Richard Sprague

My personal website

Which microbes go with each other?

2018-04-08


Microbes in the body are always part of a community, an ecology of interdependent organisms, some rising, some falling as the environment changes. By watching the shifts over time, I would expect to see some patterns: microbes that consume that same kinds of nutrients may go up and down together as the amounts of those nutrients change. Similarly, a microbe that depends on some waste product secreted by another organism should go up or down depending on the abundance levels of the other organism.

To find patterns, I’ll line up the abundances of every sample I’ve taken, then run a simple correlation analysis to see which microbe levels are most highly-correlated.

Prerequisites: The following example is written in R and I’ve written an R package, actino that converts between uBiome raw data files and Phyloseq, an excellent microbiome analysis tool developed at Stanford’s Bioconductor program.

I start with a Phyloseq object called gut.best that contains the normalized abundances of all my gut microbes. If you follow the actino directions, you should be able to create a similar object for your own data.

Then simply turn that Phyloseq object into a single matrix:

gut.mat <- as.matrix(otu_table(gut.best))

But some taxa are quite rare, occuring in just a few out of hundreds of samples. Let’s ignore any sample where a taxa has fewer than 10 reads; finally, of those that remain, let’s arbitrarily eliminate any taxa that occur fewer than five times. Here we show the resulting total number of samples.

g <- apply(gut.mat,1,function(x) ncol(gut.mat)-sum(x<10)) >= 5
gut.mat <- gut.mat[g,]
nrow(gut.mat)
## [1] 175

Now run the correlations, showing the top 10…

matCorrs<-cor(t(gut.mat)) # matrix of all correlation coefficients
mc<-matCorrs[upper.tri(matCorrs)] # just the upper triangle

ind <- which( upper.tri(matCorrs,diag=F) , arr.ind = TRUE )

mCorr<-data.frame( col = dimnames(matCorrs)[[2]][ind[,2]] ,
                   row = dimnames(matCorrs)[[1]][ind[,1]] ,
                   val = matCorrs[ ind ] )

#head(mCorr[order(mCorr$val, decreasing = TRUE),],10)

mCorr %>% arrange(desc(val)) %>% head(10) %>% knitr::kable()
col row val
Aquabacterium Methylobacterium 0.9995392
Methylobacterium Bradyrhizobium 0.9993668
Aquabacterium Bradyrhizobium 0.9992331
Methylobacterium Phyllobacterium 0.9991739
Aquabacterium Phyllobacterium 0.9986580
Bradyrhizobium Phyllobacterium 0.9985601
Weissella Veillonella 0.9963728
Veillonella Peptostreptococcus 0.9942445
Weissella Peptostreptococcus 0.9919200
Veillonella Campylobacter 0.9838099

…and the bottom:

tail(mCorr[order(mCorr$val, decreasing = TRUE),],10) %>% knitr::kable()
col row val
1354 Butyricimonas Collinsella -0.3918725
767 Akkermansia Pseudobutyrivibrio -0.4038954
1315 Blautia Akkermansia -0.4052601
776 Akkermansia Dorea -0.4124544
12254 Fusicatenibacter Acidaminococcus -0.4151745
6814 Papillibacter Collinsella -0.4302741
769 Akkermansia Collinsella -0.4424045
8413 Anaerovorax Collinsella -0.4579187
1880 Terrisporobacter Oscillibacter -0.5092867
747 Akkermansia Roseburia -0.5622769

For fun, let’s run the same correlation on other people. I have a private collection of a few hundred samples that others have sent me, stored in the Phyloseq object people.norm. Let’s run the above calculations on those samples to see if the results are similar

people.gut <- subset_samples(people.norm, Site == "gut" & Reads > 10000 & Condition == "Healthy")
people.best <- prune_taxa(taxa_sums(people.gut)>42,people.gut) 
people.mat <- as.matrix(otu_table(people.best))

g <- apply(people.mat,1,function(x) ncol(people.mat)-sum(x<10)) >= 5
people.mat <- people.mat[g,]

nrow(people.mat)
## [1] 153
matCorrs<-cor(t(people.mat)) # matrix of all correlation coefficients
mc<-matCorrs[upper.tri(matCorrs)] # just the upper triangle

ind <- which( upper.tri(matCorrs,diag=F) , arr.ind = TRUE )

mCorr.people<-data.frame( col = dimnames(matCorrs)[[2]][ind[,2]] ,
                   row = dimnames(matCorrs)[[1]][ind[,1]] ,
                   val = matCorrs[ ind ] )

# head(mCorr.people[order(mCorr$val, decreasing = TRUE),],10)
# tail(mCorr.people[order(mCorr$val, decreasing = TRUE),],10)

Here are the most and least-correlated taxa for all people:

mCorr.people %>% arrange(desc(val)) %>% head() %>% knitr::kable()
col row val
Aerococcus Actinobaculum 0.9991360
Aerococcus Solobacterium 0.9971308
Ochrobactrum Delftia 0.9967135
Solobacterium Actinobaculum 0.9962896
Pseudomonas Actinobaculum 0.9962011
Pyramidobacter Actinobaculum 0.9959956
mCorr.people %>% arrange(desc(val)) %>% tail() %>% knitr::kable()
col row val
11623 Faecalibacterium Collinsella -0.3247328
11624 Erysipelatoclostridium Sarcina -0.3261649
11625 Faecalibacterium Bacteroides -0.3268779
11626 Subdoligranulum Bacteroides -0.3305231
11627 Bilophila Roseburia -0.3384460
11628 Blautia Peptococcus -0.3774534

There are more unique taxa in the sample of people than there are in me. That makes sense, since you’d expect more diversity amount lots of people. Here are the taxa that are in me but not in people.best:

setdiff(rownames(gut.mat),rownames(people.mat))
##  [1] "Oligella"           "Achromobacter"      "Stenotrophomonas"  
##  [4] "Ralstonia"          "Shinella"           "Neisseria"         
##  [7] "Actinobacillus"     "Pasteurella"        "Rothia"            
## [10] "Johnsonella"        "Phyllobacterium"    "Acinetobacter"     
## [13] "Planomicrobium"     "Pediococcus"        "Tissierella"       
## [16] "Bradyrhizobium"     "Methylobacterium"   "Methanosphaera"    
## [19] "Sphingomonas"       "Aquabacterium"      "Pelomonas"         
## [22] "Anaerobacter"       "Azospira"           "Trueperella"       
## [25] "Parasporobacterium" "Raoultella"         "Hafnia"            
## [28] "Rahnella"           "Sedimentibacter"    "Tessaracoccus"     
## [31] "Fretibacterium"     "Caldicoprobacter"   "Geobacillus"       
## [34] "Anaerobacterium"    "Coprobacillus"      "Desulfitibacter"   
## [37] "Enorma"             "Proteiniphilum"
setdiff(rownames(people.mat),rownames(gut.mat))
##  [1] "Negativicoccus"   "Howardella"       "Acetivibrio"     
##  [4] "Syntrophococcus"  "Cellulosilyticum" "Parvibacter"     
##  [7] "Senegalimassilia" "Dermabacter"      "Actinobaculum"   
## [10] "Eremococcus"      "Alloscardovia"    "Olsenella"       
## [13] "Megamonas"        "Butyricicoccus"   "Anaeroglobus"    
## [16] "Aerococcus"

Let’s see if a few common taxa are similarly correlated:

Here are the microbes that are most and least correlated with Blautia

in all people:

mCorr.people %>% dplyr::filter(col=="Blautia") %>% arrange(desc(val)) %>% head() %>% knitr::kable()
col row val
Blautia Anaerostipes 0.1948457
Blautia Marvinbryantia 0.1726856
Blautia Dorea 0.1704917
Blautia Pseudobutyrivibrio 0.1689799
Blautia Subdoligranulum 0.1615216
Blautia Anaerotruncus 0.1592456
mCorr.people %>% dplyr::filter(col=="Blautia") %>% arrange(val) %>% head() %>% knitr::kable()
col row val
Blautia Peptococcus -0.3774534
Blautia Sarcina -0.3065172
Blautia Oscillibacter -0.2976314
Blautia Turicibacter -0.2800823
Blautia Odoribacter -0.2701173
Blautia Oscillospira -0.2571697

and just in me:

mCorr %>% dplyr::filter(col=="Blautia") %>% arrange(desc(val)) %>% head() %>% knitr::kable()
col row val
Blautia Dorea 0.6203389
Blautia Collinsella 0.5370734
Blautia Anaerostipes 0.5112434
Blautia Pseudobutyrivibrio 0.4472298
Blautia Hespellia 0.4282319
Blautia Roseburia 0.3585946
mCorr %>% dplyr::filter(col=="Blautia") %>% arrange(val) %>% head() %>% knitr::kable()
col row val
Blautia Akkermansia -0.4052601
Blautia Thalassospira -0.3045619
Blautia Barnesiella -0.2974186
Blautia Alistipes -0.2718027
Blautia Bacteroides -0.2182295
Blautia Odoribacter -0.1976182

Interestingly, at least among the top microbes, there does seem to be some agreement (Blautia - Dorea, Blautia Anaerostipes). Just a coincidence? Hmm..

If I can think of a better way to present this information – or if you have any suggestions for me – I’ll update this post.