The ReactomeGSA package is a client to the web-based Reactome Analysis System. Essentially, it performs a gene set analysis using the latest version of the Reactome pathway database as a backend.
This vignette shows how the ReactomeGSA package can be used to perform a pathway analysis of cell clusters in single-cell RNA-sequencing data.
To cite this package, use
Griss J. ReactomeGSA, https://github.com/reactome/ReactomeGSA (2019)
The ReactomeGSA
package can be directly installed from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require(ReactomeGSA))
BiocManager::install("ReactomeGSA")
# install the ReactomeGSA.data package for the example data
if (!require(ReactomeGSA.data))
BiocManager::install("ReactomeGSA.data")
For more information, see https://bioconductor.org/install/.
As an example we load single-cell RNA-sequencing data of B cells extracted from the dataset published by Jerby-Arnon et al. (Cell, 2018).
Note: This is not a complete Seurat object. To decrease the size, the object only contains gene expression values and cluster annotations.
library(ReactomeGSA.data)
#> Loading required package: limma
#> Loading required package: edgeR
#> Loading required package: ReactomeGSA
#> Loading required package: Seurat
#> Loading required package: SeuratObject
#> Loading required package: sp
#>
#> Attaching package: 'SeuratObject'
#> The following object is masked from 'package:base':
#>
#> intersect
data(jerby_b_cells)
jerby_b_cells
#> An object of class Seurat
#> 23686 features across 920 samples within 1 assay
#> Active assay: RNA (23686 features, 0 variable features)
#> 2 layers present: counts, data
The pathway analysis is at the very end of a scRNA-seq workflow. This means, that any Q/C was already performed, the data was normalized and cells were already clustered.
The ReactomeGSA package can now be used to get pathway-level expression values for every cell cluster. This is achieved by calculating the mean gene expression for every cluster and then submitting this data to a gene set variation analysis.
All of this is wrapped in the single analyse_sc_clusters
function.
library(ReactomeGSA)
gsva_result <- analyse_sc_clusters(jerby_b_cells, verbose = TRUE)
#> Calculating average cluster expression...
#> Converting expression data to string... (This may take a moment)
#> Conversion complete
#> Submitting request to Reactome API...
#> Compressing request data...
#> Reactome Analysis submitted succesfully
#> Converting dataset Seurat...
#> Mapping identifiers...
#> Performing gene set analysis using ssGSEA
#> Analysing dataset 'Seurat' using ssGSEA
#> Retrieving result...
The resulting object is a standard ReactomeAnalysisResult
object.
gsva_result
#> ReactomeAnalysisResult object
#> Reactome Release: 86
#> Results:
#> - Seurat:
#> 1773 pathways
#> 11122 fold changes for genes
#> No Reactome visualizations available
#> ReactomeAnalysisResult
pathways
returns the pathway-level expression values per cell cluster:
pathway_expression <- pathways(gsva_result)
# simplify the column names by removing the default dataset identifier
colnames(pathway_expression) <- gsub("\\.Seurat", "", colnames(pathway_expression))
pathway_expression[1:3,]
#> Name Cluster_1 Cluster_10 Cluster_11
#> R-HSA-1059683 Interleukin-6 signaling 0.1092170 0.09778349 0.1470607
#> R-HSA-109606 Intrinsic Pathway for Apoptosis 0.1175318 0.11313998 0.1158441
#> R-HSA-109703 PKB-mediated events 0.1273845 0.05246229 0.1064824
#> Cluster_12 Cluster_13 Cluster_2 Cluster_3 Cluster_4 Cluster_5
#> R-HSA-1059683 0.10983399 0.10497527 0.11718678 0.11532617 0.11482856 0.10741936
#> R-HSA-109606 0.11991881 0.13197679 0.11289277 0.11524909 0.11788849 0.10862934
#> R-HSA-109703 0.09546617 0.07332126 0.08331065 0.08430835 0.05562504 0.04623421
#> Cluster_6 Cluster_7 Cluster_8 Cluster_9
#> R-HSA-1059683 0.09962466 0.12065203 0.13868658 0.10552683
#> R-HSA-109606 0.11353425 0.12080057 0.12515946 0.11837913
#> R-HSA-109703 0.12392905 0.07718021 0.07833784 0.01423891
A simple approach to find the most relevant pathways is to assess the maximum difference in expression for every pathway:
# find the maximum differently expressed pathway
max_difference <- do.call(rbind, apply(pathway_expression, 1, function(row) {
values <- as.numeric(row[2:length(row)])
return(data.frame(name = row[1], min = min(values), max = max(values)))
}))
max_difference$diff <- max_difference$max - max_difference$min
# sort based on the difference
max_difference <- max_difference[order(max_difference$diff, decreasing = T), ]
head(max_difference)
#> name min
#> R-HSA-350864 Regulation of thyroid hormone activity -0.4875863
#> R-HSA-8964540 Alanine metabolism -0.5061732
#> R-HSA-190374 FGFR1c and Klotho ligand binding and activation -0.3432648
#> R-HSA-140180 COX reactions -0.3450870
#> R-HSA-9024909 BDNF activates NTRK2 (TRKB) signaling -0.3755033
#> R-HSA-9025046 NTF3 activates NTRK2 (TRKB) signaling -0.3963853
#> max diff
#> R-HSA-350864 0.3756096 0.8631959
#> R-HSA-8964540 0.2560705 0.7622437
#> R-HSA-190374 0.4160633 0.7593281
#> R-HSA-140180 0.3723295 0.7174166
#> R-HSA-9024909 0.3235067 0.6990100
#> R-HSA-9025046 0.2993049 0.6956902
The ReactomeGSA package contains two functions to visualize these pathway results. The first simply plots the expression for a selected pathway:
For a better overview, the expression of multiple pathways can be shown as a heatmap using gplots
heatmap.2
function:
# Additional parameters are directly passed to gplots heatmap.2 function
plot_gsva_heatmap(gsva_result, max_pathways = 15, margins = c(6,20))
The plot_gsva_heatmap
function can also be used to only display specific pahtways:
# limit to selected B cell related pathways
relevant_pathways <- c("R-HSA-983170", "R-HSA-388841", "R-HSA-2132295", "R-HSA-983705", "R-HSA-5690714")
plot_gsva_heatmap(gsva_result,
pathway_ids = relevant_pathways, # limit to these pathways
margins = c(6,30), # adapt the figure margins in heatmap.2
dendrogram = "col", # only plot column dendrogram
scale = "row", # scale for each pathway
key = FALSE, # don't display the color key
lwid=c(0.1,4)) # remove the white space on the left
This analysis shows us that cluster 8 has a marked up-regulation of B Cell receptor signalling, which is linked to a co-stimulation of the CD28 family. Additionally, there is a gradient among the cluster with respect to genes releated to antigen presentation.
Therefore, we are able to further classify the observed B cell subtypes based on their pathway activity.
The pathway-level expression analysis can also be used to run a Principal Component Analysis on the samples. This is simplified through the function plot_gsva_pca
:
In this analysis, cluster 11 is a clear outlier from the other B cell subtypes and therefore might be prioritised for further evaluation.
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