# Loading and re-analysing public data through ReactomeGSA

## 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)
    #> [1] "Aegilops tauschii"                "Anas platyrhynchos"
    #> [3] "Anolis carolinensis"              "Anopheles gambiae"
    #> [5] "Arabidopsis lyrata"               "Arabidopsis lyrata subsp. lyrata"

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")]
    #>          id
    #> 1  GSE83592
    #> 2 GSE110054
    #> 3 GSE100066
    #> 4 GSE107370
    #>                                                                           title
    #> 1                            JQ1 +/- Vemurafenib in BRAF mutant melanoma (A375)
    #> 2                Transcriptional responses of melanoma cells to BRAF inhibition
    #> 3         Transcriptome sequencing analysis of BRAF-mutant melanoma metastases.
    #> 4 Concomitant BCORL1 and BRAF mutations in vemurafenib-resistant melanoma cells

## 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)
    #> 'data.frame':    1 obs. of  7 variables:
    #>  $ title              : chr "RNA-seq of the human melanoma cell-line A375P treated with the BRAF inhibitor PLX4720 and a DMSO control"
    #>  $ id                 : chr "E-MTAB-7453"
    #>  $ resource_name      : chr "EBI Expression Atlas"
    #>  $ description        : chr ""
    #>  $ resource_loading_id: chr "ebi_gxa"
    #>  $ loading_parameters :List of 1
    #>   ..$ :'data.frame': 1 obs. of  2 variables:
    #>   .. ..$ name : chr "dataset_id"
    #>   .. ..$ value: chr "E-MTAB-7453"
    #>  $ web_link           : chr "https://www.ebi.ac.uk/gxa/experiments/E-MTAB-7453/Results"

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)
    #> Converting ExpressionAtlas data
    #> Creating summary data

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

    # use the biobase functions to access the metadata
    library(Biobase)
    #> Loading required package: BiocGenerics
    #> Loading required package: generics
    #>
    #> Attaching package: 'generics'
    #> The following object is masked from 'package:dplyr':
    #>
    #>     explain
    #> The following objects are masked from 'package:base':
    #>
    #>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    #>     setequal, union
    #>
    #> Attaching package: 'BiocGenerics'
    #> The following object is masked from 'package:dplyr':
    #>
    #>     combine
    #> The following object is masked from 'package:limma':
    #>
    #>     plotMA
    #> The following objects are masked from 'package:stats':
    #>
    #>     IQR, mad, sd, var, xtabs
    #> The following objects are masked from 'package:base':
    #>
    #>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    #>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    #>     get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
    #>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    #>     rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
    #>     unsplit, which.max, which.min
    #> Welcome to Bioconductor
    #>
    #>     Vignettes contain introductory material; view with
    #>     'browseVignettes()'. To cite Bioconductor, see
    #>     'citation("Biobase")', and for packages 'citation("pkgname")'.

    # basic metadata
    pData(mel_cells_braf)
    #>             Sample.Id AtlasAssayGroup     organism cell_line organism_part
    #> ERR2950741 ERR2950741              g2 Homo sapiens    A375-P          skin
    #> ERR2950742 ERR2950742              g2 Homo sapiens    A375-P          skin
    #> ERR2950743 ERR2950743              g2 Homo sapiens    A375-P          skin
    #> ERR2950744 ERR2950744              g1 Homo sapiens    A375-P          skin
    #> ERR2950745 ERR2950745              g1 Homo sapiens    A375-P          skin
    #> ERR2950746 ERR2950746              g1 Homo sapiens    A375-P          skin
    #>                  cell_type  disease compound         dose
    #> ERR2950741 epithelial cell melanoma     none
    #> ERR2950742 epithelial cell melanoma     none
    #> ERR2950743 epithelial cell melanoma     none
    #> ERR2950744 epithelial cell melanoma  PLX4720 1 micromolar
    #> ERR2950745 epithelial cell melanoma  PLX4720 1 micromolar
    #> ERR2950746 epithelial cell melanoma  PLX4720 1 micromolar

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)
    #> Experiment data
    #>   Experimenter name: E-MTAB-7453
    #>   Laboratory:
    #>   Contact information:
    #>   Title: RNA-seq of the human melanoma cell-line A375P treated with the BRAF inhibitor PLX4720 and a DMSO control
    #>   URL: https://www.ebi.ac.uk/gxa/experiments/E-MTAB-7453/Results
    #>   PMIDs:
    #>   No abstract available.
    #>   notes:
    #>    notes:
    #>       Public dataset loaded from EBI Expression Atlas through ReactomeGSA.

    # for some datasets, longer descriptions are available. These
    # can be accessed using the abstract function
    abstract(mel_cells_braf)
    #> [1] ""

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)
    #>
    #>    none PLX4720
    #>       3       3

## 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")
    #> Converting expression data to string... (This may take a moment)
    #> Conversion complete

    my_request
    #> ReactomeAnalysisRequestObject
    #>   Method = Camera
    #>   Parameters:
    #>   - create_reactome_visualization: FALSE
    #>   Datasets:
    #>   - E-MTAB-7453 (rnaseq_counts)
    #>     No parameters set.
    #> ReactomeAnalysisRequest

The analysis can now started using the standard workflow:

    # perform the analysis using ReactomeGSA
    res <- perform_reactome_analysis(my_request)
    #> Submitting request to Reactome API...
    #> Compressing request data...
    #> Reactome Analysis submitted succesfully
    #> Converting dataset E-MTAB-7453...
    #> Mapping identifiers...
    #> Performing gene set analysis using Camera
    #> Analysing dataset 'E-MTAB-7453' using Camera
    #> Retrieving result...

    # basic overview of the result
    print(res)
    #> ReactomeAnalysisResult object
    #>   Reactome Release: 96
    #>   Results:
    #>   - E-MTAB-7453:
    #>     2757 pathways
    #>     9504 fold changes for genes
    #>   No Reactome visualizations available
    #> [1] "ReactomeAnalysisResult"
    #> attr(,"package")
    #> [1] "ReactomeGSA"

    # key pathways
    res_pathways <- pathways(res)

    head(res_pathways)
    #>                                                                                              Name
    #> R-HSA-69239                                                                      Synthesis of DNA
    #> R-HSA-69620                                                                Cell Cycle Checkpoints
    #> R-HSA-69242                                                                               S Phase
    #> R-HSA-69206                                                                       G1/S Transition
    #> R-HSA-141424                                        Amplification of signal from the kinetochores
    #> R-HSA-141444 Amplification  of signal from unattached  kinetochores via a MAD2  inhibitory signal
    #>              Direction.E-MTAB-7453 FDR.E-MTAB-7453 PValue.E-MTAB-7453
    #> R-HSA-69239                     Up    3.013905e-14       1.093183e-17
    #> R-HSA-69620                     Up    4.264276e-14       3.093417e-17
    #> R-HSA-69242                     Up    1.780711e-13       1.937662e-16
    #> R-HSA-69206                     Up    2.820877e-13       4.092676e-16
    #> R-HSA-141424                    Up    6.273544e-13       1.500534e-15
    #> R-HSA-141444                    Up    6.273544e-13       1.500534e-15
    #>              NGenes.E-MTAB-7453 av_foldchange.E-MTAB-7453 sig.E-MTAB-7453
    #> R-HSA-69239                 108                  1.555786            TRUE
    #> R-HSA-69620                 268                  1.453771            TRUE
    #> R-HSA-69242                 150                  1.408161            TRUE
    #> R-HSA-69206                 121                  1.572788            TRUE
    #> R-HSA-141424                 93                  1.988949            TRUE
    #> R-HSA-141444                 93                  1.988949            TRUE

## Session Info

    sessionInfo()
    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [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
    #>
    #> time zone: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #>  [1] Biobase_2.72.0          BiocGenerics_0.58.1     generics_0.1.4
    #>  [4] ggplot2_4.0.3           dplyr_1.2.1             tidyr_1.3.2
    #>  [7] ReactomeGSA.data_1.26.0 Seurat_5.5.0            SeuratObject_5.4.0
    #> [10] sp_2.2-1                ReactomeGSA_1.26.0      edgeR_4.10.1
    #> [13] limma_3.68.3            rmarkdown_2.31
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3     sys_3.4.3              jsonlite_2.0.0
    #>   [4] magrittr_2.0.5         spatstat.utils_3.2-3   farver_2.1.2
    #>   [7] vctrs_0.7.3            ROCR_1.0-12            spatstat.explore_3.8-1
    #>  [10] progress_1.2.3         htmltools_0.5.9        curl_7.1.0
    #>  [13] sass_0.4.10            sctransform_0.4.3      parallelly_1.47.0
    #>  [16] KernSmooth_2.23-26     bslib_0.11.0           htmlwidgets_1.6.4
    #>  [19] ica_1.0-3              plyr_1.8.9             plotly_4.12.0
    #>  [22] zoo_1.8-15             cachem_1.1.0           buildtools_1.0.0
    #>  [25] igraph_2.3.1           mime_0.13              lifecycle_1.0.5
    #>  [28] pkgconfig_2.0.3        Matrix_1.7-5           R6_2.6.1
    #>  [31] fastmap_1.2.0          fitdistrplus_1.2-6     future_1.70.0
    #>  [34] shiny_1.13.0           digest_0.6.39          patchwork_1.3.2
    #>  [37] tensor_1.5.1           RSpectra_0.16-2        irlba_2.3.7
    #>  [40] labeling_0.4.3         progressr_0.19.0       spatstat.sparse_3.2-0
    #>  [43] httr_1.4.8             polyclip_1.10-7        abind_1.4-8
    #>  [46] compiler_4.6.0         withr_3.0.2            S7_0.2.2
    #>  [49] fastDummies_1.7.6      gplots_3.3.0           MASS_7.3-65
    #>  [52] gtools_3.9.5           caTools_1.18.3         tools_4.6.0
    #>  [55] lmtest_0.9-40          otel_0.2.0             httpuv_1.6.17
    #>  [58] future.apply_1.20.2    goftest_1.2-3          glue_1.8.1
    #>  [61] nlme_3.1-169           promises_1.5.0         grid_4.6.0
    #>  [64] Rtsne_0.17             cluster_2.1.8.2        reshape2_1.4.5
    #>  [67] gtable_0.3.6           spatstat.data_3.1-9    hms_1.1.4
    #>  [70] data.table_1.18.4      spatstat.geom_3.8-1    RcppAnnoy_0.0.23
    #>  [73] ggrepel_0.9.8          RANN_2.6.2             pillar_1.11.1
    #>  [76] stringr_1.6.0          spam_2.11-3            RcppHNSW_0.7.0
    #>  [79] later_1.4.8            splines_4.6.0          lattice_0.22-9
    #>  [82] survival_3.8-6         deldir_2.0-4           tidyselect_1.2.1
    #>  [85] locfit_1.5-9.12        maketools_1.3.2        miniUI_0.1.2
    #>  [88] pbapply_1.7-4          knitr_1.51             gridExtra_2.3
    #>  [91] scattermore_1.2        xfun_0.57              statmod_1.5.2
    #>  [94] matrixStats_1.5.0      stringi_1.8.7          lazyeval_0.2.3
    #>  [97] yaml_2.3.12            evaluate_1.0.5         codetools_0.2-20
    #> [100] tibble_3.3.1           cli_3.6.6              uwot_0.2.4
    #> [103] xtable_1.8-8           reticulate_1.46.0      jquerylib_0.1.4
    #> [106] Rcpp_1.1.1-1.1         globals_0.19.1         spatstat.random_3.5-0
    #> [109] png_0.1-9              spatstat.univar_3.2-0  parallel_4.6.0
    #> [112] prettyunits_1.2.0      dotCall64_1.2          bitops_1.0-9
    #> [115] listenv_0.10.1         viridisLite_0.4.3      scales_1.4.0
    #> [118] ggridges_0.5.7         crayon_1.5.3           purrr_1.2.2
    #> [121] rlang_1.2.0            cowplot_1.2.0