inst/doc/analysing-scRNAseq.R

In ReactomeGSA: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
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))
  BiocManager::install("ReactomeGSA.data")

## -----------------------------------------------------------------------------
library(ReactomeGSA.data)
data(jerby_b_cells)

jerby_b_cells

## -----------------------------------------------------------------------------
library(ReactomeGSA)

gsva_result <- analyse_sc_clusters(jerby_b_cells, verbose = TRUE)

## -----------------------------------------------------------------------------
gsva_result

## -----------------------------------------------------------------------------
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,]

## -----------------------------------------------------------------------------
# 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)

## ---- fig.width=7, fig.height=4-----------------------------------------------
plot_gsva_pathway(gsva_result, pathway_id = rownames(max_difference)[1])

## ---- fig.width=7, fig.height=8-----------------------------------------------
# Additional parameters are directly passed to gplots heatmap.2 function
plot_gsva_heatmap(gsva_result, max_pathways = 15, margins = c(6,20))

## ---- fig.width=7, fig.height=4-----------------------------------------------
# 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

## ---- fig.width=6, fig.height=4-----------------------------------------------
plot_gsva_pca(gsva_result)

## -----------------------------------------------------------------------------
sessionInfo()