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")
# 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/.
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: ReactomeGSA
#> Loading required package: Seurat
#> Loading required package: SeuratObject
#> Loading required package: sp
#>
#> Attaching package: 'SeuratObject'
#> The following objects are masked from 'package:base':
#>
#> intersect, t
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
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
#> Updating to new REACTOME version...
#> 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: 90
#> Results:
#> - Seurat:
#> 1741 pathways
#> 11759 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 -0.03556065 -0.04521415 0.14931444
#> R-HSA-109703 PKB-mediated events 0.32848879 -0.19771237 0.05087568
#> R-HSA-109704 PI3K Cascade -0.12080150 -0.13450596 0.16088061
#> Cluster_12 Cluster_13 Cluster_2 Cluster_3 Cluster_4
#> R-HSA-1059683 0.06075330 0.0004459001 0.14896349 -0.09445591 -0.11884117
#> R-HSA-109703 0.09556102 0.0274677841 -0.05142555 0.15462891 -0.16856699
#> R-HSA-109704 -0.04114424 0.0680222582 0.05924518 0.05295837 -0.02208554
#> Cluster_5 Cluster_6 Cluster_7 Cluster_8 Cluster_9
#> R-HSA-1059683 -0.13882420 -0.07577576 -0.06560393 0.174814091 -0.04388071
#> R-HSA-109703 0.10554980 -0.06251424 -0.30506281 -0.007670405 -0.09929333
#> R-HSA-109704 0.02008053 -0.12603775 -0.01371145 0.101810119 -0.04142070
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
#> R-HSA-140180 COX reactions
#> R-HSA-1296067 Potassium transport channels
#> R-HSA-392023 Adrenaline signalling through Alpha-2 adrenergic receptor
#> R-HSA-8964540 Alanine metabolism
#> R-HSA-3248023 Regulation by TREX1
#> R-HSA-350864 Regulation of thyroid hormone activity
#> min max diff
#> R-HSA-140180 -0.9647899 0.9841810 1.948971
#> R-HSA-1296067 -1.0000000 0.8858649 1.885865
#> R-HSA-392023 -0.9008335 0.9738051 1.874639
#> R-HSA-8964540 -0.8731077 0.9938765 1.866984
#> R-HSA-3248023 -0.9185236 0.9421670 1.860691
#> R-HSA-350864 -0.9134206 0.9394455 1.852866
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
#> Warning in plot_gsva_heatmap(gsva_result, pathway_ids = relevant_pathways, :
#> Warning: No results for the following pathways: R-HSA-983705
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.