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

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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.

The main advantages of using the Reactome Analysis System are:

• Simultaneous analysis and visualization of different types of 'omics data
• Support for direct comparison across different species
• Directly linked with Reactome's powerful pathway browser producing publication-ready figures of your gene set analysis

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")

For more information, see https://bioconductor.org/install/.

Getting available methods

The Reactome Analysis System will be continuously updated. Before starting your analysis it is therefore a good approach to check which methods are available.

This can simply be done by using:

library(ReactomeGSA)

available_methods <- get_reactome_methods(print_methods = FALSE, return_result = TRUE)

# only show the names of the available methods
available_methods$name

To get more information about a specific method, set print_details to TRUE and specify the method:

# Use this command to print the description of the specific method to the console
# get_reactome_methods(print_methods = TRUE, print_details = TRUE, method = "PADOG", return_result = FALSE)

# show the parameter names for the method
padog_params <- available_methods$parameters[available_methods$name == "PADOG"][[1]]

paste0(padog_params$name, " (", padog_params$type, ", ", padog_params$default, ")")

Creating an analysis request

To start a gene set analysis, you first have to create an analysis request. This is a simple S4 class that takes care of submitting multiple datasets simultaneously to the analysis system.

When creating the request object, you already have to specify the analysis method you want to use:

# Create a new request object using 'Camera' for the gene set analysis
my_request <-ReactomeAnalysisRequest(method = "Camera")

my_request

Setting parameters

To get a list of supported parameters for each method, use the get_reactome_methods function (see above).

Parameters are simply set using the set_parameters function:

# set the maximum number of allowed missing values to 50%
my_request <- set_parameters(request = my_request, max_missing_values = 0.5)

my_request

Multiple parameters can by set simulataneously by simply adding more name-value pairs to the function call.

Adding datasets

One analysis request can contain multiple datasets. This can be used to, for example, visualize the results of an RNA-seq and Proteomics experiment (of the same / similar samples) side by side:

library(ReactomeGSA.data)
data("griss_melanoma_proteomics")

This is a limma EList object with the sample data already added

class(griss_melanoma_proteomics)
head(griss_melanoma_proteomics$samples)

The dataset can now simply be added to the request using the add_dataset function:

my_request <- add_dataset(request = my_request, 
                          expression_values = griss_melanoma_proteomics, 
                          name = "Proteomics", 
                          type = "proteomics_int",
                          comparison_factor = "condition", 
                          comparison_group_1 = "MOCK", 
                          comparison_group_2 = "MCM",
                          additional_factors = c("cell.type", "patient.id"))
my_request

Several datasets (of the same experiment) can be added to one request. This RNA-seq data is stored as an edgeR DGEList object:

data("griss_melanoma_rnaseq")

# only keep genes with >= 100 reads in total
total_reads <- rowSums(griss_melanoma_rnaseq$counts)
griss_melanoma_rnaseq <- griss_melanoma_rnaseq[total_reads >= 100, ]

# this is a edgeR DGEList object
class(griss_melanoma_rnaseq)
head(griss_melanoma_rnaseq$samples)

Again, the dataset can simply be added using add_dataset. Here, we added an additional parameter to the add_dataset call. Such additional parameters are treated as additional dataset-level parameters.

# add the dataset
my_request <- add_dataset(request = my_request, 
                          expression_values = griss_melanoma_rnaseq, 
                          name = "RNA-seq", 
                          type = "rnaseq_counts",
                          comparison_factor = "treatment", 
                          comparison_group_1 = "MOCK", 
                          comparison_group_2 = "MCM",
                          additional_factors = c("cell_type", "patient"),
                          # This adds the dataset-level parameter 'discrete_norm_function' to the request
                          discrete_norm_function = "TMM")
my_request

Sample annotations

Datasets can be passed as limma EList, edgeR DGEList, any implementation of the Bioconductor ExpressionSet, or simply a data.frame.

For the first three, sample annotations are simply read from the respective slot. When supplying the expression values as a data.frame, the sample_data parameter has to be set using a data.frame where each row represents one sample and each column one proptery. If the the sample_data option is set while providing the expression data as an EList, DGEList, or ExpressionSet, the data in sample_data will be used instead of the sample annotations in the expression data object.

Name

Each dataset has to have a name. This can be anything but has to be unique within one analysis request.

Type

The ReactomeAnalysisSystem supports different types of 'omics data. To get a list of supported types, use the get_reactome_data_types function:

get_reactome_data_types()

Defining the experimental design

Defining the experimental design for a ReactomeAnalysisRequest is very simple. Basically, it only takes three parameters:

• comparison_factor: Name of the property within the sample data to use
• comparison_group_1: The first group to compare
• comparison_group_2: The second group to compare

The value set in comparison_factor must match a column name in the sample data (either the slot in an Elist, DGEList, or ExpressionSet object or in the sample_data parameter).

Additionally, it is possible to define blocking factors. These are supported by all methods that rely on linear models in the backend. Some methods though might simply ignore this parameter. For more information on whether a method supports blocking factors, please use get_reactome_methods.

Blocking factors can simply be set additional_factors to a vector of names. These should again reference properties (or columns) in the sample data.

Submitting the request

Once the ReactomeAnalysisRequest is created, the complete analysis can be run using perform_reactome_analysis:

result <- perform_reactome_analysis(request = my_request, compress = F)

Investigating the result

The result object is a ReactomeAnalysisResult S4 class with several helper functions to access the data.

To retrieve the names of all available results (generally one per dataset), use the names function:

names(result)

For every dataset, different result types may be available. These can be shown using the result_types function:

result_types(result)

The Camera analysis method returns two types of results, pathway-level data and gene- / protein-level fold changes.

A specific result can be retrieved using the get_result method:

# retrieve the fold-change data for the proteomics dataset
proteomics_fc <- get_result(result, type = "fold_changes", name = "Proteomics")
head(proteomics_fc)

Additionally, it is possible to directly merge the pathway level data for all result sets using the pathways function:

combined_pathways <- pathways(result)

head(combined_pathways)

Visualising results

The ReactomeGSA package includes several basic plotting functions to visualise the pathway results. For comparative gene set analysis like the one presented here, two functions are available: plot_correlations and plot_volcano.

plot_correlations can be used to quickly assess how similar two datasets are on the pathway level:

plot_correlations(result)

Individual datasets can further be visualised using volcano plots of the pathway data:

plot_volcano(result, 2)

Finally, it is possible to view the result as a heatmap:

plot_heatmap(result) +
  # reduce text size to create a better HTML rendering
  ggplot2::theme(text = ggplot2::element_text(size = 6))

By default, only 30 pathways are shown in the heatmap. It is also possible to easily manually select pathways of interest to plot:

# get the data ready to plot with ggplot2
plot_data <- plot_heatmap(result, return_data = TRUE)

# select the pathways of interest - here all pathways
# with "Interleukin" in their name
interleukin_pathways <- grepl("Interleukin", plot_data$Name)

interesting_data <- plot_data[interleukin_pathways, ]

# create the heatmap
ggplot2::ggplot(interesting_data, ggplot2::aes(x = dataset, y = Name, fill = direction)) +
    ggplot2::geom_tile() +
    ggplot2::scale_fill_brewer(palette = "RdYlBu") +
    ggplot2::labs(x = "Dataset", fill = "Direction") +
    ggplot2::theme(text = ggplot2::element_text(size = 6))

Opening web-based visualization

Additionally, it is possible to open the analysis in Reactome's web interface using the open_reactome command:

# Note: This command is not execute in the vignette, since it tries
# to open the default web browser

# open_reactome(result)

Session Info

sessionInfo()

reactome/ReactomeGSA documentation built on Nov. 13, 2023, 11:13 p.m.