PhILR is short for “Phylogenetic Isometric Log-Ratio Transform” (Silverman et al. 2017). This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure (Egozcue and Pawlowsky-Glahn 2016).
The goal of PhILR is to transform compositional data into an orthogonal unconstrained space (real space) with phylogenetic / evolutionary interpretation while preserving all information contained in the original composition. Unlike in the original compositional space, in the transformed real space, standard statistical tools may be applied. For a given set of samples consisting of measurements of taxa, we transform data into a new space of samples and orthonormal coordinates termed ‘balances’. Each balance is associated with a single internal node of a phylogenetic tree with the taxa as leaves. The balance represents the log-ratio of the geometric mean abundance of the two groups of taxa that descend from the given internal node. More details on this method can be found in Silverman et al. (2017) (Link).
Here we will demonstrate PhILR analysis using the Global Patterns dataset that
was originally published by Caporaso et al. (2011). This dataset is provided with the
phyloseq package (McMurdie and Holmes 2013) and our analysis follows, in part, that of the authors
##  '1.18.0'
##  '1.36.0'
##  '5.5'
##  '3.3.3'
Taxa that were not seen with more than 3 counts in at least 20% of samples are filtered. Subsequently, those witha coefficient of variation ≤ 3 are filtered. These steps follow those of (McMurdie and Holmes 2013). Finally we add a pseudocount of 1 to the remaining OTUs to avoid calculating log-ratios involving zeros. Alternatively other replacement methods (multiplicative replacement etc…) may be used instead if desired; the subsequent taxa weighting procedure we will describe complements a variety of zero replacement methods.
GP <- filter_taxa(GlobalPatterns, function(x) sum(x > 3) > (0.2*length(x)), TRUE) GP <- filter_taxa(GP, function(x) sd(x)/mean(x) > 3.0, TRUE) GP <- transform_sample_counts(GP, function(x) x+1)
With these two commands we have removed the filtered taxa from the OTU table, pruned the phylogenetic tree, and subset the taxa table. Here is the result of those filtering steps
## phyloseq-class experiment-level object ## otu_table() OTU Table: [ 1248 taxa and 26 samples ] ## sample_data() Sample Data: [ 26 samples by 7 sample variables ] ## tax_table() Taxonomy Table: [ 1248 taxa by 7 taxonomic ranks ] ## phy_tree() Phylogenetic Tree: [ 1248 tips and 1247 internal nodes ]
Next we check that the tree is rooted and binary (all multichotomies have been resolved).
is.rooted(phy_tree(GP)) # Is the tree Rooted?
##  TRUE
is.binary.tree(phy_tree(GP)) # All multichotomies resolved?
##  TRUE
Note that if the tree is not binary, the function
multi2di from the
can be used to replace multichotomies with a series of dichotomies with one (or several)
branch(es) of zero length .
Once this is done, we name the internal nodes of the tree so they
are easier to work with. We prefix the node number with
n and thus the root
phy_tree(GP) <- makeNodeLabel(phy_tree(GP), method="number", prefix='n')
We note that the tree is already rooted with Archea as the outgroup and
no multichotomies are present. This uses the function
name.balance from the
package. This function uses a simple voting scheme to find a consensus naming
for the two clades that descend from a given balance. Specifically for a
x refers to the consensus name of the clade in the numerator
of the log-ratio and
y refers to the denominator.
name.balance(phy_tree(GP), tax_table(GP), 'n1')
##  "Kingdom_Archaea/Kingdom_Bacteria"
Finally we transpose the OTU table (
philr uses the conventions of the
package for compositional data analysis in R, taxa are columns, samples are rows).
Then we will take a look at part of the dataset in more detail
otu.table <- t(otu_table(GP)) tree <- phy_tree(GP) metadata <- sample_data(GP) tax <- tax_table(GP) otu.table[1:2,1:2] # OTU Table
## OTU Table: [2 taxa and 2 samples] ## taxa are columns ## 540305 108964 ## CL3 1 1 ## CC1 1 2
tree # Phylogenetic Tree
## ## Phylogenetic tree with 1248 tips and 1247 internal nodes. ## ## Tip labels: ## 540305, 108964, 175045, 546313, 54107, 71074, ... ## Node labels: ## n1, n2, n3, n4, n5, n6, ... ## ## Rooted; includes branch lengths.
head(metadata,2) # Metadata
## X.SampleID Primer Final_Barcode Barcode_truncated_plus_T ## CL3 CL3 ILBC_01 AACGCA TGCGTT ## CC1 CC1 ILBC_02 AACTCG CGAGTT ## Barcode_full_length SampleType Description ## CL3 CTAGCGTGCGT Soil Calhoun South Carolina Pine soil, pH 4.9 ## CC1 CATCGACGAGT Soil Cedar Creek Minnesota, grassland, pH 6.1
head(tax,2) # taxonomy table
## Taxonomy Table: [2 taxa by 7 taxonomic ranks]: ## Kingdom Phylum Class Order ## 540305 "Archaea" "Crenarchaeota" "Thaumarchaeota" "Cenarchaeales" ## 108964 "Archaea" "Crenarchaeota" "Thaumarchaeota" "Cenarchaeales" ## Family Genus Species ## 540305 "Cenarchaeaceae" NA NA ## 108964 "Cenarchaeaceae" "Nitrosopumilus" "pIVWA5"
philr::philr() implements a user friendly wrapper for the key
steps in the philr transform.
philr::miniclo()) and ‘shift’ dataset using the weightings (Egozcue and Pawlowsky-Glahn 2016) using the function
Note: The preprocessed OTU table should be passed
to the function
philr::philr() before it is closed (normalized) to relative abundances, as
some of the preset weightings of the taxa use the original count data to down weight low
Here we will use the same weightings as we used in the main paper.
gp.philr <- philr(otu.table, tree, part.weights='enorm.x.gm.counts', ilr.weights='blw.sqrt')
## Building Sequential Binary Partition from Tree...
## Building Contrast Matrix...
## Transforming the Data...
## Calculating ILR Weights...
## n1 n2 n3 n4 n5 ## CL3 -1.3638521 1.9756259 2.6111996 -3.3174292 0.08335109 ## CC1 -0.9441168 3.9054807 2.9804522 -4.7771598 -0.05334306 ## SV1 5.8436901 5.9067782 6.7315081 -8.8020849 0.08335109 ## M31Fcsw -3.9010427 -0.1816618 -0.5432099 0.1705271 0.08335109 ## M11Fcsw -5.4554073 0.5398249 -0.5647474 0.5551616 -0.02389182
Now the transformed data is represented in terms of balances and since
each balance is associated with a single internal node of the tree, we denote the balances
using the same names we assigned to the internal nodes (e.g.,
Euclidean distance in PhILR space can be used for ordination analysis. We
do this ordination using tools from the
gp.dist <- dist(gp.philr, method="euclidean") gp.pcoa <- ordinate(GP, 'PCoA', distance=gp.dist) plot_ordination(GP, gp.pcoa, color='SampleType') + geom_point(size=4)
More than just ordination analysis, PhILR provides an entire coordinate system
in which standard multivariate tools can be used. Here we will make use of sparse
logistic regression (from the
glmnet pacakge) to identify a small number of
balances that best distinguish human from non-human samples.
First we will make a new variable distinguishing human/non-human
sample_data(GP)$human <- factor(get_variable(GP, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue"))
Now we will fit a sparse logistic regression model (logistic regression with \(l_1\) penalty)
##  '4.1.1'
glmmod <- glmnet(gp.philr, sample_data(GP)$human, alpha=1, family="binomial")
We will use a hard-threshold for the \(l_1\) penalty of \(\lambda = 0.2526\) which we choose so that the resulting number of non-zero coefficients is \(\approx 5\) (for easy of visualization in this tutorial).
top.coords <- as.matrix(coefficients(glmmod, s=0.2526)) top.coords <- rownames(top.coords)[which(top.coords != 0)] (top.coords <- top.coords[2:length(top.coords)]) # remove the intercept as a coordinate
##  "n16" "n106" "n122" "n188" "n730"
To find the taxonomic labels that correspond to these balances we can use the function
philr::name.balance(). This funciton uses a simple voting scheme to name
the two descendent clades of a given balance separately. For a given clade,
the taxonomy table is subset to only contain taxa from that clade. Starting
at the finest taxonomic rank (e.g., species) the subset taxonomy table is checked to see
if any label (e.g., species name) represents ≥ threshold (default 95%) of
the table entries at that taxonomic rank. If no consensus identifier is found, the table is checked at the next-most specific taxonomic rank (etc…).
tc.names <- sapply(top.coords, function(x) name.balance(tree, tax, x)) tc.names
## n16 ## "Kingdom_Bacteria/Phylum_Firmicutes" ## n106 ## "Order_Actinomycetales/Order_Actinomycetales" ## n122 ## "Kingdom_Bacteria/Phylum_Cyanobacteria" ## n188 ## "Genus_Campylobacter/Phylum_Proteobacteria" ## n730 ## "Order_Bacteroidales/Order_Bacteroidales"
We can also get more information on what goes into the naming by viewing the votes directly.
votes <- name.balance(tree, tax, 'n730', return.votes = c('up', 'down')) votes[[c('up.votes', 'Family')]] # Numerator at Family Level
## votes ## Porphyromonadaceae ## 1
votes[[c('down.votes', 'Family')]] # Denominator at Family Level
## votes ## Bacteroidaceae Porphyromonadaceae Prevotellaceae Rikenellaceae ## 12 9 10 5
##  '3.0.0'
##  '1.0.6'
Above we found the top 5 coordinates (balances) that distinguish whether a
sample is from a human or non-human source. Now using the
ggtree (Yu et al. 2016) package we
can visualize these balances on the tree using the
To use these functions we need to know the acctual node number (not just the names we have given) of these balances
on the tree. To convert between node number and name, we have added the functions
In addition, it is important that we know which clade of the balance is in the numerator (+) and which is in the denominator (-) of the log-ratio. To help us keep track we have created the function
philr::annotate_balance() which allows us to easily
label these two clades.
tc.nn <- name.to.nn(tree, top.coords) tc.colors <- c('#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99') p <- ggtree(tree, layout='fan') + geom_balance(node=tc.nn, fill=tc.colors, alpha=0.6) + geom_balance(node=tc.nn, fill=tc.colors, alpha=0.6) + geom_balance(node=tc.nn, fill=tc.colors, alpha=0.6) + geom_balance(node=tc.nn, fill=tc.colors, alpha=0.6) + geom_balance(node=tc.nn, fill=tc.colors, alpha=0.6) p <- annotate_balance(tree, 'n16', p=p, labels = c('n16+', 'n16-'), offset.text=0.15, bar=FALSE) annotate_balance(tree, 'n730', p=p, labels = c('n730+', 'n730-'), offset.text=0.15, bar=FALSE)
We can also view the distribution of these 5 balances for human/non-human sources.
In order to plot with
ggplot2 we first need to convert the PhILR transformed
data to long format. We have included a function
gp.philr.long <- convert_to_long(gp.philr, get_variable(GP, 'human')) %>% filter(coord %in% top.coords) ggplot(gp.philr.long, aes(x=labels, y=value)) + geom_boxplot(fill='lightgrey') + facet_grid(.~coord, scales='free_x') + xlab('Human') + ylab('Balance Value') + theme_bw()
Lets just look at balance n16 vs. balance n730 (the ones we annotated in the above tree).
##  '1.1.3'
gp.philr.long %>% rename(Human=labels) %>% filter(coord %in% c('n16', 'n730')) %>% spread(coord, value) %>% ggplot(aes(x=n16, y=n730, color=Human)) + geom_point(size=4) + xlab(tc.names['n16']) + ylab(tc.names['n730']) + theme_bw()
Caporaso, J Gregory, Christian L Lauber, William A Walters, Donna Berg-Lyons, Catherine A Lozupone, Peter J Turnbaugh, Noah Fierer, and Rob Knight. 2011. “Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences Per Sample.” Journal Article. Proceedings of the National Academy of Sciences 108: 4516–22.
Egozcue, J. J., and V. Pawlowsky-Glahn. 2016. “Changing the Reference Measure in the Simlex and Its Weightings Effects.” Journal Article. Austrian Journal of Statistics 45 (4): 25–44.
McMurdie, P. J., and S. Holmes. 2013. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” Journal Article. PLoS One 8 (4): e61217. https://doi.org/10.1371/journal.pone.0061217.
Silverman, Justin D, Alex D Washburne, Sayan Mukherjee, and Lawrence A David. 2017. “A Phylogenetic Transform Enhances Analysis of Compositional Microbiota Data.” eLife 6. https://doi.org/10.7554/eLife.21887.
Yu, Guangchuang, David K Smith, Huachen Zhu, Yi Guan, and Tommy Tsan‐Yuk Lam. 2016. “Ggtree: An R Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data.” Journal Article. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.12628.