Introduction
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.
Citation
To cite this package, use
Griss J. ReactomeGSA, https://github.com/reactome/ReactomeGSA (2019)
Installation
The ReactomeGSA package can be directly installed from
Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require(ReactomeGSA))
BiocManager::install("ReactomeGSA")
#> Loading required package: ReactomeGSA
# install the ReactomeGSA.data package for the example data
if (!require(ReactomeGSA.data))
BiocManager::install("ReactomeGSA.data")
#> Loading required package: ReactomeGSA.data
#> Loading required package: limma
#> Loading required package: edgeR
#> Loading required package: Seurat
#> Attaching SeuratObject
#> Attaching sp
For more information, see https://bioconductor.org/install/.
Example data
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)
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)
Pathway analysis of cell clusters
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: 82
#> Results:
#> - Seurat:
#> 1748 pathways
#> 11011 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 0.1082226 0.09933853 0.1419730
#> R-HSA-109606 Intrinsic Pathway for Apoptosis 0.1120679 0.10765182 0.1129435
#> R-HSA-109703 PKB-mediated events 0.1270311 0.05260470 0.1062198
#> Cluster_12 Cluster_13 Cluster_2 Cluster_3 Cluster_4 Cluster_5
#> R-HSA-1059683 0.11064796 0.10185627 0.11596313 0.11297619 0.11265103 0.10463467
#> R-HSA-109606 0.11687287 0.12568925 0.10690499 0.10872697 0.11114136 0.10329045
#> R-HSA-109703 0.09538291 0.07349972 0.08293139 0.08383722 0.05544193 0.04586484
#> Cluster_6 Cluster_7 Cluster_8 Cluster_9
#> R-HSA-1059683 0.09747205 0.12272050 0.13383987 0.1012149
#> R-HSA-109606 0.10807538 0.11564920 0.11937146 0.1125818
#> R-HSA-109703 0.12325952 0.07692891 0.07796547 0.0142512
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 -0.4876458
#> R-HSA-8964540 Alanine metabolism -0.5061431
#> R-HSA-190374 FGFR1c and Klotho ligand binding and activation -0.3434411
#> R-HSA-140180 COX reactions -0.3450976
#> R-HSA-9024909 BDNF activates NTRK2 (TRKB) signaling -0.3757822
#> R-HSA-5263617 Metabolism of ingested MeSeO2H into MeSeH -0.1941002
#> max diff
#> R-HSA-350864 0.3752821 0.8629279
#> R-HSA-8964540 0.2551881 0.7613312
#> R-HSA-190374 0.4157735 0.7592146
#> R-HSA-140180 0.3721532 0.7172508
#> R-HSA-9024909 0.3232355 0.6990177
#> R-HSA-5263617 0.4938569 0.6879571
Plotting the results
The ReactomeGSA package contains two functions to visualize these
pathway results. The first simply plots the expression for a selected
pathway:
plot_gsva_pathway(gsva_result, pathway_id = rownames(max_difference)[1])
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.
Pathway-level PCA
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:
plot_gsva_pca(gsva_result)
In this analysis, cluster 11 is a clear outlier from the other B cell
subtypes and therefore might be prioritised for further evaluation.