--- title: "Introduction to PhILR" author: "Justin Silverman" date: "`r Sys.Date()`" bibliography: philr.bib output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{Introduction to PhILR} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Introduction PhILR is short for "Phylogenetic Isometric Log-Ratio Transform" [@silverman2017]. This package provides functions for the analysis of [compositional data](https://en.wikipedia.org/wiki/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 [@egozcue2016]. # Overview of PhILR Analysis 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 @silverman2017 ([Link](https://elifesciences.org/content/6/e21887)). The analysis uses abundance table and a phylogenetic tree. These can be provided as separate data objects, or embedded in standard R/Bioconductor data containers. The philr R package supports two alternative data containers for microbiome data, `TreeSE` [@huang2021] and `phyloseq` [@mcmurdie2013]. # Loading and Preprocessing Dataset We demonstrate PhILR analysis by using the Global Patterns dataset that was originally published by @caporaso2011. Let us first load necessary libraries. ```{r, echo=TRUE, message=FALSE} library(philr); packageVersion("philr") library(ape); packageVersion("ape") library(ggplot2); packageVersion("ggplot2") ``` # Data preparation: TreeSE We show the GlobalPatterns example workflow as initially outlined in [@mcmurdie2013]. We retrieve the example data in `TreeSummarizedExperiment` (`TreeSE`) data format in this vignette [@huang2021], and then show example also for the [phyloseq](philr-intro-phyloseq.md) format. The TreeSE version for the GlobalPatterns data is provided with the [`mia` package](microbiome.github.io/mia) [@lahti2020]. Let us load the data. ```{r, echo=TRUE, message=FALSE} library(mia); packageVersion("mia") library(dplyr); packageVersion("dplyr") data(GlobalPatterns, package = "mia") ``` ## Filter Extremely Low-Abundance OTUs Taxa that were not seen with more than 3 counts in at least 20% of samples are filtered. Subsequently, those with a coefficient of variation ≤ 3 are filtered. 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. ```{r, message=FALSE, warning=FALSE} ## Select prevalent taxa tse <- GlobalPatterns %>% subsetByPrevalentTaxa( detection = 3, prevalence = 20/100, as_relative = FALSE) ## Pick taxa that have notable abundance variation across sammples variable.taxa <- apply(assays(tse)$counts, 1, function(x) sd(x)/mean(x) > 3.0) tse <- tse[variable.taxa,] # Collapse the tree! # Otherwise the original tree with all nodes is kept # (including those that were filtered out from rowData) tree <- ape::keep.tip(phy = rowTree(tse), tip = rowLinks(tse)$nodeNum) rowTree(tse) <- tree ## Add a new assay with a pseudocount assays(tse)$counts.shifted <- assays(tse)$counts + 1 ``` We have now 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. ```{r, echo=FALSE} tse ``` ## Process Phylogenetic Tree Next we check that the tree is rooted and binary (all multichotomies have been resolved). ```{r, message=FALSE, warning=FALSE} library(ape); packageVersion("ape") is.rooted(tree) # Is the tree Rooted? is.binary(tree) # All multichotomies resolved? ``` Note that if the tree is not binary, the function `multi2di` from the `ape` package 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 is named `n1`. ```{r, message=FALSE, warning=FALSE} tree <- makeNodeLabel(tree, method="number", prefix='n') # Add the modified tree back to the (`TreeSE`) data object rowTree(tse) <- tree ``` 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 `philr` 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 balance named `x/y`, `x` refers to the consensus name of the clade in the numerator of the log-ratio and `y` refers to the denominator. ```{r} # Extract taxonomy table from the TreeSE object tax <- rowData(tse)[,taxonomyRanks(tse)] # Get name balances name.balance(tree, tax, 'n1') ``` ## Investigate Dataset Components Finally we transpose the OTU table (`philr` uses the conventions of the `compositions` 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. ```{r} otu.table <- t(as(assays(tse)$counts.shifted, "matrix")) tree <- rowTree(tse) metadata <- colData(tse) tax <- rowData(tse)[,taxonomyRanks(tse)] otu.table[1:2,1:2] # OTU Table tree # Phylogenetic Tree head(metadata,2) # Metadata head(tax,2) # taxonomy table ``` A new variable distinguishing human/non-human: ```{r} human.samples <- factor(colData(tse)$SampleType %in% c("Feces", "Mock", "Skin", "Tongue")) ``` # Transform Data using PhILR The function `philr::philr()` implements a user friendly wrapper for the key steps in the philr transform. 1. Convert the phylogenetic tree to its sequential binary partition (SBP) representation using the function `philr::phylo2sbp()` 2. Calculate the weighting of the taxa (aka parts) or use the user specified weights 3. Built the contrast matrix from the SBP and taxa weights using the function `philr::buildilrBasep()` 4. Convert OTU table to relative abundance (using `philr::miniclo()`) and 'shift' dataset using the weightings [@egozcue2016] using the function `philr::shiftp()`. 5. Transform the data to PhILR space using the function `philr::ilrp()` 6. (Optional) Weight the resulting PhILR space using phylogenetic distance. These weights are either provided by the user or can be calculated by the function `philr::calculate.blw()`. 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 abundance taxa. Here we will use the same weightings as we used in the main paper. You can run `philr` with the abundance table and phylogenetic tree. ```{r, message=FALSE} gp.philr <- philr(otu.table, tree, part.weights='enorm.x.gm.counts', ilr.weights='blw.sqrt') gp.philr[1:5,1:5] ``` Alternatively, you can provide the data directly in `TreeSE` format. ```{r, message=FALSE} gp.philr <- philr(tse, abund_values = "counts.shifted", part.weights='enorm.x.gm.counts', ilr.weights='blw.sqrt') ``` Alternatively, you can provide the data in `phyloseq` format. For simplicity, let us just convert the `TreeSE` object to `phyloseq` object to give a brief example. ```{r, message=FALSE} pseq <- convertToPhyloseq(tse, assay.type="counts.shifted") gp.philr <- philr(pseq, part.weights='enorm.x.gm.counts', ilr.weights='blw.sqrt') ``` After running `philr` 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., `n1`). # Ordination in PhILR Space Euclidean distance in PhILR space can be used for ordination analysis. Let us first calculate distances and then calculate standard MDS ordination. ```{r} # Distances between samples based on philr transformed data gp.dist <- dist(gp.philr, method="euclidean") # Calculate MDS for the distance matrix d <- as.data.frame(cmdscale(gp.dist)) colnames(d) <- paste0("PC", 1:2) ``` # Visualization with TreeSE Let us next visualize the ordination. This example employs standard tools for ordination and visualization that can be used regardless of the preferred data container. Note that the `phyloseq` and `TreeSE` frameworks may provide access to additional ordination and visualization methods. ```{r} # Add some metadata for the visualization d$SampleType <- factor(metadata$SampleType) # Create a plot ggplot(data = d, aes(x=PC1, y=PC2, color=SampleType)) + geom_point() + labs(title = "Euclidean distances with phILR") ``` # Identify Balances that Distinguish Human/Non-Human 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` package) to identify a small number of balances that best distinguish human from non-human samples. Now we will fit a sparse logistic regression model (logistic regression with $l_1$ penalty) ```{r, message=FALSE, warning=FALSE} library(glmnet); packageVersion('glmnet') glmmod <- glmnet(gp.philr, human.samples, 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). ```{r} 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 ``` # Name Balances 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...). ```{r} tc.names <- sapply(top.coords, function(x) name.balance(tree, tax, x)) tc.names ``` We can also get more information on what goes into the naming by viewing the votes directly. ```{r} votes <- name.balance(tree, tax, 'n730', return.votes = c('up', 'down')) votes[[c('up.votes', 'Family')]] # Numerator at Family Level votes[[c('down.votes', 'Family')]] # Denominator at Family Level ``` # Visualize Results ```{r, message=FALSE, warning=FALSE} library(ggtree); packageVersion("ggtree") library(dplyr); packageVersion('dplyr') ``` 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` [@yu2016] package we can visualize these balances on the tree using the `geom_balance` object. 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 `philr::name.to.nn()` and `philr::nn.to.name()`. 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. ```{r, message=FALSE, warning=FALSE} 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[1], fill=tc.colors[1], alpha=0.6) + geom_balance(node=tc.nn[2], fill=tc.colors[2], alpha=0.6) + geom_balance(node=tc.nn[3], fill=tc.colors[3], alpha=0.6) + geom_balance(node=tc.nn[4], fill=tc.colors[4], alpha=0.6) + geom_balance(node=tc.nn[5], fill=tc.colors[5], 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 `philr::convert_to_long()` for this purpose. ```{r} gp.philr.long <- convert_to_long(gp.philr, human.samples) %>% filter(coord %in% top.coords) ggplot(gp.philr.long, aes(x=labels, y=value)) + geom_boxplot(fill='lightgrey') + facet_grid(.~coord, scales='free_x') + labs(x = 'Human', y = 'Balance Value') + theme_bw() ``` # Use Balances for Dimension Reduction Lets just look at balance n16 vs. balance n730 (the ones we annotated in the above tree). ```{r, message=FALSE, warning=FALSE} library(tidyr); packageVersion('tidyr') gp.philr.long %>% dplyr::rename(Human=labels) %>% dplyr::filter(coord %in% c('n16', 'n730')) %>% tidyr::spread(coord, value) %>% ggplot(aes(x=n16, y=n730, color=Human)) + geom_point(size=4) + labs(x = tc.names['n16'], y = tc.names['n730']) + theme_bw() ``` # Package versions ```{r, echo=TRUE, message=FALSE} sessionInfo() ``` # References