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
#> 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: 73
#>   Results:
#>   - Seurat:
#>     1722 pathways
#>     10627 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 0.1032524 0.09356161  0.1412498
#> R-HSA-109606  Intrinsic Pathway for Apoptosis 0.1136558 0.11283812  0.1139654
#> R-HSA-109703              PKB-mediated events 0.0971882 0.02846737  0.0946912
#>               Cluster_12 Cluster_13  Cluster_2  Cluster_3  Cluster_4  Cluster_5
#> R-HSA-1059683  0.1072879 0.10123552 0.11344316 0.10793976 0.10716422 0.10226196
#> R-HSA-109606   0.1170965 0.12637114 0.10707198 0.11559951 0.11123559 0.10213139
#> R-HSA-109703   0.1170903 0.05873329 0.05625757 0.08730618 0.05040619 0.04703383
#>                Cluster_6  Cluster_7  Cluster_8   Cluster_9
#> R-HSA-1059683 0.09615926 0.11386514 0.13528812  0.10648549
#> R-HSA-109606  0.11229148 0.12011620 0.12217230  0.11730710
#> R-HSA-109703  0.09461243 0.04830705 0.04526926 -0.01201461

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-389542                               NADPH regeneration -0.4476989
#> R-HSA-350864           Regulation of thyroid hormone activity -0.4877657
#> R-HSA-8964540                              Alanine metabolism -0.5063681
#> R-HSA-190374  FGFR1c and Klotho ligand binding and activation -0.3448607
#> R-HSA-140180                                    COX reactions -0.3475319
#> R-HSA-9024909           BDNF activates NTRK2 (TRKB) signaling -0.3749056
#>                     max      diff
#> R-HSA-389542  0.4197475 0.8674464
#> R-HSA-350864  0.3733845 0.8611502
#> R-HSA-8964540 0.2535657 0.7599338
#> R-HSA-190374  0.4145007 0.7593614
#> R-HSA-140180  0.3709996 0.7185315
#> R-HSA-9024909 0.3217632 0.6966687

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 4.0.2 (2020-06-22)
#> 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_1.2.0 Seurat_3.2.0           edgeR_3.30.3          
#> [4] limma_3.44.3           ReactomeGSA_1.2.4     
#> 
#> loaded via a namespace (and not attached):
#>   [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.1-28        
#>   [4] ellipsis_0.3.1        ggridges_0.5.2        spatstat.data_1.4-3  
#>   [7] farver_2.0.3          leiden_0.3.3          listenv_0.8.0        
#>  [10] ggrepel_0.8.2         codetools_0.2-16      splines_4.0.2        
#>  [13] knitr_1.29            polyclip_1.10-0       jsonlite_1.7.0       
#>  [16] ica_1.0-2             cluster_2.1.0         png_0.1-7            
#>  [19] uwot_0.1.8            shiny_1.5.0           sctransform_0.2.1    
#>  [22] BiocManager_1.30.10   compiler_4.0.2        httr_1.4.2           
#>  [25] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
#>  [28] later_1.1.0.1         prettyunits_1.1.1     htmltools_0.5.0      
#>  [31] tools_4.0.2           rsvd_1.0.3            igraph_1.2.5         
#>  [34] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
#>  [37] reshape2_1.4.4        dplyr_1.0.2           rappdirs_0.3.1       
#>  [40] Rcpp_1.0.5            spatstat_1.64-1       vctrs_0.3.4          
#>  [43] gdata_2.18.0          ape_5.4-1             nlme_3.1-149         
#>  [46] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
#>  [49] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
#>  [52] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
#>  [55] goftest_1.2-2         future_1.18.0         MASS_7.3-52          
#>  [58] zoo_1.8-8             scales_1.1.1          hms_0.5.3            
#>  [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_4.0.2       
#>  [64] RColorBrewer_1.1-2    yaml_2.2.1            curl_4.3             
#>  [67] reticulate_1.16       pbapply_1.4-3         gridExtra_2.3        
#>  [70] ggplot2_3.3.2         rpart_4.1-15          stringi_1.4.6        
#>  [73] caTools_1.18.0        rlang_0.4.7           pkgconfig_2.0.3      
#>  [76] bitops_1.0-6          evaluate_0.14         lattice_0.20-41      
#>  [79] ROCR_1.0-11           purrr_0.3.4           tensor_1.5           
#>  [82] labeling_0.3          patchwork_1.0.1       htmlwidgets_1.5.1    
#>  [85] cowplot_1.0.0         tidyselect_1.1.0      RcppAnnoy_0.0.16     
#>  [88] plyr_1.8.6            magrittr_1.5          R6_2.4.1             
#>  [91] gplots_3.0.4          generics_0.0.2        pillar_1.4.6         
#>  [94] mgcv_1.8-33           fitdistrplus_1.1-1    survival_3.2-3       
#>  [97] abind_1.4-5           tibble_3.0.3          future.apply_1.6.0   
#> [100] crayon_1.3.4          KernSmooth_2.23-17    plotly_4.9.2.1       
#> [103] rmarkdown_2.3         progress_1.2.2        locfit_1.5-9.4       
#> [106] grid_4.0.2            data.table_1.13.0     digest_0.6.25        
#> [109] xtable_1.8-4          tidyr_1.1.2           httpuv_1.5.4         
#> [112] munsell_0.5.0         viridisLite_0.3.0