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))
BiocManager::install("ReactomeGSA.data")
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)
#> Loading required package: limma
#> Loading required package: edgeR
#> Loading required package: Seurat
#> Attaching SeuratObject
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: 75
#> Results:
#> - Seurat:
#> 1726 pathways
#> 10877 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 0.1032472 0.10008086 0.1497649
#> R-HSA-109606 Intrinsic Pathway for Apoptosis 0.1095918 0.10716671 0.1123212
#> R-HSA-109703 PKB-mediated events 0.1271271 0.05247245 0.1060551
#> Cluster_12 Cluster_13 Cluster_2 Cluster_3 Cluster_4 Cluster_5
#> R-HSA-1059683 0.10709318 0.1079596 0.11536591 0.11046754 0.11324535 0.10860458
#> R-HSA-109606 0.11484536 0.1264604 0.10426341 0.10737779 0.11095973 0.10312804
#> R-HSA-109703 0.09532549 0.0729352 0.08280237 0.08390533 0.05531344 0.04627873
#> Cluster_6 Cluster_7 Cluster_8 Cluster_9
#> R-HSA-1059683 0.09265093 0.11630862 0.13375275 0.10534315
#> R-HSA-109606 0.10819825 0.11376882 0.11859846 0.11439947
#> R-HSA-109703 0.12356494 0.07705906 0.07798356 0.01401364
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 -0.4872121
#> R-HSA-8964540 Alanine metabolism -0.5062198
#> R-HSA-190374 FGFR1c and Klotho ligand binding and activation -0.3440953
#> R-HSA-140180 COX reactions -0.3451202
#> R-HSA-9024909 BDNF activates NTRK2 (TRKB) signaling -0.3747605
#> R-HSA-9025046 NTF3 activates NTRK2 (TRKB) signaling -0.3957010
#> max diff
#> R-HSA-350864 0.3746567 0.8618688
#> R-HSA-8964540 0.2550200 0.7612398
#> R-HSA-190374 0.4152810 0.7593763
#> R-HSA-140180 0.3719546 0.7170748
#> R-HSA-9024909 0.3228439 0.6976044
#> R-HSA-9025046 0.2986478 0.6943488
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.