knitr::opts_chunk$set(
  collapse = TRUE,
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Introduction

Since October 2023, ReactomeGSA was extended to simplify the reuse of public data. As key features, ReactomeGSA can now directly load data from EBI's ExpressionAtlas, and NCBI's GREIN. Both of these resources reprocess available public datasets using consistent pipelines.

Additionally, a search function was integrated into ReactomeGSA that can search for datasets simultaneously in all of these supported resources.

The ReactomeGSA R package now also has all required functions to directly access this web-based service. It is thereby possible to search for public datasets directly and download them as ExpressionSet objects.

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/.

Searching for Public Datasets

The find_public_datasets function uses ReactomeGSA's web service to search for public datasets in all supported resources.

By default, the datasets are limited to human studies. This can be changed by setting the species parameter. The complete list of available species is returned by the get_public_species function.

library(ReactomeGSA)

# get all available species found in the datasets
all_species <- get_public_species()

head(all_species)

The search_term parameter takes a single string as an argument. Words separated by a space are logically combined using an AND.

# search for datasets on BRAF and melanoma
datasets <- find_public_datasets("melanoma BRAF")

# the function returns the found datasets as a data.frame
datasets[1:4, c("id", "title")]

Load a public dataset

Datasets found through the find_public_datasets function can subsequently loaded using the load_public_dataset function.

# find the correct entry in the search result
# this must be the complete row of the data.frame returned
# by the find_public_datasets function
dataset_search_entry <- datasets[datasets$id == "E-MTAB-7453", ]

str(dataset_search_entry)

The selected dataset can now be loaded through the load_public_dataset function.

# this function only takes one argument, which must be
# a single row from the data.frame returned by the
# find_public_datasets function
mel_cells_braf <- load_public_dataset(dataset_search_entry, verbose = TRUE)

The returned object is an ExpressionSet object that already contains all available metada.

# use the biobase functions to access the metadata
library(Biobase)

# basic metadata
pData(mel_cells_braf)

Detailed descriptions of the loaded study are further stored in the metadata slot.

# access the stored metadata using the experimentData function
experimentData(mel_cells_braf)

# for some datasets, longer descriptions are available. These
# can be accessed using the abstract function
abstract(mel_cells_braf)

Additionally, you can use the table function to quickly get the number of available samples for a specific metadata field.

table(mel_cells_braf$compound)

Perform the pathway analysis using ReactomeGSA

This object is now directly compatible with ReactomeGSA's pathway analysis functions. A detailed explanation of how to perform this analysis, please have a look at the respective vignette.

# create the analysis request
my_request <-ReactomeAnalysisRequest(method = "Camera")

# do not create a visualization for this example
my_request <- set_parameters(request = my_request, create_reactome_visualization = FALSE)

# add the dataset using the loaded object
my_request <- add_dataset(request = my_request, 
                          expression_values = mel_cells_braf, 
                          name = "E-MTAB-7453", 
                          type = "rnaseq_counts",
                          comparison_factor = "compound", 
                          comparison_group_1 = "PLX4720", 
                          comparison_group_2 = "none")

my_request

The analysis can now started using the standard workflow:

# perform the analysis using ReactomeGSA
res <- perform_reactome_analysis(my_request)

# basic overview of the result
print(res)

# key pathways
res_pathways <- pathways(res)

head(res_pathways)

Session Info

sessionInfo()


reactome/ReactomeGSA documentation built on Nov. 9, 2024, 10:56 a.m.