients_vs_healthy-controls_UP"                                                                            
## 
## [[10]]
## [1] "bsdb:478/3/2_COVID-19:Flu-patients_vs_Healthy-controls_DOWN"                                         
## [2] "bsdb:479/4/1_COVID-19:ICU-COVID-19-patients_vs_Patients-infected-with-other-human-coronaviruses_DOWN"
## [3] "bsdb:482/1/1_COVID-19:Severe-COVID-19-patients_vs_Mild-COVID-19-patients_DOWN"                       
## [4] "bsdb:485/2/1_COVID-19:COVID-19-patients-with-fever_vs_COVID-19-patients-without-fever_UP"            
## 
## [[11]]
## [1] "bsdb:478/4/1_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                          
## [2] "bsdb:486/1/1_COVID-19:Asymptomatic-COVID-19-patients_vs_Healthy-controls_UP"                               
## [3] "bsdb:498/2/2_COVID-19:COVID-19-patients-with-high-viral-load_vs_COVID-19-patients-with-low-viral-load_DOWN"
## [4] "bsdb:511/3/1_COVID-19:COVID-19-patients-not-treated-with-antibiotics_vs_Healthy-controls_UP"               
## [5] "bsdb:511/4/2_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                          
## [6] "bsdb:547/1/1_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_UP"                                  
## [7] "bsdb:548/1/2_COVID-19:COVID-19-cases_vs_Healthy-controls_DOWN"                                             
## 
## [[12]]
## [1] "bsdb:486/2/2_COVID-19:Asymptomatic-COVID-19-patients_vs_Healthy-controls_DOWN"                           
## [2] "bsdb:511/1/1_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                          
## [3] "bsdb:512/1/1_COVID-19:COVID-19-positive-patients_vs_COVID-19-negative-controls-and-healthcare-workers_UP"
## [4] "bsdb:782/2/1_COVID-19:Sputum-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_UP"         
## [5] "bsdb:782/2/2_COVID-19:Sputum-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_DOWN"       
## [6] "bsdb:1040/1/2_COVID-19:RPs-(Recovered-Patients)_vs_HCs-(Healthy-Controls)_DOWN"                          
## 
## [[13]]
## [1] "bsdb:486/3/1_COVID-19:Severe-COVID-19-patients_vs_Healthy-controls_UP"                                                         
## [2] "bsdb:489/1/2_COVID-19:COVID-positive-patients-admitted-to-ICU-(i-COVID-19)_vs_Pneumonia-control-patients-(COVID-negative)_DOWN"
## [3] "bsdb:496/2/1_COVID-19:COVID-19-patients_vs_Seasonal-flu-patients_UP"                                                           
## [4] "bsdb:977/3/2_COVID-19:Covid-19-patients_vs_non-Covid-19-patients_DOWN"                                                         
## 
## [[14]]
## [1] "bsdb:486/3/2_COVID-19:Severe-COVID-19-patients_vs_Healthy-controls_DOWN"                                      
## [2] "bsdb:511/4/1_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                                               
## [3] "bsdb:965/2/2_COVID-19:COVID-19-patients_vs_NO-COVID-19-(Patients-with-diagnosis-different-from-COVID-19)_DOWN"
## [4] "bsdb:977/6/2_COVID-19:Hospitalized-patients_vs_local-controls_DOWN"                                           
## 
## [[15]]
## [1] "bsdb:487/2/2_COVID-19:Recovered-COVID-19-samples-(respiratory-negative)_vs_Healthy-controls_UP"       
## [2] "bsdb:487/3/1_COVID-19:Infected-COVID-19-samples-(respiratory-positive)_vs_Healthy-controls_DOWN"      
## [3] "bsdb:782/1/1_COVID-19:Pharyngeal-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_DOWN"
## [4] "bsdb:831/2/2_COVID-19:COVID-19-patients---Ventilated_vs_COVID-19-patients---Non-Ventilated_DOWN"      
## [5] "bsdb:977/6/1_COVID-19:Hospitalized-patients_vs_local-controls_UP"                                     
## 
## [[16]]
## [1] "bsdb:487/3/2_COVID-19:Infected-COVID-19-samples-(respiratory-positive)_vs_Healthy-controls_UP"
## [2] "bsdb:977/1/1_COVID-19:COVID-19-patients_vs_local-controls_UP"                                 
## [3] "bsdb:977/1/2_COVID-19:COVID-19-patients_vs_local-controls_DOWN"                               
## [4] "bsdb:1040/1/1_COVID-19:RPs-(Recovered-Patients)_vs_HCs-(Healthy-Controls)_UP"                 
## 
## [[17]]
## [1] "bsdb:489/3/1_COVID-19:COVID-positive-patients-admitted-to-infectious-disease-ward-(w-COVID-19)_vs_Pneumonia-controls-(COVID-negative)_UP"
## [2] "bsdb:496/2/2_COVID-19:COVID-19-patients_vs_Seasonal-flu-patients_DOWN"                                                                   
## [3] "bsdb:548/1/1_COVID-19:COVID-19-cases_vs_Healthy-controls_UP"                                                                             
## [4] "bsdb:782/3/2_COVID-19:Feces-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_DOWN"                                        
## 
## [[18]]
## [1] "bsdb:496/1/1_COVID-19:COVID-19-patients_vs_Healthy-controls_UP"                               
## [2] "bsdb:511/5/2_COVID-19:COVID-19-patients-not-treated-with-antibiotics_vs_Healthy-controls_DOWN"
## 
## [[19]]
## [1] "bsdb:496/1/2_COVID-19:COVID-19-patients_vs_Healthy-controls_DOWN"                                   
## [2] "bsdb:782/1/2_COVID-19:Pharyngeal-samples-from-COVID-19-patients_vs_samples-from-Healthy-patients_UP"
## [3] "bsdb:977/3/1_COVID-19:Covid-19-patients_vs_non-Covid-19-patients_UP"                                
## 
## [[20]]
## [1] "bsdb:500/1/2_COVID-19:COVID-19-patients_vs_COVID-19-negative-patients-with-pneumonia_DOWN"
## [2] "bsdb:547/1/2_COVID-19:Recovered-COVID-19-patients_vs_Healthy-controls_DOWN"               
## 
## [[21]]
## [1] "bsdb:513/2/1_COVID-19:COVID-19-positive-patients-with-breathing-assistance_vs_COVID-19-positive-patients-without-breathing-assistance_UP"
## [2] "bsdb:965/1/2_COVID-19:COVID-19-patients_vs_healthy-controls_DOWN"

Create a wide-format dataframe

This would be suitable for regression analysis.

covid_withsigs <- filter(covid_all, !is.na(covid_all$`NCBI Taxonomy IDs`))
sigs <- bugsigdbr::getSignatures(covid_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
## 170 categories formed

cdf <- data.frame(cmat, stringsAsFactors = FALSE, check.names = FALSE)
cdf <- cbind(covid_withsigs, cdf)
colnames(cdf)[1:54]

##  [1] "BSDB ID"                    "Study"                     
##  [3] "Study design"               "PMID"                      
##  [5] "DOI"                        "URL"                       
##  [7] "Authors list"               "Title"                     
##  [9] "Journal"                    "Year"                      
## [11] "Keywords"                   "Experiment"                
## [13] "Location of subjects"       "Host species"              
## [15] "Body site"                  "UBERON ID"                 
## [17] "Condition"                  "EFO ID"                    
## [19] "Group 0 name"               "Group 1 name"              
## [21] "Group 1 definition"         "Group 0 sample size"       
## [23] "Group 1 sample size"        "Antibiotics exclusion"     
## [25] "Sequencing type"            "16S variable region"       
## [27] "Sequencing platform"        "Statistical test"          
## [29] "Significance threshold"     "MHT correction"            
## [31] "LDA Score above"            "Matched on"                
## [33] "Confounders controlled for" "Pielou"                    
## [35] "Shannon"                    "Chao1"                     
## [37] "Simpson"                    "Inverse Simpson"           
## [39] "Richness"                   "Signature page name"       
## [41] "Source"                     "Curated date"              
## [43] "Curator"                    "Revision editor"           
## [45] "Description"                "Abundance in Group 1"      
## [47] "MetaPhlAn taxon names"      "NCBI Taxonomy IDs"         
## [49] "State"                      "Reviewer"                  
## [51] "studyexp"                   "site"                      
## [53] "comparison1"                "[Clostridium] colinum"

Note this has a number of columns that are mostly zeros, it could be filtered significantly for any regression or machine learning analysis:

table(cdf[["[Brevibacterium] frigoritolerans"]])

## < table of extent 0 >

Create another heatmap on correlations of presence/absence of taxa. This is not necessary because the previous Jaccard Index heatmap is probably better, it is just a demonstration of doing something with the taxa presence/absence directly.

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(20, "cm"),
  column_names_max_height = unit(20, "cm"),
  row_labels = sub(".+:", "", rownames(sigcors)), ##removing study just to make signature names legible
  column_labels = sub(".+:", "", colnames(sigcors))
)

## The automatically generated colors map from the 1^st and 99^th of the
## values in the matrix. There are outliers in the matrix whose patterns
## might be hidden by this color mapping. You can manually set the color
## to `col` argument.
## 
## Use `suppressMessages()` to turn off this message.

hm

Use this interactively to make an interactive heatmap:

library(InteractiveComplexHeatmap)
hm <- draw(hm)
htShiny(hm)

Clare Grieve

30 January, 2025