Introduction to TreeSummarizedExperiment

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)


The TreeSummarizedExperiment class is an extension of the SingleCellExperiment class [@LunA2020]. It's used to store rectangular data of experimental results as in a SingleCellExperiment, and also supports the storage of a hierarchical structure and its link information to the rectangular data.

TreeSummarizedExperiment {#tse-class}

Anatomy of TreeSummarizedExperiment


Compared to the SingleCellExperiment objects, TreeSummarizedExperiment has four additional slots:

The rowTree and$/$or colTree can be left empty (NULL) if no trees are available; in this case, the rowLinks and$/$or colLinks are also set to NULL. All other TreeSummarizedExperiment slots are inherited from SingleCellExperiment.

The rowTree and colTree slots require the tree to be an object of the phylo class. If a tree is available in an alternative format, it can often be converted to a phylo object using dedicated R packages (e.g., r Biocpkg("treeio") [@Wang2019]).

Functions in the r Biocpkg("TreeSummarizedExperiment") package fall in two main categories: operations on the TreeSummarizedExperiment object or operations on the tree (phylo) objects. The former includes constructors and accessors, and the latter serves as "pieces" to be assembled as accessors or functions that manipulate the TreeSummarizedExperiment object. Given that more than 200 R packages make use of the phylo class, there are many resources (e.g., r CRANpkg("ape")) for users to manipulate the small "pieces" in addition to those provided in r Biocpkg("TreeSummarizedExperiment").

Toy data

We generate a toy dataset that has observations of 6 entities collected from 4 samples as an example to show how to construct a TreeSummarizedExperiment object.


# assays data (typically, representing observed data from an experiment)
assay_data <- rbind(rep(0, 4), matrix(1:20, nrow = 5))
colnames(assay_data) <- paste0("sample", 1:4)
rownames(assay_data) <- paste("entity", seq_len(6), sep = "")

The information of entities and samples are given in the row_data and col_data, respectively.

# row data (feature annotations)
row_data <- data.frame(Kingdom = "A",
                       Phylum = rep(c("B1", "B2"), c(2, 4)),
                       Class = rep(c("C1", "C2", "C3"), each = 2),
                       OTU = paste0("D", 1:6),
                       row.names = rownames(assay_data),
                       stringsAsFactors = FALSE)

# column data (sample annotations)
col_data <- data.frame(gg = c(1, 2, 3, 3),
                       group = rep(LETTERS[1:2], each = 2), 
                       row.names = colnames(assay_data),
                       stringsAsFactors = FALSE)

The hierarchical structure of the r nrow(assay_data) entities and r ncol(assay_data) samples are denoted as row_tree and col_tree, respectively. The two trees are phylo objects randomly created with rtree from the package r CRANpkg("ape"). Note that the row tree has 5 rather than 6 leaves; this is used later to show that multiple rows in the assays are allowed to map to a single node in the tree.


# The first toy tree 
row_tree <- rtree(5)

# The second toy tree 
col_tree <- rtree(4)

# change node labels
col_tree$tip.label <- colnames(assay_data)
col_tree$node.label <- c("All", "GroupA", "GroupB")

We visualize the tree using the package r Biocpkg("ggtree") [@Yu2017]. Here, the internal nodes of the row_tree have no labels as shown in Figure \@ref(fig:plot-rtree).

```r The structure of the row tree. The node labels and the node numbers are in orange and blue text, respectively.", out.width="90%"} library(ggtree) library(ggplot2)

Visualize the row tree

ggtree(row_tree, size = 2, branch.length = "none") + geom_text2(aes(label = node), color = "darkblue", hjust = -0.5, vjust = 0.7, size = 5.5) + geom_text2(aes(label = label), color = "darkorange", hjust = -0.1, vjust = -0.7, size = 5.5)

The **col_tree** has labels for internal nodes.
```r The structure of the column tree. The node labels and the node numbers are in orange and blue text, respectively.", out.width="90%"}
# Visualize the column tree
ggtree(col_tree, size = 2, branch.length = "none") +
    geom_text2(aes(label = node), color = "darkblue",
               hjust = -0.5, vjust = 0.7, size = 5.5) +
    geom_text2(aes(label = label), color = "darkorange",
               hjust = -0.1, vjust = -0.7, size = 5.5)+
    ylim(c(0.8, 4.5)) +
    xlim(c(0, 2.2))

The construction of TreeSummarizedExperiment

The TreeSummarizedExperiment class is used to store the toy data generated in the previous section: assay_data, row_data, col_data, col_tree and row_tree. To correctly store data, the link information between the rows (or columns) of assay_data and the nodes of the row_tree (or col_tree) can be provided via a character vector rowNodeLab (or colNodeLab), with length equal to the number of rows (or columns) of the assays; otherwise the row (or column) names are used. Those columns or rows with labels that are not present among the node labels of the tree are removed with warnings. The link data between the assays tables and the tree data is automatically generated in the construction.

The row and column trees can be included simultaneously in the construction. Here, the column names of assay_data can be found in the node labels of the column tree, which enables the link to be created between the column dimension of assay_data and the column tree col_tree. If the row names of assay_data are not in the node labels of row_tree, we would need to provide their corresponding node labels (row_lab) to rowNodeLab in the construction of the object. It is allowed to have multiple rows or/and columns mapped to a node, for example, the same leaf label is used for the first two rows in row_lab.

# all column names could be found in the node labels of the column tree
all(colnames(assay_data) %in% c(col_tree$tip.label, col_tree$node.label))

# provide the node labels in rowNodeLab
tip_lab <- row_tree$tip.label
row_lab <- tip_lab[c(1, 1:5)]

both_tse <- TreeSummarizedExperiment(assays = list(Count = assay_data),
                                     rowData = row_data,
                                     colData = col_data,
                                     rowTree = row_tree,
                                     rowNodeLab = row_lab,
                                     colTree = col_tree)

When printing out both_tse, we see a similar message as SingleCellExperiment with four additional lines for rowLinks, rowTree, colLinks and colTree.

The accessor functions

Assays, rowData, colData, and metadata

For slots inherited from the SingleCellExperiment class, the accessors are exactly the same as shown in r Biocpkg("SingleCellExperiment").

# to get the first table in the assays
(count <- assays(both_tse)[[1]])
# to get row data
# to get column data
# to get metadata: it's empty here

rowLinks, colLinks {#linkData}

For new slots, we provide rowTree (and colTree) accessors to retrieve the row (column) trees, and rowLinks (and colLinks) to retrieve the link information between assays and nodes of the row (column) tree. If the tree is not available, the corresponding link data is NULL.

# access trees
# access the link data
(r_link <- rowLinks(both_tse))
(c_link <- colLinks(both_tse))

The link data objects are of the LinkDataFrame class, which extends the DataFrame class with the restriction that it has at least four columns:

More details about the DataFrame class could be found in the r Biocpkg("S4Vectors") R/Bioconductor package.


The link data is automatically generated when constructing the TreeSummarizedExperiment object. We highly recommend users not to modify it manually; otherwise the link might be broken. For R packages developers, we show in the Section \@ref(modifyLink) about how to update the link.

The subseting function

A TreeSummarizedExperiment object can be subset in two different ways: [ to subset by rows or columns, and subsetByNode to subset by nodes of a tree. To keep track of the original data, the rowTree and colTree stay the same after subsetting, while rowLinks and rowData are updated accordingly.

sub_tse <- both_tse[1:2, 1]
# the row data

# the row link data
# The first four columns are from colLinks data and the others from colData
cbind(colLinks(sub_tse), colData(sub_tse))

To subset by nodes, we specify the node by its node label or node number. Here, entity1 and entity2 are both mapped to the same node t3, so both of them are retained.

node_tse <- subsetByNode(x = both_tse, rowNode = "t3")


Subsetting simultaneously in both dimensions is also allowed.

node_tse <- subsetByNode(x = both_tse, rowNode = "t3", 
                         colNode = c("sample1", "sample2"))

Changing the tree

The current tree can be replaced by a new one using changeTree. If the hierarchical information is available as a data.frame with each column representing a taxonomic level (e.g., row_data), we provide toTree to convert it into a phylo object.

```r The structure of the taxonomic tree that is generated from the taxonomic table.", out.width="90%"}

The toy taxonomic table

(taxa <- rowData(both_tse))

convert it to a phylo tree

taxa_tree <- toTree(data = taxa)

Viz the new tree

ggtree(taxa_tree)+ geom_text2(aes(label = node), color = "darkblue", hjust = -0.5, vjust = 0.7, size = 5.5) + geom_text2(aes(label = label), color = "darkorange", hjust = -0.1, vjust = -0.7, size = 5.5) + geom_point2()

A mapping to match nodes of the two trees is required if nodes are labeled differently. 
taxa_tse <- changeTree(x = both_tse, rowTree = taxa_tree, 
                       rowNodeLab = taxa[["OTU"]])



Since it may be of interest to report or analyze observed data on multiple resolutions based on the provided tree, the TreeSummarizedExperiment package offers functionionality to flexibly aggregate data to different levels of a tree.

The column dimension {#aggCol}

Here, we show the aggregation along the column dimension. The desired aggregation level is given in the colLevel argument, which can be specified via the node label (orange text in Figure \@ref(fig:plot-ctree)) or the node number (blue text in Figure \@ref(fig:plot-ctree)). We could further specify how to aggregate via the argument FUN.

# use node labels to specify colLevel
agg_col <- aggValue(x = taxa_tse, 
                    colLevel = c("GroupA", "GroupB"),
                    FUN = sum)
# or use node numbers to specify colLevel
agg_col <- aggValue(x = taxa_tse, colLevel = c(6, 7), FUN = sum)

The rowData does not change, but the colData adjusts with the change of the assays table. For example, the column group has the A value for GroupA because the descendant nodes of GroupA all have the value A; the column gg has the NA value for GroupA because the descendant nodes of GroupA have different values, (1 and 2).

# before aggregation
# after aggregation

The colLinks is updated to link the new rows of assays tables and the column tree.

# the link data is updated

From Figure \@ref(fig:plot-ctree), nodes 6 and 7 are labeled with GroupA and GroupB, respectively, which agrees with the column link data.

The row dimension {#aggRow}

Similarly, we could aggregate the data to the phylum level by providing the names of the internal nodes that represent the phylum level (see taxa_one below).

# the phylum level
taxa <- c(taxa_tree$tip.label, taxa_tree$node.label)
(taxa_one <- taxa[startsWith(taxa, "Phylum:")])

# aggregation
agg_taxa <- aggValue(x = taxa_tse, rowLevel = taxa_one, FUN = sum)

The user is nonetheless free to choose nodes from different taxonomic ranks. Note that not all rows in the original table are included in one of the aggregated rows. Similarly, it is possible for a row to contribute to multiple aggregated rows

# A mixed level
taxa_mix <- c("Class:C3", "Phylum:B1")
agg_any <- aggValue(x = taxa_tse, rowLevel = taxa_mix, FUN = sum)

Both dimensions

The aggregation on both dimensions could be performed in one step using the same function specified via FUN. If different functions are required for different dimensions, the aggregation should be performed in two steps because the aggregation order matters.

agg_both <- aggValue(x = both_tse, colLevel = c(6, 7), 
                     rowLevel = 7:9, FUN = sum)

As expected, we obtain a table with 3 rows (rowLevel = 7:9) and 2 columns (colLevel = c(6, 7)).


Functions operating on the phylo object.

Next, we highlight some functions to manipulate and/or to extract information from the phylo object. Further operations can be found in other packages, such as r CRANpkg("ape") [@ape2019], r CRANpkg("tidytree")[@R-tidytree]. These functions are useful when users want to customize functions for the TreeSummarizedExperiment class.

To show these functions, we use the tree shown in Figure \@ref(fig:plot-exTree).

```r An example tree with node labels and numbers in black and orange texts, respectively.", out.width="90%"} data("tinyTree") ggtree(tinyTree, branch.length = "none") + geom_text2(aes(label = label), hjust = -0.1, size = 5.5) + geom_text2(aes(label = node), vjust = -0.8, hjust = -0.2, color = 'orange', size = 5.5)

### Conversion of the node label and the node number

The translation between the node labels and node numbers can be achieved by the function `convertNode`. 
convertNode(tree = tinyTree, node = c(12, 1, 4))
convertNode(tree = tinyTree, node = c("t4", "Node_18"))

Find the descendants

To get descendants that are at the leaf level, we could set the argument only.leaf = TRUE for the function findDescendant.

# only the leaf nodes
findDescendant(tree = tinyTree, node = 17, only.leaf = TRUE)

When only.leaf = FALSE, all descendants are returned.

# all descendant nodes
findDescendant(tree = tinyTree, node = 17, only.leaf = FALSE)

More functions

We list some functions that might also be useful in Table \@ref(tab:phyloFun). More are available in the package, and we encourage users to contribute their functions that might be helpful for others.

| Functions | Goal | | ----------- | ------------------------------------------------------------ | | printNode | print out the information of nodes | | countNode | count the number of nodes | | distNode | give the distance between a pair of nodes | | matTree | list paths of a tree | | findAncestor| find ancestor nodes | | findChild | find child nodes | | findSibling | find sibling nodes | | shareNode | find the first node shared in the paths of nodes to the root | | unionLeaf | find the union of descendant leaves | | trackNode | track nodes by adding alias labels to a phylo object | | isLeaf | test whether a node is a leaf node | : (#tab:phyloFun) A table lists some functions operating on the phylo object that are available in the TreeSummarizedExperiment.

Custom functions for the TreeSummarizedExperiment class {#modifyLink}

Here, we show examples on how to write custom functions for TreeSummarizedExperiment objects. To extract data of specific leaves, we created a function subsetByLeaf by combining functions working on the phylo class (e.g., convertNode, keep.tip, trackNode, isLeaf) with the accessor function subsetByNode. Here, convertNode, trackNode and isLeaf are available in TreeSummarizedExperiment, and keep.tip is from the r CRANpkg("ape") package. Since the node number of a node is changed after pruning a tree with keep.tip, trackNode is provided to track the node and further update the link between the data and the new tree.

# tse: a TreeSummarizedExperiment object
# rowLeaf: specific leaves
subsetByLeaf <- function(tse, rowLeaf) {
  # if rowLeaf is provided as node labels, convert them to node numbers
  if (is.character(rowLeaf)) {
    rowLeaf <- convertNode(tree = rowTree(tse), node = rowLeaf)

  # subset data by leaves
  sse <- subsetByNode(tse, rowNode = rowLeaf)

  # update the row tree
    ## -------------- new tree: drop leaves ----------
    oldTree <- rowTree(sse)
    newTree <- ape::keep.tip(phy = oldTree, tip = rowLeaf)

    ## -------------- update the row link ----------
    # track the tree
    track <- trackNode(oldTree)
    track <- ape::keep.tip(phy = track, tip = rowLeaf)

    # row links
    rowL <- rowLinks(sse)
    rowL <- DataFrame(rowL)

    # update the row links: 
    #   1. use the alias label to track and updates the nodeNum
    #   2. the nodeLab should be updated based on the new tree using the new
    #      nodeNum
    #   3. lastly, update the nodeLab_alias
    rowL$nodeNum <- convertNode(tree = track, node = rowL$nodeLab_alias,
                              message = FALSE)
    rowL$nodeLab <- convertNode(tree = newTree, node = rowL$nodeNum, 
                              use.alias = FALSE, message = FALSE)
    rowL$nodeLab_alias <- convertNode(tree = newTree, node = rowL$nodeNum, 
                                    use.alias = TRUE, message = FALSE)
    rowL$isLeaf <- isLeaf(tree = newTree, node = rowL$nodeNum)

    rowNL <- new("LinkDataFrame", rowL)

    ## update the row tree and links
                              rowLinks = rowNL,
                              rowTree = list(phylo = newTree))

The row tree is updated after the subsetting. It now has only two leaves, t2 and t3.

(both_sse <- subsetByLeaf(tse = both_tse, rowLeaf = c("t2", "t3")))

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TreeSummarizedExperiment documentation built on Dec. 8, 2020, 2 a.m.