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: 74
#>   Results:
#>   - Seurat:
#>     1723 pathways
#>     10728 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 0.10334592 0.09666255 0.14251763
#> R-HSA-109606  Intrinsic Pathway for Apoptosis 0.10525530 0.10528882 0.10817361
#> R-HSA-109703              PKB-mediated events 0.09708567 0.02866064 0.09485118
#>               Cluster_12 Cluster_13  Cluster_2  Cluster_3 Cluster_4  Cluster_5
#> R-HSA-1059683  0.1071082 0.10752020 0.11754258 0.10910198 0.1104005 0.10829945
#> R-HSA-109606   0.1108030 0.12184146 0.09976769 0.10788626 0.1068078 0.09857598
#> R-HSA-109703   0.1172487 0.05920051 0.05643768 0.08710097 0.0505033 0.04704229
#>                Cluster_6  Cluster_7  Cluster_8   Cluster_9
#> R-HSA-1059683 0.09547943 0.11350268 0.12997181  0.10159703
#> R-HSA-109606  0.10562406 0.11320979 0.11622302  0.11388589
#> R-HSA-109703  0.09465369 0.04818709 0.04534148 -0.01169581

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 -0.4470641
#> R-HSA-350864           Regulation of thyroid hormone activity -0.4872210
#> R-HSA-8964540                              Alanine metabolism -0.5063074
#> R-HSA-190374  FGFR1c and Klotho ligand binding and activation -0.3445460
#> R-HSA-140180                                    COX reactions -0.3475696
#> R-HSA-5263617       Metabolism of ingested MeSeO2H into MeSeH -0.1940543
#>                     max      diff
#> R-HSA-389542  0.4200409 0.8671051
#> R-HSA-350864  0.3738369 0.8610579
#> R-HSA-8964540 0.2538387 0.7601461
#> R-HSA-190374  0.4147497 0.7592957
#> R-HSA-140180  0.3713802 0.7189498
#> R-HSA-5263617 0.4936926 0.6877470

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 4.0.3 (2020-10-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.12-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_1.3.0 Seurat_3.2.2           edgeR_3.32.0          
#> [4] limma_3.46.0           ReactomeGSA_1.4.0     
#> 
#> loaded via a namespace (and not attached):
#>   [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.1-29        
#>   [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_4.0.3        
#>  [13] knitr_1.30            polyclip_1.10-0       jsonlite_1.7.1       
#>  [16] ica_1.0-2             cluster_2.1.0         png_0.1-7            
#>  [19] uwot_0.1.8            shiny_1.5.0           sctransform_0.3.1    
#>  [22] BiocManager_1.30.10   compiler_4.0.3        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_4.0.3           rsvd_1.0.3            igraph_1.2.6         
#>  [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] nlme_3.1-150          lmtest_0.9-38         xfun_0.18            
#>  [46] stringr_1.4.0         globals_0.13.1        mime_0.9             
#>  [49] miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3          
#>  [52] gtools_3.8.2          goftest_1.2-2         future_1.19.1        
#>  [55] MASS_7.3-53           zoo_1.8-8             scales_1.1.1         
#>  [58] hms_0.5.3             promises_1.1.1        spatstat.utils_1.17-0
#>  [61] parallel_4.0.3        RColorBrewer_1.1-2    yaml_2.2.1           
#>  [64] curl_4.3              reticulate_1.18       pbapply_1.4-3        
#>  [67] gridExtra_2.3         ggplot2_3.3.2         rpart_4.1-15         
#>  [70] stringi_1.5.3         caTools_1.18.0        rlang_0.4.8          
#>  [73] pkgconfig_2.0.3       matrixStats_0.57.0    bitops_1.0-6         
#>  [76] evaluate_0.14         lattice_0.20-41       ROCR_1.0-11          
#>  [79] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
#>  [82] patchwork_1.0.1       htmlwidgets_1.5.2     cowplot_1.1.0        
#>  [85] tidyselect_1.1.0      RcppAnnoy_0.0.16      plyr_1.8.6           
#>  [88] magrittr_1.5          R6_2.4.1              gplots_3.1.0         
#>  [91] generics_0.0.2        pillar_1.4.6          mgcv_1.8-33          
#>  [94] fitdistrplus_1.1-1    survival_3.2-7        abind_1.4-5          
#>  [97] tibble_3.0.4          future.apply_1.6.0    crayon_1.3.4         
#> [100] KernSmooth_2.23-17    plotly_4.9.2.1        rmarkdown_2.5        
#> [103] progress_1.2.2        locfit_1.5-9.4        grid_4.0.3           
#> [106] data.table_1.13.2     digest_0.6.27         xtable_1.8-4         
#> [109] tidyr_1.1.2           httpuv_1.5.4          munsell_0.5.0        
#> [112] viridisLite_0.3.0