Analysing single-cell RNA-sequencing Data

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.

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
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: 72
#>   Results:
#>   - Seurat:
#>     1722 pathways
#>     11228 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 0.09549581 0.08293384  0.1350809
#> R-HSA-109606  Intrinsic Pathway for Apoptosis 0.09638458 0.09340358  0.1059640
#> R-HSA-109703              PKB-mediated events 0.16843847 0.12249589  0.1391007
#>               Cluster_12 Cluster_13  Cluster_2  Cluster_3  Cluster_4  Cluster_5
#> R-HSA-1059683 0.09702636 0.09623266 0.10116027 0.09434008 0.10323660 0.09643691
#> R-HSA-109606  0.10559409 0.12427241 0.09116051 0.09898600 0.10020046 0.09256660
#> R-HSA-109703  0.16069309 0.08955986 0.13484563 0.13795633 0.09786222 0.09717648
#>                Cluster_6 Cluster_7 Cluster_8  Cluster_9
#> R-HSA-1059683 0.08203173 0.1056159 0.1250548 0.10137450
#> R-HSA-109606  0.09312198 0.1069164 0.1082075 0.10149058
#> R-HSA-109703  0.19192954 0.1529008 0.1505227 0.06424437

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-389542                                                        NADPH regeneration
#> R-HSA-8964540                                                       Alanine metabolism
#> R-HSA-140180                                                             COX reactions
#> R-HSA-5263617                                Metabolism of ingested MeSeO2H into MeSeH
#> R-HSA-8981607                                           Intracellular oxygen transport
#> R-HSA-141333  Biogenic amines are oxidatively deaminated to aldehydes by MAOA and MAOB
#>                      min       max      diff
#> R-HSA-389542  -0.4453421 0.4221523 0.8674944
#> R-HSA-8964540 -0.5060685 0.2554931 0.7615616
#> R-HSA-140180  -0.4869392 0.2338373 0.7207765
#> R-HSA-5263617 -0.1928000 0.4939315 0.6867315
#> R-HSA-8981607 -0.3571850 0.3084595 0.6656445
#> R-HSA-141333  -0.4631178 0.1703137 0.6334315

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.

Session Info

sessionInfo()
#> R version 4.0.0 (2020-04-24)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ReactomeGSA.data_1.1.1 Seurat_3.1.5           edgeR_3.30.0          
#> [4] limma_3.44.1           ReactomeGSA_1.2.0     
#> 
#> loaded via a namespace (and not attached):
#>  [1] tsne_0.1-3          nlme_3.1-147        bitops_1.0-6       
#>  [4] progress_1.2.2      RcppAnnoy_0.0.16    RColorBrewer_1.1-2 
#>  [7] httr_1.4.1          sctransform_0.2.1   tools_4.0.0        
#> [10] R6_2.4.1            irlba_2.3.3         KernSmooth_2.23-17 
#> [13] uwot_0.1.8          lazyeval_0.2.2      colorspace_1.4-1   
#> [16] npsurv_0.4-0        prettyunits_1.1.1   gridExtra_2.3      
#> [19] tidyselect_1.0.0    curl_4.3            compiler_4.0.0     
#> [22] plotly_4.9.2.1      labeling_0.3        caTools_1.18.0     
#> [25] scales_1.1.0        lmtest_0.9-37       ggridges_0.5.2     
#> [28] pbapply_1.4-2       rappdirs_0.3.1      stringr_1.4.0      
#> [31] digest_0.6.25       rmarkdown_2.1       pkgconfig_2.0.3    
#> [34] htmltools_0.4.0     htmlwidgets_1.5.1   rlang_0.4.5        
#> [37] farver_2.0.3        zoo_1.8-7           jsonlite_1.6.1     
#> [40] ica_1.0-2           gtools_3.8.2        dplyr_0.8.5        
#> [43] magrittr_1.5        patchwork_1.0.0     Matrix_1.2-18      
#> [46] Rcpp_1.0.4.6        munsell_0.5.0       ape_5.3            
#> [49] reticulate_1.15     lifecycle_0.2.0     stringi_1.4.6      
#> [52] yaml_2.2.1          MASS_7.3-51.6       gplots_3.0.3       
#> [55] Rtsne_0.15          plyr_1.8.6          grid_4.0.0         
#> [58] parallel_4.0.0      gdata_2.18.0        listenv_0.8.0      
#> [61] ggrepel_0.8.2       crayon_1.3.4        lattice_0.20-41    
#> [64] cowplot_1.0.0       splines_4.0.0       hms_0.5.3          
#> [67] locfit_1.5-9.4      knitr_1.28          pillar_1.4.3       
#> [70] igraph_1.2.5        future.apply_1.5.0  reshape2_1.4.4     
#> [73] codetools_0.2-16    leiden_0.3.3        glue_1.4.0         
#> [76] evaluate_0.14       lsei_1.2-0          data.table_1.12.8  
#> [79] BiocManager_1.30.10 vctrs_0.2.4         png_0.1-7          
#> [82] gtable_0.3.0        RANN_2.6.1          purrr_0.3.4        
#> [85] tidyr_1.0.2         future_1.17.0       assertthat_0.2.1   
#> [88] ggplot2_3.3.0       xfun_0.13           rsvd_1.0.3         
#> [91] survival_3.1-12     viridisLite_0.3.0   tibble_3.0.1       
#> [94] cluster_2.1.0       globals_0.12.5      fitdistrplus_1.0-14
#> [97] ellipsis_0.3.0      ROCR_1.0-7