Finding optimal resolution of hierarchical hypotheses with treeclimbR
2024-10-29
Package
treeclimbR 1.3.0
Contents
1 Introduction
treeclimbR is a method for analyzing hierarchical trees of entities, such as
phylogenies, at different levels of resolution. It proposes multiple
candidates (corresponding to different aggregation levels) that capture the
latent signal, and pinpoints branches or leaves that contain features of
interest, in a data-driven way. One motivation for such a multi-level
analysis is that the most highly resolved entities (e.g., individual species
in a microbial context) may not be abundant enough to allow a potential
abundance difference between conditions to be reliably detected. Aggregating
abundances on a higher level in the tree can detect families of species that
are closely related and that all change (possibly weakly but) concordantly. At
the same time, blindly aggregating to a higher level across the whole tree may
imply losing the ability to pinpoint specific species with a strong
signal that may not be shared with their closest neighbors in the tree.
Taken together, this motivates the development of a data-dependent
aggregation approach, which is also allowed to aggregate different parts of
the tree at different levels of resolution.
If you are using treeclimbR, please cite Huang et al. (2021), which also contains
the theoretical justifications and more details of the method.
1.1 Installation
treeclimbR can be installed from Bioconductor using the following code:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("treeclimbR")It integrates seamlessly with the TreeSummarizedExperiment class, which
allows observed data, feature and sample annotations, as well as a tree
representing the hierarchical relationship among features (or samples) to be
stored in the same object.
1.2 Preparation
This vignette outlines the main functionality of treeclimbR, using simulated
example data provided with the package. We start by loading the packages
that will be needed in the analyses below.
suppressPackageStartupMessages({
library(TreeSummarizedExperiment)
library(treeclimbR)
library(ggtree)
library(dplyr)
library(ggplot2)
})2 Differential abundance (DA) analysis
The differential abundance (DA) workflow in treeclimbR is suitable in
situations where we have
observed abundances (often counts) of a set of entities in a set of
samples, the entities can be represented as leaves of a given tree, and we are
interested in finding entities (or groups of entities in the same subtree) whose
abundance is associated with some sample phenotype (e.g., different between
two conditions). For example, in Huang et al. (2021) we studied differences in the
abundance of microbial species between babies born vaginally or via
C-section. We also investigated differences in miRNA abundances between groups
of mice receiving transaortic constriction or sham surgery, and cell
type abundance differences between conditions at
different clustering granularities. In all these cases, the entities are
naturally represented as leaves in a tree (a phylogenetic tree in the first
case, a tree where internal nodes represent miRNA duplexes, primary
transcripts, and clusters of miRNAs for the second, and a clustering tree
defined based on the average similarity between baseline high-resolution
cell clusters for the third).
2.1 Load and visualize example data
In this vignette, we will work with a simulated data set with 30 samples
(15 from each of two conditions) and 100 features. 18 of the features are
differentially abundant between the two conditions; this information is stored
in the Signal column of the object’s rowData. The features represent
leaves in a tree, and the data is stored in a TreeSummarizedExperiment
object. Below, we first load the data and visualize the tree and the
corresponding data.
## Read data
da_lse <- readRDS(system.file("extdata", "da_sim_100_30_18de.rds",
package = "treeclimbR"))
da_lse
#> class: TreeSummarizedExperiment
#> dim: 100 30
#> metadata(1): parentNodeForSignal
#> assays(1): counts
#> rownames(100): t8 t85 ... t45 t92
#> rowData names(1): Signal
#> colnames(30): A_1 A_2 ... B_29 B_30
#> colData names(1): group
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (100 rows)
#> rowTree: 1 phylo tree(s) (100 leaves)
#> colLinks: NULL
#> colTree: NULL
## Generate tree visualization where true signal leaves are colored orange
## ...Find internal nodes in the subtrees where all leaves are differentially
## abundant. These will be colored orange.
nds <- joinNode(tree = rowTree(da_lse),
node = rownames(da_lse)[rowData(da_lse)$Signal])
br <- unlist(findDescendant(tree = rowTree(da_lse), node = nds,
only.leaf = FALSE, self.include = TRUE))
df_color <- data.frame(node = showNode(tree = rowTree(da_lse),
only.leaf = FALSE)) |>
mutate(signal = ifelse(node %in% br, "yes", "no"))
## ...Generate tree
da_fig_tree <- ggtree(tr = rowTree(da_lse), layout = "rectangular",
branch.length = "none",
aes(color = signal)) %<+% df_color +
scale_color_manual(values = c(no = "grey", yes = "orange"))
## ...Zoom into the subtree defined by a particular node. In this case, we
## know that all true signal leaves were sampled from the subtree defined
## by a particular node (stored in metadata(da_lse)$parentNodeForSignal).
da_fig_tree <- scaleClade(da_fig_tree,
node = metadata(da_lse)$parentNodeForSignal,
scale = 4)
## Extract count matrix and scale each row to [0, 1]
count <- assay(da_lse, "counts")
scale_count <- t(apply(count, 1, FUN = function(x) {
xx <- x
rx <- (max(xx) - min(xx))
(xx - min(xx))/max(rx, 1)
}))
rownames(scale_count) <- rownames(count)
colnames(scale_count) <- colnames(count)
## Plot tree and heatmap of scaled counts
## ...Generate sample annotation
vv <- gsub(pattern = "_.*", "", colnames(count))
names(vv) <- colnames(scale_count)
anno_c <- structure(vv, names = vv)
TreeHeatmap(tree = rowTree(da_lse), tree_fig = da_fig_tree,
hm_data = scale_count, legend_title_hm = "Scaled\ncount",
column_split = vv, rel_width = 0.6,
tree_hm_gap = 0.3,
column_split_label = anno_c) +
scale_fill_viridis_c(option = "B") +
scale_y_continuous(expand = c(0, 10))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
2.2 Aggregate counts for internal nodes
treeclimbR provides functionality to find an ‘optimal’ aggregation level at
which to interpret hierarchically structured data. Starting from the
TreeSummarizedExperiment above (containing the observed data as well as the
tree for the features), the first step is to calculate aggregated values (in
this case, counts) for all internal nodes. This is needed so that we can then
run a differential abundance analysis on leaves and nodes simultaneously. The
results from that analysis will then be used to find the optimal aggregation
level.
Here, we use the aggTSE function from the TreeSummarizedExperiment
package to calculate an aggregated count for each internal node in the tree by
summing the counts for all its descendant leaves. For other applications,
other aggregation methods (e.g., averaging) may be more suitable. This can
be controlled via the rowFun argument.
## Get a list of all node IDs
all_node <- showNode(tree = rowTree(da_lse), only.leaf = FALSE)
## Calculate counts for internal nodes
da_tse <- aggTSE(x = da_lse, rowLevel = all_node, rowFun = sum)
da_tse
#> class: TreeSummarizedExperiment
#> dim: 199 30
#> metadata(1): parentNodeForSignal
#> assays(1): counts
#> rownames(199): alias_1 alias_2 ... alias_198 alias_199
#> rowData names(1): Signal
#> colnames(30): A_1 A_2 ... B_29 B_30
#> colData names(1): group
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (199 rows)
#> rowTree: 1 phylo tree(s) (100 leaves)
#> colLinks: NULL
#> colTree: NULLWe see that the new TreeSummarizedExperiment now has 199 rows
(representing the original leaves + the internal nodes).
2.3 Perform differential analysis for leaves and nodes
Next, we perform differential abundance analysis for each leaf and node,
comparing the average abundance in the two conditions.
Here, any suitable function can be used (depending on the properties of the
data matrix). treeclimbR provides a convenience function to perform
the differential abundance analysis using edgeR, which we will use here.
We will ask the wrapper function to filter out lowly abundant features (with
a total count below 15).
## Run differential analysis
da_res <- runDA(da_tse, assay = "counts", option = "glmQL",
design = model.matrix(~ group, data = colData(da_tse)),
contrast = c(0, 1), filter_min_count = 0,
filter_min_prop = 0, filter_min_total_count = 15)The output of runDA contains the edgeR results, a list of the nodes that
were dropped due to a low total count, and the tree.
names(da_res)
#> [1] "edgeR_results" "nodes_drop" "tree"
class(da_res$edgeR_results)
#> [1] "DGELRT"
#> attr(,"package")
#> [1] "edgeR"
## Nodes with too low total count
da_res$nodes_drop
#> [1] "alias_32" "alias_33" "alias_40" "alias_44" "alias_51" "alias_68"
#> [7] "alias_87" "alias_97" "alias_98" "alias_134"Again, note that any differential abundance method can be used, as long as it
produces a data frame with at least columns corresponding to the node number,
the p-value, and the inferred effect size (only the sign will be used).
For the runDA output, we
can generate such a table with the nodeResult() function (where the PValue
column contains the p-value, and the logFC column provides information
about the sign of the inferred change):
da_tbl <- nodeResult(da_res, n = Inf, type = "DA")
dim(da_tbl)
#> [1] 189 6
head(da_tbl)
#> node logFC logCPM F PValue FDR
#> alias_102 102 -0.6284291 18.47205 219.1181 2.333631e-22 4.410563e-20
#> alias_114 114 -0.5744082 17.74475 120.7757 2.193131e-16 2.072509e-14
#> alias_103 103 -0.7101800 17.17136 108.4700 2.032601e-15 1.280538e-13
#> alias_115 115 -0.5944614 17.40516 104.3255 4.460684e-15 2.107673e-13
#> alias_116 116 -0.6787586 16.78855 75.5666 1.922497e-12 7.267041e-11
#> alias_110 110 -0.8245752 16.18132 65.4190 2.230117e-11 6.259878e-102.4 Find candidates
Next, treeclimbR proposes a set of aggregation candidates, corresponding
to a range of values for a threshold parameter \(t\) (see (Huang et al. 2021)).
A candidate consists of a set of nodes,
representing a specific pattern of aggregation. In general, higher values of
\(t\) lead to aggregation further up in the tree (closer to the root).
## Get candidates
da_cand <- getCand(tree = rowTree(da_tse), score_data = da_tbl,
node_column = "node", p_column = "PValue",
threshold = 0.05, sign_column = "logFC", message = FALSE)For a given \(t\)-value, we can indicate the corresponding candidate in the tree. Note that at this point, we have not yet selected the optimal aggregation level, nor are we making conclusions about which of the retained nodes show a significant difference between the conditions. The visualization thus simply illustrates which leaves would be aggregated together if this candidate were to be chosen.
## All candidates
names(da_cand$candidate_list)
#> [1] "0" "0.01" "0.02" "0.03" "0.04" "0.05" "0.1" "0.15" "0.2" "0.25"
#> [11] "0.3" "0.35" "0.4" "0.45" "0.5" "0.55" "0.6" "0.65" "0.7" "0.75"
#> [21] "0.8" "0.85" "0.9" "0.95" "1"
## Nodes contained in the candidate corresponding to t = 0.03
## This is a mix of leaves and internal nodes
(da_cand_0.03 <- da_cand$candidate_list[["0.03"]])
#> [1] 1 2 5 6 7 8 9 10 11 12 15 16 17 18 21 22 23 24 25
#> [20] 26 27 28 29 30 31 34 35 36 37 38 39 41 42 43 45 46 47 48
#> [39] 49 50 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 69
#> [58] 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 88 89
#> [77] 90 91 92 93 94 95 96 99 100 108 122 117
## Visualize candidate
da_fig_tree +
geom_point2(aes(subset = (node %in% da_cand_0.03)),
color = "navy", size = 0.5) +
labs(title = "t = 0.03") +
theme(legend.position = "none",
plot.title = element_text(color = "navy", size = 7,
hjust = 0.5, vjust = -0.08))
2.5 Select the optimal candidate
Finally, given the set of candidates extracted above, treeclimbR can now
extract the one providing the optimal aggregation level (Huang et al. 2021).
## Evaluate candidates
da_best <- evalCand(tree = rowTree(da_tse), levels = da_cand$candidate_list,
score_data = da_tbl, node_column = "node",
p_column = "PValue", sign_column = "logFC")We can get a summary of all the candidates, as well as an indication of which
treeclimbR considers the optimal one, using the infoCand function:
infoCand(object = da_best)
#> t upper_t is_valid method limit_rej level_name best rej_leaf rej_node
#> 1 0.00 0.02142857 TRUE BH 0.05 0 FALSE 17 17
#> 2 0.01 0.02142857 TRUE BH 0.05 0.01 TRUE 17 14
#> 3 0.02 0.02142857 TRUE BH 0.05 0.02 TRUE 17 14
#> 4 0.03 0.02142857 FALSE BH 0.05 0.03 FALSE 17 14
#> 5 0.04 0.02142857 FALSE BH 0.05 0.04 FALSE 17 14
#> 6 0.05 0.04285714 FALSE BH 0.05 0.05 FALSE 20 14
#> 7 0.10 0.05000000 FALSE BH 0.05 0.1 FALSE 21 14
#> 8 0.15 0.05000000 FALSE BH 0.05 0.15 FALSE 21 14
#> 9 0.20 0.05000000 FALSE BH 0.05 0.2 FALSE 21 14
#> 10 0.25 0.06923077 FALSE BH 0.05 0.25 FALSE 22 13
#> 11 0.30 0.09166667 FALSE BH 0.05 0.3 FALSE 23 12
#> 12 0.35 0.10000000 FALSE BH 0.05 0.35 FALSE 26 13
#> 13 0.40 0.10000000 FALSE BH 0.05 0.4 FALSE 26 13
#> 14 0.45 0.10000000 FALSE BH 0.05 0.45 FALSE 26 13
#> 15 0.50 0.10000000 FALSE BH 0.05 0.5 FALSE 26 13
#> 16 0.55 0.10000000 FALSE BH 0.05 0.55 FALSE 26 13
#> 17 0.60 0.10000000 FALSE BH 0.05 0.6 FALSE 26 13
#> 18 0.65 0.10000000 FALSE BH 0.05 0.65 FALSE 26 13
#> 19 0.70 0.10000000 FALSE BH 0.05 0.7 FALSE 26 13
#> 20 0.75 0.10000000 FALSE BH 0.05 0.75 FALSE 26 13
#> 21 0.80 0.10000000 FALSE BH 0.05 0.8 FALSE 26 13
#> 22 0.85 0.10000000 FALSE BH 0.05 0.85 FALSE 26 13
#> 23 0.90 0.10000000 FALSE BH 0.05 0.9 FALSE 26 13
#> 24 0.95 0.10000000 FALSE BH 0.05 0.95 FALSE 26 13
#> 25 1.00 0.10000000 FALSE BH 0.05 1 FALSE 22 11We can also extract a vector of significant nodes from the optimal candidate.
da_out <- topNodes(object = da_best, n = Inf, p_value = 0.05)These nodes can then be indicated in the tree.
da_fig_tree +
geom_point2(aes(subset = node %in% da_out$node),
color = "red")
This visualization shows that in most cases, we correctly identify groups of leaves changing synchronously, and we don’t combine true signal nodes with non-changing ones.
In this case, since we are working with simulated data, we can estimate the
(leaf-level) false discovery rate and true positive rate for the significant
nodes. treeclimbR will automatically extract the descendant leaves for each
of the provided nodes, and perform the evaluation on the leaf level.
fdr(rowTree(da_tse), truth = rownames(da_lse)[rowData(da_lse)$Signal],
found = da_out$node, only.leaf = TRUE)
#> fdr
#> 0.05882353
tpr(rowTree(da_tse), truth = rownames(da_lse)[rowData(da_lse)$Signal],
found = da_out$node, only.leaf = TRUE)
#> tpr
#> 0.88888893 Differential state (DS) analysis
treeclimbR also provides functionality for performing so called ‘differential
state analysis’ in a hierarchical setting. An example use case for this type
of analysis is in a multi-sample, multi-condition single-cell RNA-seq data set
with multiple cell types, where we are interested in finding genes that are
differentially expressed between conditions in either a single (high-resolution)
cell type (subpopulation) or a group of similar cell subpopulations.
3.1 Load and visualize example data
We load a simulated example data set with 20 genes and 500 cells. The cells
are assigned to 10 initial (high-resolution) clusters or subpopulations
(labelled ‘t1’ to ‘t10’), which
are further hierarchically clustered into larger meta-clusters. The
hierarchical clustering tree for the subpopulations is provided as the column
tree of the TreeSummarizedExperiment object. In addition, the cells come
from four different samples (two from condition ‘A’ and two from condition ‘B’).
We are interested in finding genes that are differentially expressed between
the conditions (so called ‘state markers’) at some level of the clustering tree.
ds_tse <- readRDS(system.file("extdata", "ds_sim_20_500_8de.rds",
package = "treeclimbR"))
ds_tse
#> class: TreeSummarizedExperiment
#> dim: 20 500
#> metadata(0):
#> assays(1): counts
#> rownames: NULL
#> rowData names(1): Signal
#> colnames: NULL
#> colData names(3): cluster_id sample_id group
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: a LinkDataFrame (500 rows)
#> colTree: 1 phylo tree(s) (10 leaves)
## Assignment of cells to high-resolution clusters, samples and conditions
head(colData(ds_tse))
#> DataFrame with 6 rows and 3 columns
#> cluster_id sample_id group
#> <character> <integer> <character>
#> 1 t7 4 B
#> 2 t7 4 B
#> 3 t1 4 B
#> 4 t4 1 A
#> 5 t6 1 A
#> 6 t1 1 A
## Tree providing the successive aggregation of the high-resolution clusters
## into more coarse-grained ones
## Node numbers are indicated in blue, node labels in orange
ggtree(colTree(ds_tse)) +
geom_text2(aes(label = node), color = "darkblue",
hjust = -0.5, vjust = 0.7) +
geom_text2(aes(label = label), color = "darkorange",
hjust = -0.1, vjust = -0.7)
The data set contains 8 genes that are simulated to be differentially
expressed between the two conditions. Four genes are differentially expressed
in all the clusters, two in clusters t2, t6 and t7, and two in clusters
t4 and t5.
rowData(ds_tse)[rowData(ds_tse)$Signal != "no", , drop = FALSE]
#> DataFrame with 8 rows and 1 column
#> Signal
#> <character>
#> 1 all
#> 2 t2, t6, t7
#> 3 all
#> 4 t2, t6, t7
#> 5 all
#> 6 all
#> 7 t4, t5
#> 8 t4, t53.2 Aggregate counts for internal nodes
As for the DA analysis, the first step is to aggregate the counts for the internal nodes. In this use case, it effectively corresponds to generating pseudobulk samples for each original sample and each internal node in the tree.
ds_se <- aggDS(TSE = ds_tse, assay = "counts", sample_id = "sample_id",
group_id = "group", cluster_id = "cluster_id", FUN = sum)
#> Warning in TreeSummarizedExperiment::convertNode(tree = tree, node =
#> as.character(cell_info$cluster_id)): Multiple nodes are found to have the same
#> label.
ds_se
#> class: SummarizedExperiment
#> dim: 20 4
#> metadata(3): experiment_info agg_pars n_cells
#> assays(19): alias_1 alias_2 ... alias_18 alias_19
#> rownames: NULL
#> rowData names(0):
#> colnames(4): 4 1 2 3
#> colData names(1): groupNote how there is now one assay for each node in the tree (ten for the leaves and nine for the internal nodes). Each of these assays contains the aggregated counts for all genes in all cells belonging to the corresponding node, split by sample ID.
This object also stores information about the number of cells contributing to each node and sample
metadata(ds_se)$n_cells
#> 4 1 2 3
#> alias_1 10 8 14 12
#> alias_2 17 12 10 14
#> alias_3 9 9 18 11
#> alias_4 14 13 14 7
#> alias_5 11 9 13 14
#> alias_6 15 14 13 12
#> alias_7 16 18 16 13
#> alias_8 17 13 15 7
#> alias_9 7 12 13 13
#> alias_10 12 11 12 12
#> alias_11 128 119 138 115
#> alias_12 116 108 126 103
#> alias_13 36 29 42 37
#> alias_14 26 21 28 25
#> alias_15 80 79 84 66
#> alias_16 73 67 71 53
#> alias_17 40 36 40 33
#> alias_18 25 22 27 21
#> alias_19 33 31 31 203.3 Perform differential analysis for leaves and nodes
Next, we perform the differential analysis. As for the DA analysis above,
treeclimbR provides a convenience function for applying edgeR on the
aggregated counts for each node. However, any suitable method can be used.
ds_res <- runDS(SE = ds_se, tree = colTree(ds_tse), option = "glmQL",
design = model.matrix(~ group, data = colData(ds_se)),
contrast = c(0, 1), filter_min_count = 0,
filter_min_total_count = 1, filter_min_prop = 0, min_cells = 5,
group_column = "group", message = FALSE)
#> 0 nodes are ignored, as they don't contain at least 5 cells in at least half of the samples.As before, the output contains the edgeR results for each node, the tree, and
the list of nodes that were dropped because of a too low count.
names(ds_res)
#> [1] "edgeR_results" "tree" "nodes_drop"
names(ds_res$edgeR_results)
#> [1] "alias_1" "alias_2" "alias_3" "alias_4" "alias_5" "alias_6"
#> [7] "alias_7" "alias_8" "alias_9" "alias_10" "alias_11" "alias_12"
#> [13] "alias_13" "alias_14" "alias_15" "alias_16" "alias_17" "alias_18"
#> [19] "alias_19"We can create a table with the node results as well. Note that this table contains the results from all genes (features) at all nodes.
ds_tbl <- nodeResult(ds_res, type = "DS", n = Inf)
dim(ds_tbl)
#> [1] 380 7
head(ds_tbl)
#> logFC logCPM F PValue FDR node feature
#> 3...1 1.647224 16.31850 2702.109 2.402920e-38 9.131097e-36 11 3
#> 1...2 1.623036 16.28457 2411.255 2.264792e-37 4.303105e-35 11 1
#> 6...3 -1.566550 16.27571 2177.354 1.684458e-36 2.133646e-34 11 6
#> 5...4 -1.528560 16.26806 2042.835 5.892588e-36 5.597959e-34 11 5
#> 6...5 -1.633382 16.19005 1671.077 3.012255e-34 2.289314e-32 13 6
#> 5...6 -1.577632 16.18364 1610.875 6.168542e-34 3.906743e-32 13 53.4 Find candidates
The next step in the analysis is to generate a list of candidates. This is done
separately for each gene - hence, we first split the result table above by
the feature column, and then run getCand() for each subtable.
## Split result table by feature
ds_tbl_list <- split(ds_tbl, f = ds_tbl$feature)
## Find candidates for each gene separately
ds_cand_list <- lapply(seq_along(ds_tbl_list),
FUN = function(x) {
getCand(
tree = colTree(ds_tse),
t = seq(from = 0.05, to = 1, by = 0.05),
score_data = ds_tbl_list[[x]],
node_column = "node",
p_column = "PValue",
sign_column = "logFC",
message = FALSE)$candidate_list
})
names(ds_cand_list) <- names(ds_tbl_list)3.5 Select the optimal candidate
We can then find the optimal candidate using the evalCand() function, and
list the top hits.
ds_best <- evalCand(tree = colTree(ds_tse), type = "multiple",
levels = ds_cand_list, score_data = ds_tbl_list,
node_column = "node",
p_column = "PValue",
sign_column = "logFC",
feature_column = "feature",
limit_rej = 0.05,
message = FALSE,
use_pseudo_leaf = FALSE)
ds_out <- topNodes(object = ds_best, n = Inf, p_value = 0.05)
ds_out
#> logFC logCPM F PValue FDR node feature
#> 1 1.623036 16.28457 2411.2547 2.264792e-37 4.303105e-35 11 1
#> 2 1.535873 16.20796 1350.8552 1.904533e-32 1.033890e-30 13 2
#> 3 1.647224 16.31850 2702.1091 2.402920e-38 9.131097e-36 11 3
#> 4 -1.613073 16.25077 1258.5324 7.530189e-32 3.576840e-30 13 4
#> 5 -1.528560 16.26806 2042.8352 5.892588e-36 5.597959e-34 11 5
#> 6 -1.566550 16.27571 2177.3536 1.684458e-36 2.133646e-34 11 6
#> 7...7 1.322240 16.11680 501.7687 3.012677e-24 4.770073e-23 5 7
#> 7...8 1.656876 16.10320 630.0092 1.171324e-09 5.634218e-09 9 7
#> 8...9 -1.523605 16.26492 719.5137 3.466238e-27 6.272241e-26 5 8
#> 8...10 -1.685560 16.34385 252.2818 6.672580e-08 2.848967e-07 9 8
#> adj.p signal.node
#> 1 1.811834e-35 TRUE
#> 2 6.094507e-31 TRUE
#> 3 3.844672e-36 TRUE
#> 4 2.008050e-30 TRUE
#> 5 2.357035e-34 TRUE
#> 6 8.983774e-35 TRUE
#> 7...7 6.025355e-23 TRUE
#> 7...8 2.082354e-08 TRUE
#> 8...9 7.922830e-26 TRUE
#> 8...10 1.067613e-06 TRUEAs expected, we detect the eight truly differentially expressed features (feature 1-8). Furthermore, we see that they have been aggregated to different nodes in the tree. Features 1, 3, 5 and 6 are aggregated to node 11, features 2 and 4 to node 13, and features 7 and 8 to nodes 5 and 9. Let’s see which leaves these internal nodes correspond to.
lapply(findDescendant(colTree(ds_tse), node = c(11, 13, 5, 9),
self.include = TRUE,
use.alias = FALSE, only.leaf = TRUE),
function(x) convertNode(colTree(ds_tse), node = x))
#> $Node_11
#> [1] "t2" "t7" "t6" "t9" "t4" "t8" "t10" "t1" "t5" "t3"
#>
#> $Node_13
#> [1] "t2" "t7" "t6"
#>
#> $t4
#> [1] "t4"
#>
#> $t5
#> [1] "t5"Indeed, comparing this to the ground truth provided above, we see that each gene has been aggregated to the lowest node level that contains all the original subpopulations where the gene was simulated to be differentially expressed between conditions A and B:
rowData(ds_tse)[rowData(ds_tse)$Signal != "no", , drop = FALSE]
#> DataFrame with 8 rows and 1 column
#> Signal
#> <character>
#> 1 all
#> 2 t2, t6, t7
#> 3 all
#> 4 t2, t6, t7
#> 5 all
#> 6 all
#> 7 t4, t5
#> 8 t4, t54 Additional examples
Additional examples of applying treeclimbR to experimental data sets can be
found on GitHub.
5 Session info
This document was executed with the following package versions (click to expand)
sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ggplot2_3.5.1 dplyr_1.1.4
#> [3] ggtree_3.15.0 treeclimbR_1.3.0
#> [5] TreeSummarizedExperiment_2.15.0 Biostrings_2.75.0
#> [7] XVector_0.47.0 SingleCellExperiment_1.29.0
#> [9] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [11] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
#> [13] IRanges_2.41.0 S4Vectors_0.45.0
#> [15] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
#> [17] matrixStats_1.4.1 BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
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#> [3] shape_1.4.6.1 magrittr_2.0.3
#> [5] TH.data_1.1-2 farver_2.1.2
#> [7] nloptr_2.1.1 rmarkdown_2.28
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#> [11] zlibbioc_1.53.0 vctrs_0.6.5
#> [13] minqa_1.2.8 rstatix_0.7.2
#> [15] htmltools_0.5.8.1 S4Arrays_1.7.0
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#> [19] SparseArray_1.7.0 Formula_1.2-5
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#> [29] iterators_1.0.14 pkgconfig_2.0.3
#> [31] Matrix_1.7-1 R6_2.5.1
#> [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
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