Introduction to the GraphExperiment class

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.

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 reproducibility

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

  • rowGraphs: list of igraph objects containing graphs representing feature-feature relationships, including (but optional) node and edge attributes.
  • colGraphs: list of igraph objects containing graphs representing observation-observation relationships, including (but optional) node and edge attributes.
The GraphExperiment class.

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.

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.

# 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 306da6d UNW- 200 20096 -- 
#> + attr: name (v/c), degree (v/n), weight (e/n)
#> + edges from 306da6d (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_cor

If 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)
#> NULL

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 of igraph objects.
  • 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 306da6d UNW- 200 20096 -- 
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from 306da6d (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 306da6d UNW- 200 20096 -- 
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from 306da6d (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 306da6d UNW- 200 20096 -- 
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from 306da6d (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

Careful 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 5e145ac UNW- 100 5050 -- 
#> + attr: name (v/c), cell_type (v/c), weight (e/n)
#> + edges from 5e145ac (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 edges

Variables ‘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.

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 316dfd8 UNW- 200 202 -- 
#> + attr: name (v/c), weight (e/n)
#> + edges from 316dfd8 (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_cor

If 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_cor

Lastly, 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_correlations

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 bd5c829 UNW- 10 55 -- 
#> + attr: name (v/c), degree (v/n), pathway (v/c), coding (v/l), weight
#> | (e/n)
#> + edges from bd5c829 (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 edges

Session information

This document was created under the following conditions:

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.6.0 (2026-04-24)
#>  os       Ubuntu 24.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Etc/UTC
#>  date     2026-05-29
#>  pandoc   3.8.3 @ /usr/local/bin/ (via rmarkdown)
#>  quarto   1.9.37 @ /usr/local/bin/quarto
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package              * version date (UTC) lib source
#>  abind                  1.4-8   2024-09-12 [2] RSPM (R 4.6.0)
#>  Biobase              * 2.72.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  BiocBaseUtils          1.14.2  2026-05-26 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  BiocGenerics         * 0.58.1  2026-05-14 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  BiocManager            1.30.27 2025-11-14 [2] RSPM (R 4.6.0)
#>  BiocStyle            * 2.40.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  bslib                  0.11.0  2026-05-16 [2] RSPM (R 4.6.0)
#>  buildtools             1.0.0   2026-05-22 [3] local (/pkg)
#>  cachem                 1.1.0   2024-05-16 [2] RSPM (R 4.6.0)
#>  cli                    3.6.6   2026-04-09 [2] RSPM (R 4.6.0)
#>  DelayedArray           0.38.2  2026-05-26 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  digest                 0.6.39  2025-11-19 [2] RSPM (R 4.6.0)
#>  evaluate               1.0.5   2025-08-27 [2] RSPM (R 4.6.0)
#>  fastmap                1.2.0   2024-05-15 [2] RSPM (R 4.6.0)
#>  generics             * 0.1.4   2025-05-09 [2] RSPM (R 4.6.0)
#>  GenomicRanges        * 1.64.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  GraphExperiment      * 1.0.1   2026-05-22 [1] https://bioc-release.r-universe.dev (R 4.6.0)
#>  htmltools              0.5.9   2025-12-04 [2] RSPM (R 4.6.0)
#>  igraph               * 2.3.1   2026-05-04 [2] RSPM (R 4.6.0)
#>  IRanges              * 2.46.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  jquerylib              0.1.4   2021-04-26 [2] RSPM (R 4.6.0)
#>  jsonlite               2.0.0   2025-03-27 [2] RSPM (R 4.6.0)
#>  knitr                  1.51    2025-12-20 [2] RSPM (R 4.6.0)
#>  lattice                0.22-9  2026-02-09 [2] RSPM (R 4.6.0)
#>  lifecycle              1.0.5   2026-01-08 [2] RSPM (R 4.6.0)
#>  magrittr               2.0.5   2026-04-04 [2] RSPM (R 4.6.0)
#>  maketools              1.3.2   2025-01-25 [3] RSPM (R 4.6.0)
#>  Matrix                 1.7-5   2026-03-21 [2] RSPM (R 4.6.0)
#>  MatrixGenerics       * 1.24.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  matrixStats          * 1.5.0   2025-01-07 [2] RSPM (R 4.6.0)
#>  pkgconfig              2.0.3   2019-09-22 [2] RSPM (R 4.6.0)
#>  R6                     2.6.1   2025-02-15 [2] RSPM (R 4.6.0)
#>  rlang                  1.2.0   2026-04-06 [2] RSPM (R 4.6.0)
#>  rmarkdown              2.31    2026-03-26 [2] RSPM (R 4.6.0)
#>  S4Arrays               1.12.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  S4Vectors            * 0.50.1  2026-05-03 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  sass                   0.4.10  2025-04-11 [2] RSPM (R 4.6.0)
#>  Seqinfo              * 1.2.0   2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  sessioninfo            1.2.3   2025-02-05 [2] RSPM (R 4.6.0)
#>  SingleCellExperiment * 1.34.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  SparseArray            1.12.2  2026-05-01 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  SummarizedExperiment * 1.42.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  sys                    3.4.3   2024-10-04 [2] RSPM (R 4.6.0)
#>  xfun                   0.57    2026-03-20 [2] RSPM (R 4.6.0)
#>  XVector                0.52.0  2026-04-28 [2] https://bioc-release.r-universe.dev (R 4.6.0)
#>  yaml                   2.3.12  2025-12-10 [2] RSPM (R 4.6.0)
#> 
#>  [1] /tmp/Rtmp4VLLPY/Rinstd9729c0f8c
#>  [2] /github/workspace/pkglib
#>  [3] /usr/local/lib/R/site-library
#>  [4] /usr/lib/R/site-library
#>  [5] /usr/lib/R/library
#>  * ── Packages attached to the search path.
#> 
#> ──────────────────────────────────────────────────────────────────────────────

References

Amezquita, Robert, Aaron Lun, Etienne Becht, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17: 137–45. https://www.nature.com/articles/s41592-019-0654-x.

  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).↩︎

  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.↩︎