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] 171

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 0.9993667
Aquabacterium Bradyrhizobium 0.9992331
Methylobacterium Phyllobacterium 0.9991739
Aquabacterium Phyllobacterium 0.9986580
Bradyrhizobium Phyllobacterium 0.9985600
Weissella Veillonella 0.9964041
Veillonella Peptostreptococcus 0.9942439
Weissella Peptostreptococcus 0.9919486
Veillonella Campylobacter 0.9838293

…and the bottom:

tail(mCorr[order(mCorr$val, decreasing = TRUE),],10) %>% knitr::kable()
col row val
12098 Fusicatenibacter Acidaminococcus -0.4088233
767 Akkermansia Pseudobutyrivibrio -0.4100106
1315 Blautia Akkermansia -0.4135916
6814 Papillibacter Collinsella -0.4206363
776 Akkermansia Dorea -0.4277877
755 Akkermansia Clostridium -0.4364484
8413 Anaerovorax Collinsella -0.4399600
769 Akkermansia Collinsella -0.4458351
1880 Terrisporobacter Oscillibacter -0.5185039
747 Akkermansia Roseburia -0.5594010

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] 140
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 0.9992074
Aerococcus Solobacterium 0.9971287
Ochrobactrum Delftia 0.9967128
Actinobaculum Solobacterium 0.9963677
Actinobaculum Pseudomonas 0.9962911
Actinobaculum Pyramidobacter 0.9960798
mCorr.people %>% arrange(desc(val)) %>% tail() %>% knitr::kable()
col row val
9725 Subdoligranulum Bacteroides -0.3237482
9726 Faecalibacterium Bacteroides -0.3260428
9727 Sarcina Bacteroides -0.3323116
9728 Bilophila Roseburia -0.3430269
9729 Faecalibacterium Collinsella -0.3446945
9730 Peptococcus Blautia -0.4095067

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] "Acidaminococcus"     "Arthrobacter"        "Oligella"           
##  [4] "Achromobacter"       "Stenotrophomonas"    "Ralstonia"          
##  [7] "Shinella"            "Neisseria"           "Actinobacillus"     
## [10] "Pasteurella"         "Rothia"              "Johnsonella"        
## [13] "Aggregatibacter"     "Alloprevotella"      "Phyllobacterium"    
## [16] "Acinetobacter"       "Acetanaerobacterium" "Planomicrobium"     
## [19] "Pediococcus"         "Anaerovorax"         "Tissierella"        
## [22] "Pantoea"             "Bradyrhizobium"      "Methylobacterium"   
## [25] "Sphingomonas"        "Aquabacterium"       "Pelomonas"          
## [28] "Christensenella"     "Anaerobacter"        "Azospira"           
## [31] "Trueperella"         "Parasporobacterium"  "Raoultella"         
## [34] "Hafnia"              "Rahnella"            "Sedimentibacter"    
## [37] "Tessaracoccus"       "Caldicoprobacter"    "Geobacillus"        
## [40] "Cronobacter"         "Anaerobacterium"     "Coprobacillus"      
## [43] "Desulfitibacter"     "Proteiniphilum"
setdiff(rownames(people.mat),rownames(gut.mat))
##  [1] "Olsenella"        "Megamonas"        "Negativicoccus"  
##  [4] "Senegalimassilia" "Dermabacter"      "Eremococcus"     
##  [7] "Parvibacter"      "Butyricicoccus"   "Syntrophococcus" 
## [10] "Howardella"       "Anaeroglobus"     "Actinobaculum"   
## [13] "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 Dorea 0.3072171
Blautia Pseudobutyrivibrio 0.2017246
Blautia Subdoligranulum 0.2017201
Blautia Anaerostipes 0.1857530
Blautia Marvinbryantia 0.1815135
Blautia Anaerotruncus 0.1440489
mCorr.people %>% dplyr::filter(col=="Blautia") %>% arrange(val) %>% head() %>% knitr::kable()
col row val
Blautia Oscillibacter -0.2873210
Blautia Oscillospira -0.2739586
Blautia Sarcina -0.2736859
Blautia Odoribacter -0.2587790
Blautia Butyrivibrio -0.2456773
Blautia Porphyromonas -0.2342377

and just in me:

mCorr %>% dplyr::filter(col=="Blautia") %>% arrange(desc(val)) %>% head() %>% knitr::kable()
col row val
Blautia Dorea 0.6254313
Blautia Collinsella 0.5464608
Blautia Anaerostipes 0.5218580
Blautia Pseudobutyrivibrio 0.4469536
Blautia Hespellia 0.4286412
Blautia Roseburia 0.3600341
mCorr %>% dplyr::filter(col=="Blautia") %>% arrange(val) %>% head() %>% knitr::kable()
col row val
Blautia Akkermansia -0.4135916
Blautia Thalassospira -0.3097967
Blautia Barnesiella -0.3083497
Blautia Alistipes -0.2797115
Blautia Bacteroides -0.2209333
Blautia Bilophila -0.1994229

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.