Introduction to the GraphExperiment class
24 May 2026
Contents
1 Introduction
Networks (or graphs) have become widely used data representations in biology,
as they can efficiently encode node-node interactions and neighborhoods.
In high-throughput, quantitative omics data (e.g., transcriptomics, proteomics,
metabolomics, epigenomics, etc), widely used feature-level
network representations include
gene coexpression, protein-protein interaction, gene regulatory, and
co-abundance networks, and cell-cell communication,
sample-sample similarity, and species-species networks at the observation
level. While data structures to store quantitative data and
associated metadata exist (e.g., SummarizedExperiment, SingleCellExperiment,
SpatialExperiment, etc), support for networks describing how features
(i.e., assay rows) and/or observations (i.e., assay columns) relate
to each other is currently missing. GraphExperiment is an S4 class that
extends SingleCellExperiment (Amezquita et al. 2020) to include additional containers
for networks associated with assay features (rowGraphs) and
assay observations (colGraphs).
Of note, trees are an alternative way of representing how assay
features/observations are related to each other. Users interested in tree
representations of assays rows/columns can use
the TreeSummarizedExperiment package.
Trees are essentially a kind of graph (i.e., all trees are graphs,
but not all graphs are trees). Here, we chose to use a more
general graph representation (namely igraph objects) to provide users and
developers with more flexibility.
2 Installation
GraphExperiment can be installed from Bioconductor with the following code:
if(!requireNamespace('BiocManager', quietly = TRUE))
install.packages('BiocManager')
BiocManager::install("GraphExperiment")# Load package after installation
library(GraphExperiment)
set.seed(777) # for reproducibility3 Anatomy of a GraphExperiment object
Since the GraphExperiment class extends the SingleCellExperiment class,
all SingleCellExperiment slots are present in GraphExperiment, including:
assays: list of matrices with primary (e.g., counts) and transformed (e.g., log-normalized counts, TPM, etc) data, with features in rows and observations in columns.colData: a data frame with column (observation) metadata, such as cell type, sample ID, condition, batch ID, genotype, etc.rowData: a data frame with row (feature) metadata, such as gene ID, genomic coordinates, functional annotation, etc.reducedDims: list of data frames with reduced dimensions, such as PCA, t-SNE, and UMAP embeddings.
Compared to SingleCellExperiment objects, GraphExperiment
provides two additional containers:1 Note on software design: if you’re familiar with
SingleCellExperiment objects, you probably know that it offers
rowPairs/colPairs slots to store pairwise relationships between rows and
columns of assays, respectively.
In theory, some of the data stored in rowGraphs/colGraphs (of a
GraphExperiment object) could be stored in rowPairs/colPairs (of a
SingleCellExperiment). However, we chose to implement a dedicated slot with
igraph objects to guarantee (i) seamless interoperability with other
packages, given that igraph is the de facto standard class for graphs in R;
and (ii) convenience in methods (e.g., subsetting, integration with
rowData/colData, integration across multiple graphs, etc).
rowGraphs: list ofigraphobjects containing graphs representing feature-feature relationships, including (but optional) node and edge attributes.colGraphs: list ofigraphobjects containing graphs representing observation-observation relationships, including (but optional) node and edge attributes.
Figure 1: The GraphExperiment class
The igraph data class from the igraph package
is the standard data structure for graph representation in R. If you are
unfamiliar with igraph objects, you can learn more about it by reading
the igraph vignettes.
4 Building a GraphExperiment object
GraphExperiment objects can be created from scratch using the constructor
function GraphExperiment(). Below we will simulate a scRNA-seq count matrix
with some gene (row) and cell (column) metadata, and create graphs based
on gene-gene and cell-cell correlations.2 Tip: in day-to-day single-cell RNA-seq analyses, researchers
typically infer cell-cell graphs based on shared nearest-neighbors (SNN),
which are then used to find clusters that can be mapped to cell types.
Readers interested in this sort of graph can have a look
at the buildSNNGraph() function from the scran
package.
# Simulate parts of a `GraphExperiment` object
## Assays
gene_ids <- paste0("gene", seq_len(200))
cell_ids <- paste0("cell", seq_len(100))
mat <- matrix(rpois(20000, 5), ncol = 100, dimnames = list(gene_ids, cell_ids))
mat[1:5, 1:5]
#> cell1 cell2 cell3 cell4 cell5
#> gene1 6 7 5 5 6
#> gene2 5 3 6 3 5
#> gene3 4 5 9 7 2
#> gene4 12 4 5 9 3
#> gene5 6 6 5 2 8
## rowData
rdata <- data.frame(
row.names = gene_ids,
pathway = sample(c("P1", "P2"), size = length(gene_ids), replace = TRUE),
coding = sample(c(TRUE, FALSE), size = length(gene_ids), replace = TRUE)
)
head(rdata)
#> pathway coding
#> gene1 P1 FALSE
#> gene2 P1 TRUE
#> gene3 P2 TRUE
#> gene4 P1 FALSE
#> gene5 P1 TRUE
#> gene6 P1 FALSE
## colData
cdata <- data.frame(
row.names = cell_ids,
cell_type = sample(c("ct1", "ct2"), size = length(cell_ids), replace = TRUE)
)
head(cdata)
#> cell_type
#> cell1 ct2
#> cell2 ct2
#> cell3 ct2
#> cell4 ct1
#> cell5 ct2
#> cell6 ct1
## rowGraph (with node attribute `degree`)
rg <- graph_from_adjacency_matrix(
cor(t(mat)), mode = "undirected", weighted = TRUE
)
rg <- set_vertex_attr(rg, "degree", value = strength(rg))
rg
#> IGRAPH fad826d UNW- 200 20096 --
#> + attr: name (v/c), degree (v/n), weight (e/n)
#> + edges from fad826d (vertex names):
#> [1] gene1--gene1 gene1--gene2 gene1--gene3 gene1--gene4 gene1--gene5
#> [6] gene1--gene6 gene1--gene7 gene1--gene8 gene1--gene9 gene1--gene10
#> [11] gene1--gene11 gene1--gene12 gene1--gene13 gene1--gene14 gene1--gene15
#> [16] gene1--gene16 gene1--gene17 gene1--gene18 gene1--gene19 gene1--gene20
#> [21] gene1--gene21 gene1--gene22 gene1--gene23 gene1--gene24 gene1--gene25
#> [26] gene1--gene26 gene1--gene27 gene1--gene28 gene1--gene29 gene1--gene30
#> [31] gene1--gene31 gene1--gene32 gene1--gene33 gene1--gene34 gene1--gene35
#> [36] gene1--gene36 gene1--gene37 gene1--gene38 gene1--gene39 gene1--gene40
#> + ... omitted several edges
## colGraph
cg <- graph_from_adjacency_matrix(
cor(mat), mode = "undirected", weighted = TRUE
)To create a GraphExperiment object from the constructor function, you would
run:
# Create a `GraphExperiment` object
ge <- GraphExperiment(
assays = list(counts = mat),
rowData = rdata,
colData = cdata,
rowGraphs = list(gene_cor = rg),
colGraphs = list(cell_cor = cg)
)
ge
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(3): pathway coding gene_cor__degree
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(1): cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(1): gene_cor
#> colGraphs(1): cell_corIf you’re familiar with SummarizedExperiment and SingleCellExperiment
objects, you will certainly recognize nearly everything you see in ge.
Compared to SingleCellExperiment objects, the only difference here is
in the last two rows, which indicate that this object contains a rowGraph
named ‘gene_cor’ and a colGraph named ‘cell_cor’.
Importantly, since nodes of rowGraphs/colGraphs are always in sync
with rownames/colnames,
feature IDs in rownames and rowGraphs must be the same, and
likewise for observation IDs in colnames and colGraphs.
For example, attempting to create a GraphExperiment object with some
features from rownames missing would lead to an error:
# Remove 'gene1' to 'gene10' from the rowGraph and try to recreate object
rg2 <- delete_vertices(rg, paste0("gene", 1:10))
GraphExperiment(
assays = list(counts = mat),
rowData = rdata,
colData = cdata,
rowGraphs = list(gene_cor = rg2)
)
#> Error in `validObject()`:
#> ! invalid class "GraphExperiment" object:
#> 10 feature(s) in 'rownames' are missing from graph 'gene_cor'.Alternatively, you can create a GraphExperiment object by coercing from
an existing (Ranged)SummarizedExperiment or SingleCellExperiment object.
For example:
# Coercing from `SummarizedExperiment`
se <- SummarizedExperiment(list(counts = mat))
ge1 <- as(se, "GraphExperiment")
ge1
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(0):
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(0):
#> colGraphs(0):Note that the rowGraphs/colGraphs containers are still there, but empty.
To access the names of all graphs, you will use the rowGraphNames() and
colGraphNames() functions.
# Get rowGraph names
rowGraphNames(ge) # 'gene_cor'
#> [1] "gene_cor"
rowGraphNames(ge1) # empty (NULL)
#> NULL
# Get colGraph names
colGraphNames(ge) # 'cell_cor'
#> [1] "cell_cor"
colGraphNames(ge1) # empty (NULL)
#> NULL5 Accessing rowGraphs/colGraphs and rowData/colData (a.k.a. ‘getters’)
To access graphs in rowGraphs/colGraphs, you can use one of the following
getter functions:
rowGraphs(x)/colGraphs(x): retrieves all (row/col)Graphs as a list ofigraphobjects.rowGraph(x, i)/colGraph(x, i): retrieves only graph \(i\) from the list. Note that \(i\) can be a numeric scalar (index) or a character scalar (name).
The design here is equivalent to assays() versus assay() for
SummarizedExperiment objects.
# Get rowGraphs
rowGraphs(ge)
#> List of length 1
#> names(1): gene_cor
# Get colGraphs
colGraphs(ge)
#> List of length 1
#> names(1): cell_cor
# Get first rowGraph by index
rowGraph(ge, 1)
#> IGRAPH fad826d UNW- 200 20096 --
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from fad826d (vertex names):
#> [1] gene1--gene1 gene1--gene2 gene1--gene3 gene1--gene4 gene1--gene5
#> [6] gene1--gene6 gene1--gene7 gene1--gene8 gene1--gene9 gene1--gene10
#> [11] gene1--gene11 gene1--gene12 gene1--gene13 gene1--gene14 gene1--gene15
#> [16] gene1--gene16 gene1--gene17 gene1--gene18 gene1--gene19 gene1--gene20
#> [21] gene1--gene21 gene1--gene22 gene1--gene23 gene1--gene24 gene1--gene25
#> [26] gene1--gene26 gene1--gene27 gene1--gene28 gene1--gene29 gene1--gene30
#> [31] gene1--gene31 gene1--gene32 gene1--gene33 gene1--gene34 gene1--gene35
#> + ... omitted several edges
# Get first rowGraph by index (alternative)
rowGraphs(ge)[[1]]
#> IGRAPH fad826d UNW- 200 20096 --
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from fad826d (vertex names):
#> [1] gene1--gene1 gene1--gene2 gene1--gene3 gene1--gene4 gene1--gene5
#> [6] gene1--gene6 gene1--gene7 gene1--gene8 gene1--gene9 gene1--gene10
#> [11] gene1--gene11 gene1--gene12 gene1--gene13 gene1--gene14 gene1--gene15
#> [16] gene1--gene16 gene1--gene17 gene1--gene18 gene1--gene19 gene1--gene20
#> [21] gene1--gene21 gene1--gene22 gene1--gene23 gene1--gene24 gene1--gene25
#> [26] gene1--gene26 gene1--gene27 gene1--gene28 gene1--gene29 gene1--gene30
#> [31] gene1--gene31 gene1--gene32 gene1--gene33 gene1--gene34 gene1--gene35
#> + ... omitted several edges
# Get graph by name
rowGraph(ge, "gene_cor")
#> IGRAPH fad826d UNW- 200 20096 --
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from fad826d (vertex names):
#> [1] gene1--gene1 gene1--gene2 gene1--gene3 gene1--gene4 gene1--gene5
#> [6] gene1--gene6 gene1--gene7 gene1--gene8 gene1--gene9 gene1--gene10
#> [11] gene1--gene11 gene1--gene12 gene1--gene13 gene1--gene14 gene1--gene15
#> [16] gene1--gene16 gene1--gene17 gene1--gene18 gene1--gene19 gene1--gene20
#> [21] gene1--gene21 gene1--gene22 gene1--gene23 gene1--gene24 gene1--gene25
#> [26] gene1--gene26 gene1--gene27 gene1--gene28 gene1--gene29 gene1--gene30
#> [31] gene1--gene31 gene1--gene32 gene1--gene33 gene1--gene34 gene1--gene35
#> + ... omitted several edgesCareful readers will notice that this igraph object has node attributes that
were not present in the original graph: ‘pathway’ and ‘coding’. This is
because rowGraphs()/rowGraph() automatically extract rowData variables
(if any) and add them to node attributes. colGraphs()/colGraph() work
in the same way (but with colData, of course).
The same happens in the other direction: the rowData()/colData() methods
for GraphExperiment objects automatically add node attributes (if any) to
rowData/colData.
# `rowGraphs` and `rowData` are always in sync!
rowData(ge)
#> DataFrame with 200 rows and 3 columns
#> pathway coding gene_cor__degree
#> <character> <logical> <numeric>
#> gene1 P1 FALSE 3.125219
#> gene2 P1 TRUE 4.454433
#> gene3 P2 TRUE 0.873847
#> gene4 P1 FALSE 1.511270
#> gene5 P1 TRUE 0.568855
#> ... ... ... ...
#> gene196 P1 TRUE 0.773387
#> gene197 P2 FALSE 0.857870
#> gene198 P1 TRUE 1.591484
#> gene199 P1 TRUE 2.324144
#> gene200 P1 FALSE -0.887166
# `colGraphs` and `colData` too - yay!
colGraph(ge, 1) # note the `cell_type` attribute extracted from `colData`
#> IGRAPH 6e4bfdb UNW- 100 5050 --
#> + attr: name (v/c), cell_type (v/c), weight (e/n)
#> + edges from 6e4bfdb (vertex names):
#> [1] cell1--cell1 cell1--cell2 cell1--cell3 cell1--cell4 cell1--cell5
#> [6] cell1--cell6 cell1--cell7 cell1--cell8 cell1--cell9 cell1--cell10
#> [11] cell1--cell11 cell1--cell12 cell1--cell13 cell1--cell14 cell1--cell15
#> [16] cell1--cell16 cell1--cell17 cell1--cell18 cell1--cell19 cell1--cell20
#> [21] cell1--cell21 cell1--cell22 cell1--cell23 cell1--cell24 cell1--cell25
#> [26] cell1--cell26 cell1--cell27 cell1--cell28 cell1--cell29 cell1--cell30
#> [31] cell1--cell31 cell1--cell32 cell1--cell33 cell1--cell34 cell1--cell35
#> [36] cell1--cell36 cell1--cell37 cell1--cell38 cell1--cell39 cell1--cell40
#> + ... omitted several edgesVariables ‘pathway’ and ‘coding’ were in the original data frame we used as
rowData, but variable ’gene_cor__degree’ was added by extracting the degree
attribute of nodes in rowGraph gene_cor.
6 Modifying GraphExperiment objects (a.k.a. ‘setters’)
Like in the SummarizedExperiment and SingleCellExperiment classes,
all getter methods specific to GraphExperiment objects have a corresponding
setter method. Such methods allow users to modify elements by adding <-
after the getter method. For example, to add or replace a particular graph,
you would use the rowGraph<-/colGraph<- method as follows:
# Create a new rowGraph without correlations between -0.4 and 0.4
rg_filt <- rowGraph(ge, "gene_cor") |>
delete_vertex_attr("pathway") |>
delete_vertex_attr("degree") |>
delete_vertex_attr("coding")
todelete <- abs(E(rg_filt)$weight) <0.4
rg_filt <- delete_edges(rg_filt, which(todelete))
rg_filt
#> IGRAPH 963f007 UNW- 200 202 --
#> + attr: name (v/c), weight (e/n)
#> + edges from 963f007 (vertex names):
#> [1] gene1 --gene1 gene2 --gene2 gene3 --gene3 gene4 --gene4 gene5 --gene5
#> [6] gene6 --gene6 gene7 --gene7 gene8 --gene8 gene9 --gene9 gene10--gene10
#> [11] gene11--gene11 gene12--gene12 gene13--gene13 gene14--gene14 gene15--gene15
#> [16] gene16--gene16 gene17--gene17 gene18--gene18 gene19--gene19 gene20--gene20
#> [21] gene21--gene21 gene22--gene22 gene23--gene23 gene24--gene24 gene25--gene25
#> [26] gene26--gene26 gene27--gene27 gene28--gene28 gene29--gene29 gene30--gene30
#> [31] gene31--gene31 gene32--gene32 gene33--gene33 gene34--gene34 gene35--gene35
#> [36] gene36--gene36 gene37--gene37 gene38--gene38 gene39--gene39 gene40--gene40
#> + ... omitted several edges
# Add filtered graph a new graph named `fcor`
rowGraph(ge, "filt_genecor") <- rg_filt
ge
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(3): pathway coding gene_cor__degree
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(1): cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(2): gene_cor filt_genecor
#> colGraphs(1): cell_corIf you’d like to replace all graphs at once, you could use the
rowGraphs<-/colGraphs<- setters. For example, let’s add a few graphs to
the GraphExperiment object we created before by coercing
from SummarizedExperiment:
# Taking a quick look (note: nothing in `rowGraphs`/`colGraphs`)
ge1
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(0):
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(0):
#> colGraphs(0):
# Adding graphs from `ge`
rowGraphs(ge1) <- rowGraphs(ge)
colGraphs(ge1) <- colGraphs(ge)
ge1
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(5): gene_cor__degree gene_cor__pathway gene_cor__coding
#> filt_genecor__pathway filt_genecor__coding
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(1): cell_cor__cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(2): gene_cor filt_genecor
#> colGraphs(1): cell_corLastly, you can also rename graphs by updating rowGraphNames/colGraphNames
as follows:
# Rename graphs
rowGraphNames(ge1) <- c("correlations", "correlations_filtered_0.4")
colGraphNames(ge1) <- c("cell_correlations")
ge1
#> class: GraphExperiment
#> dim: 200 100
#> metadata(0):
#> assays(1): counts
#> rownames(200): gene1 gene2 ... gene199 gene200
#> rowData names(5): correlations__degree correlations__pathway
#> correlations__coding correlations_filtered_0.4__pathway
#> correlations_filtered_0.4__coding
#> colnames(100): cell1 cell2 ... cell99 cell100
#> colData names(1): cell_correlations__cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(2): correlations correlations_filtered_0.4
#> colGraphs(1): cell_correlations7 Subsetting GraphExperiment objects
In SummarizedExperiment objects, subsetting rows and columns
(using square brackets, [) automatically subsets rowData and colData
besides the assays. The same is true for SingleCellExperiment objects:
subsetting columns automatically subsets colData and reducedDims.
Since graphs in GraphExperiment objects are linked to rows and columns,
subsetting rows of a GraphExperiment object automatically subsets rows of the
assays, rowData, and all graphs in rowGraphs, and subsetting
columns automatically subsets columns of the assays, colData, and
graphs in colGraphs. For example:
# Subsetting `GraphExperiment` object
ge_subset <- ge[1:10, 1:10]
ge_subset
#> class: GraphExperiment
#> dim: 10 10
#> metadata(0):
#> assays(1): counts
#> rownames(10): gene1 gene2 ... gene9 gene10
#> rowData names(3): pathway coding gene_cor__degree
#> colnames(10): cell1 cell2 ... cell9 cell10
#> colData names(1): cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowGraphs(2): gene_cor filt_genecor
#> colGraphs(1): cell_cor
rowGraph(ge_subset, "gene_cor")
#> IGRAPH 487879d UNW- 10 55 --
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from 487879d (vertex names):
#> [1] gene1--gene1 gene1--gene2 gene2--gene2 gene1--gene3 gene2--gene3
#> [6] gene3--gene3 gene1--gene4 gene2--gene4 gene3--gene4 gene4--gene4
#> [11] gene1--gene5 gene2--gene5 gene3--gene5 gene4--gene5 gene5--gene5
#> [16] gene1--gene6 gene2--gene6 gene3--gene6 gene4--gene6 gene5--gene6
#> [21] gene6--gene6 gene1--gene7 gene2--gene7 gene3--gene7 gene4--gene7
#> [26] gene5--gene7 gene6--gene7 gene7--gene7 gene1--gene8 gene2--gene8
#> [31] gene3--gene8 gene4--gene8 gene5--gene8 gene6--gene8 gene7--gene8
#> + ... omitted several edgesSession information
This document was created under the following conditions:
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Amezquita, Robert, Aaron Lun, Etienne Becht, Vince Carey, Lindsay Carpp, Ludwig Geistlinger, Federico Marini, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17: 137–45. https://www.nature.com/articles/s41592-019-0654-x.