multiGSEA 1.14.0
The multiGSEA package was designed to run a robust GSEA-based pathway
enrichment for multiple omics layers. The enrichment is calculated for each
omics layer separately and aggregated p-values are calculated afterwards to
derive a composite multi-omics pathway enrichment.
Pathway definitions can be downloaded from up to eight different pathway
databases by means of the graphite Bioconductor package (Sales, Calura, and Romualdi 2018).
Feature mapping for transcripts and proteins is supported towards Entrez Gene
IDs, Uniprot, Gene Symbol, RefSeq, and Ensembl IDs. The mapping is accomplished
through the AnnotationDbi package (Pagès et al. 2019) and currently
supported for 11 different model organisms including human, mouse, and rat. ID
conversion of metabolite features to Comptox Dashboard IDs (DTXCID, DTXSID),
CAS-numbers, Pubchem IDs (CID), HMDB, KEGG, ChEBI, Drugbank IDs, or common
metabolite names is accomplished through the AnnotationHub package
metabliteIDmapping. This package provides a comprehensive ID mapping for more
than 1.1 million entries.
This vignette covers a simple example workflow illustrating how the multiGSEA
package works. The omics data sets that will be used throughout the example
were originally provided by Quiros et al. (Quirós et al. 2017). In their publication
the authors analyzed the mitochondrial response to four different toxicants,
including Actinonin, Diclofenac, FCCB, and Mito-Block (MB), within the
transcriptome, proteome, and metabolome layer. The transcriptome data can be
downloaded from NCBI
GEO, the proteome
data from the ProteomeXchange
Consortium,
and the non-targeted metabolome raw data can be found in the online supplement.
There are two different ways to install the multiGSEA package.
First, the multiGSEA package is part of
Bioconductor.
Hence, the best way to install the package is via BiocManager. Start R
(>=4.0.0) and run the following commands:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
# The following initializes usage of Bioc devel
BiocManager::install(version = "devel")
BiocManager::install("multiGSEA")Alternatively, the multiGSEA package is hosted on our github page
https://github.com/yigbt/multiGSEA and can be installed via the
devtools package:
install.packages("devtools")
devtools::install_github("https://github.com/yigbt/multiGSEA")Once installed, just load the multiGSEA package with:
library(multiGSEA)A common workflow which is exemplified in this vignette is typically separated in the following steps:
multiGSEA package, and omics data sets.At first, we need to load the necessary packages to run the multi-omics
enrichment analysis. In our example data we have to deal with omics data that
has been measured in human cell lines. We therefore need the org.Hs.eg.db
package (Carlson 2019a) for transcript and protein mapping. In case the
omics data was measured in mouse or rat, we would need the packages
org.Mm.eg.db (Carlson 2019b) and org.Rn.eg.db (Carlson 2019c),
respectively.
library("org.Hs.eg.db")In principle, multiGSEA is able to deal with 11 different model organisms.
A summary of supported organisms, their naming format within multiGSEA and
their respective AnnotationDbi package is shown in Table
1.
| Organisms | Abbreviations | Mapping |
|---|---|---|
| Human | hsapiens | org.Hs.eg.db |
| Mouse | mmusculus | org.Mm.eg.db |
| Rat | rnorvegicus | org.Rn.eg.db |
| Dog | cfamiliaris | org.Cf.eg.db |
| Cow | btaurus | org.Bt.eg.db |
| Pig | sscrofa | org.Ss.eg.db |
| Chicken | ggallus | org.Gg.eg.db |
| Zebrafish | drerio | org.Xl.eg.db |
| Frog | xlaevis | org.Dr.eg.db |
| Fruit Fly | dmelanogaster | org.Dm.eg.db |
| C.elegans | celegans | org.Ce.eg.db |
To run the analysis of this vignette, load the installed version of
multiGSEA.
library(multiGSEA)
library(magrittr)Load the omics data for each layer where an enrichment should be calculated. The example data is provided within the package and already preprocessed such that we have log2 transformed fold changes and their associated p-values.
# load transcriptomic features
data(transcriptome)
# load proteomic features
data(proteome)
# load metabolomic features
data(metabolome)This example involves preprocessed omics data from public
repositories, which means that the data might look different when you
download and pre-process it with your own workflow. Therefore, we put
our processed data as an example data in the R package. We here sketch
out the pipeline described in the multiGSEA paper. We will not focus
on the pre-processing steps and how to derive the necessary input
format for the multi-omics pathway enrichment in terms of
differentially expression analysis, since this is highly dependent on
your experiment and analysis workflow.
However, the required input format is quite simple and exactly the same for each input omics layer: A data frame with 3 mandatory columns, including feature IDs, the log2-transformed fold change (logFC), and the associated p-value.
The header of the data frame can be seen in Table 2:
| Symbol | logFC | pValue | adj.pValue |
|---|---|---|---|
| A2M | -0.1615651 | 0.0000060 | 0.0002525 |
| A2M-AS1 | 0.2352903 | 0.2622606 | 0.4594253 |
| A4GALT | -0.0384392 | 0.3093539 | 0.5077487 |
| AAAS | 0.0170947 | 0.5407324 | 0.7126172 |
| AACS | -0.0260510 | 0.4034970 | 0.5954772 |
| AADAT | 0.0819910 | 0.2355455 | 0.4285059 |
multiGSEA works with nested lists where each sublist represents an
omics layer. The function rankFeatures calculates the pre-ranked
features, that are needed for the subsequent calculation of the
enrichment score. rankFeatures calculates the a local statistic ls
based on the direction of the fold change and the magnitude of its
significance:
\[\begin{equation} ls = sign( log_2(FC)) * log_{10}( p-value) \end{equation}\]
Please note, that any other rank metric will work as well and the choice on which one to chose is up to the user. However, as it was shown by Zyla et al. (Joanna Zyla and Polanska 2017), the choice of the applied metric might have a big impact on the outcome of your analysis.
# create data structure
omics_data <- initOmicsDataStructure(layer = c(
"transcriptome",
"proteome",
"metabolome"
))
## add transcriptome layer
omics_data$transcriptome <- rankFeatures(
transcriptome$logFC,
transcriptome$pValue
)
names(omics_data$transcriptome) <- transcriptome$Symbol
## add proteome layer
omics_data$proteome <- rankFeatures(proteome$logFC, proteome$pValue)
names(omics_data$proteome) <- proteome$Symbol
## add metabolome layer
## HMDB features have to be updated to the new HMDB format
omics_data$metabolome <- rankFeatures(metabolome$logFC, metabolome$pValue)
names(omics_data$metabolome) <- metabolome$HMDB
names(omics_data$metabolome) <- gsub(
"HMDB", "HMDB00",
names(omics_data$metabolome)
)The first elements of each omics layer are shown below:
omics_short <- lapply(names(omics_data), function(name) {
head(omics_data[[name]])
})
names(omics_short) <- names(omics_data)
omics_short## $transcriptome
## STC2 ASNS PCK2 FAM129A NUPR1 ASS1
## 13.43052 12.69346 12.10931 11.97085 11.81069 11.61673
##
## $proteome
## IFRD1 FAM129A FDFT1 ASNS CTH PCK2
## 10.818222 10.108260 -9.603185 9.327082 8.914447 8.908938
##
## $metabolome
## HMDB0000042 HMDB0003344 HMDB0000820 HMDB0000863 HMDB0006853 HMDB0013785
## -1.404669 -1.404669 -3.886828 -3.886828 -3.130641 -3.130641Now we have to select the databases we want to query and the omics layer we are interested in before pathway definitions are downloaded and features are mapped to the desired format.
databases <- c("kegg", "reactome")
layers <- names(omics_data)
pathways <- getMultiOmicsFeatures(
dbs = databases, layer = layers,
returnTranscriptome = "SYMBOL",
returnProteome = "SYMBOL",
returnMetabolome = "HMDB",
useLocal = FALSE
)The first two pathway definitions of each omics layer are shown below:
pathways_short <- lapply(names(pathways), function(name) {
head(pathways[[name]], 2)
})
names(pathways_short) <- names(pathways)
pathways_short## $transcriptome
## $transcriptome$`(KEGG) Glycolysis / Gluconeogenesis`
## [1] "AKR1A1" "ADH1A" "ADH1B" "ADH1C" "ADH4" "ADH5" "ADH6"
## [8] "GALM" "ADH7" "LDHAL6A" "DLAT" "DLD" "ENO1" "ENO2"
## [15] "ENO3" "ALDH2" "ALDH3A1" "ALDH1B1" "FBP1" "ALDH3B1" "ALDH3B2"
## [22] "ALDH9A1" "ALDH3A2" "ALDOA" "ALDOB" "ALDOC" "G6PC1" "GAPDH"
## [29] "GAPDHS" "GCK" "GPI" "HK1" "HK2" "HK3" "ENO4"
## [36] "LDHA" "LDHB" "LDHC" "PGAM4" "ALDH7A1" "PCK1" "PCK2"
## [43] "PDHA1" "PDHA2" "PDHB" "PFKL" "PFKM" "PFKP" "PGAM1"
## [50] "PGAM2" "PGK1" "PGK2" "PGM1" "PKLR" "PKM" "PGM2"
## [57] "ACSS2" "G6PC2" "BPGM" "TPI1" "HKDC1" "ADPGK" "ACSS1"
## [64] "FBP2" "LDHAL6B" "G6PC3" "MINPP1"
##
## $transcriptome$`(KEGG) Citrate cycle (TCA cycle)`
## [1] "CS" "DLAT" "DLD" "DLST" "FH" "IDH1" "IDH2" "IDH3A"
## [9] "IDH3B" "IDH3G" "MDH1" "MDH2" "ACLY" "ACO1" "OGDH" "ACO2"
## [17] "PC" "PDHA1" "PDHA2" "PDHB" "OGDHL" "SDHA" "SDHB" "SDHC"
## [25] "SDHD" "SUCLG2" "SUCLG1" "SUCLA2" "PCK1" "PCK2"
##
##
## $proteome
## $proteome$`(KEGG) Glycolysis / Gluconeogenesis`
## [1] "AKR1A1" "ADH1A" "ADH1B" "ADH1C" "ADH4" "ADH5" "ADH6"
## [8] "GALM" "ADH7" "LDHAL6A" "DLAT" "DLD" "ENO1" "ENO2"
## [15] "ENO3" "ALDH2" "ALDH3A1" "ALDH1B1" "FBP1" "ALDH3B1" "ALDH3B2"
## [22] "ALDH9A1" "ALDH3A2" "ALDOA" "ALDOB" "ALDOC" "G6PC1" "GAPDH"
## [29] "GAPDHS" "GCK" "GPI" "HK1" "HK2" "HK3" "ENO4"
## [36] "LDHA" "LDHB" "LDHC" "PGAM4" "ALDH7A1" "PCK1" "PCK2"
## [43] "PDHA1" "PDHA2" "PDHB" "PFKL" "PFKM" "PFKP" "PGAM1"
## [50] "PGAM2" "PGK1" "PGK2" "PGM1" "PKLR" "PKM" "PGM2"
## [57] "ACSS2" "G6PC2" "BPGM" "TPI1" "HKDC1" "ADPGK" "ACSS1"
## [64] "FBP2" "LDHAL6B" "G6PC3" "MINPP1"
##
## $proteome$`(KEGG) Citrate cycle (TCA cycle)`
## [1] "CS" "DLAT" "DLD" "DLST" "FH" "IDH1" "IDH2" "IDH3A"
## [9] "IDH3B" "IDH3G" "MDH1" "MDH2" "ACLY" "ACO1" "OGDH" "ACO2"
## [17] "PC" "PDHA1" "PDHA2" "PDHB" "OGDHL" "SDHA" "SDHB" "SDHC"
## [25] "SDHD" "SUCLG2" "SUCLG1" "SUCLA2" "PCK1" "PCK2"
##
##
## $metabolome
## $metabolome$`(KEGG) Glycolysis / Gluconeogenesis`
## [1] "HMDB0001586" "HMDB0001206" "HMDB0001294" "HMDB0001270" "HMDB0000122"
## [6] "HMDB0003498" "HMDB0003391" "HMDB0060180" "HMDB0003904" "HMDB0000108"
## [11] "HMDB0000223" "HMDB0000243" "HMDB0000042" "HMDB0000190" "HMDB0000990"
## [16] "HMDB0001473" "HMDB0003345" "HMDB0000263" "HMDB0001112" "HMDB0000124"
##
## $metabolome$`(KEGG) Citrate cycle (TCA cycle)`
## [1] "HMDB0001206" "HMDB0003974" "HMDB0001022" "HMDB0006744" "HMDB0003904"
## [6] "HMDB0000094" "HMDB0000134" "HMDB0000223" "HMDB0000243" "HMDB0000254"
## [11] "HMDB0000208" "HMDB0000263" "HMDB0000156" "HMDB0000072" "HMDB0000193"At this stage, we have the ranked features for each omics layer and the
extracted and mapped features from external pathway databases. In the following
step we can use the multiGSEA function to calculate the enrichment for all
omics layer separately.
# use the multiGSEA function to calculate the enrichment scores
# for all omics layer at once.
enrichment_scores <- multiGSEA(pathways, omics_data)The enrichment score data structure is a list containing sublists named
transcriptome, proteome, and metabolome. Each sublist represents the
complete pathway enrichment for this omics layer.
Making use of the Stouffers Z-method to combine multiple p-values that have been
derived from independent tests that are based on the same null hypothesis. The
function extractPvalues creates a simple data frame where each row represents
a pathway and columns represent omics related p-values and adjusted p-values.
This data structure can then be used to calculate the aggregated p-value. The
subsequent calculation of adjusted p-values can be achieved by the function
p.adjust.
multiGSEA provided three different methods to aggregate p-values. These
methods differ in their way how they weight either small or large p-values. By
default, combinePvalues will apply the Z-method or Stouffer’s method
(Stouffer et al. 1949) which has no bias towards small or large p-values. The widely
used Fisher’s combined probability test (Fisher 1932) can also be applied but
is known for its bias towards small p-values. Edgington’s method goes the
opposite direction by favoring large p-values (Edgington 1972). Those methods
can be applied by setting the parameter method to “fisher” or “edgington”.
df <- extractPvalues(
enrichmentScores = enrichment_scores,
pathwayNames = names(pathways[[1]])
)
df$combined_pval <- combinePvalues(df)
df$combined_padj <- p.adjust(df$combined_pval, method = "BH")
df <- cbind(data.frame(pathway = names(pathways[[1]])), df)Please note: In former versions of multiGSEA, adjusted p-values of
individual omics-layers have been combined. The correction for multiple testing
prior to the p-value combination will, however, violate the assumptions about
p-values of certain combination methods (e.g., Fisher’s combined probability
test).
Therefore, multiGSEA uses p-values for combination from RELEASE 3.18.
To keep previous results reproducible, we introduced the col_pattern parameter
which can be set to padj to use adjusted p-values.
Finally, print the pathways sorted based on their combined adjusted p-values. For displaying reasons, only the adjusted p-values are shown in Table 3.
| pathway | transcriptome_padj | proteome_padj | metabolome_padj | combined_pval |
|---|---|---|---|---|
| (KEGG) Metabolic pathways | 0.2479584 | 0.0000000 | 0.0000003 | 0 |
| (REACTOME) Amino acid and derivative metabolism | 0.0001692 | 0.0000000 | 0.2876328 | 0 |
| (REACTOME) Cell Cycle | 0.0000000 | 0.0000007 | 0.7396231 | 0 |
| (REACTOME) Cell Cycle, Mitotic | 0.0000000 | 0.0000331 | 0.7396231 | 0 |
| (REACTOME) Metabolism | 0.1825524 | 0.0000000 | 0.0094995 | 0 |
| (KEGG) Carbon metabolism | 0.1047382 | 0.0000035 | 0.0003733 | 0 |
| (KEGG) Biosynthesis of amino acids | 0.0010805 | 0.0000000 | 0.2876328 | 0 |
| (REACTOME) Selenocysteine synthesis | 0.0053671 | 0.0000004 | 0.1164502 | 0 |
| (REACTOME) Selenoamino acid metabolism | 0.0017822 | 0.0000000 | 0.4690678 | 0 |
| (REACTOME) Cholesterol biosynthesis | 0.0000000 | 0.0005555 | 0.5999308 | 0 |
| (KEGG) Steroid biosynthesis | 0.0000002 | 0.0000793 | 0.4983957 | 0 |
| (REACTOME) RHO GTPase Effectors | 0.0000000 | 0.0000841 | 0.8049823 | 0 |
| (REACTOME) Signaling Pathways | 0.0000001 | 0.0041149 | 0.3522947 | 0 |
| (REACTOME) M Phase | 0.0000000 | 0.0244762 | 0.7396231 | 0 |
| (REACTOME) Regulation of cholesterol biosynthesis by SREBP (SREBF) | 0.0000002 | 0.0238168 | 0.2876328 | 0 |
In principle, multiGSEA can also be run as single/multi omics analysis on
custom gene sets.
The pathways object storing the pathway features across multiple omics layers
is a nested list, and hence can be designed manually to fit ones purposes.
In the following example, HALLMARK gene sets are retrieved from MSigDB and
used to create a transcriptomics input list:
library(msigdbr)
library(dplyr)
hallmark <- msigdbr(species = "Rattus norvegicus", category = "H") %>%
dplyr::select(gs_name, ensembl_gene) %>%
dplyr::filter(!is.na(ensembl_gene)) %>%
unstack(ensembl_gene ~ gs_name)
pathways <- list("transcriptome" = hallmark)Please note, feature sets across multiple omics layers have to be in the same order and their names have to be identical, see the example presented above.
Here is the output of sessionInfo() on the system on which this document was
compiled:
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
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## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] magrittr_2.0.3 multiGSEA_1.14.0 org.Hs.eg.db_3.19.1
## [4] AnnotationDbi_1.66.0 IRanges_2.38.0 S4Vectors_0.42.0
## [7] Biobase_2.64.0 BiocGenerics_0.50.0 BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] Rdpack_2.6 DBI_1.2.2
## [3] mnormt_2.1.1 sandwich_3.1-0
## [5] rlang_1.1.3 multcomp_1.4-25
## [7] qqconf_1.3.2 compiler_4.4.0
## [9] RSQLite_2.3.6 png_0.1-8
## [11] vctrs_0.6.5 pkgconfig_2.0.3
## [13] crayon_1.5.2 fastmap_1.1.1
## [15] dbplyr_2.5.0 XVector_0.44.0
## [17] utf8_1.2.4 rmarkdown_2.26
## [19] graph_1.82.0 UCSC.utils_1.0.0
## [21] purrr_1.0.2 bit_4.0.5
## [23] xfun_0.43 zlibbioc_1.50.0
## [25] cachem_1.0.8 graphite_1.50.0
## [27] GenomeInfoDb_1.40.0 jsonlite_1.8.8
## [29] blob_1.2.4 BiocParallel_1.38.0
## [31] parallel_4.4.0 R6_2.5.1
## [33] bslib_0.7.0 mutoss_0.1-13
## [35] jquerylib_0.1.4 numDeriv_2016.8-1.1
## [37] Rcpp_1.0.12 bookdown_0.39
## [39] knitr_1.46 zoo_1.8-12
## [41] Matrix_1.7-0 splines_4.4.0
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## [45] codetools_0.2-20 curl_5.2.1
## [47] lattice_0.22-6 tibble_3.2.1
## [49] withr_3.0.0 KEGGREST_1.44.0
## [51] evaluate_0.23 metaboliteIDmapping_1.0.0
## [53] survival_3.6-4 BiocFileCache_2.12.0
## [55] Biostrings_2.72.0 pillar_1.9.0
## [57] BiocManager_1.30.22 filelock_1.0.3
## [59] sn_2.1.1 generics_0.1.3
## [61] mathjaxr_1.6-0 BiocVersion_3.19.1
## [63] ggplot2_3.5.1 munsell_0.5.1
## [65] scales_1.3.0 TFisher_0.2.0
## [67] glue_1.7.0 tools_4.4.0
## [69] metap_1.10 AnnotationHub_3.12.0
## [71] data.table_1.15.4 fgsea_1.30.0
## [73] mvtnorm_1.2-4 fastmatch_1.1-4
## [75] cowplot_1.1.3 grid_4.4.0
## [77] plotrix_3.8-4 rbibutils_2.2.16
## [79] colorspace_2.1-0 GenomeInfoDbData_1.2.12
## [81] cli_3.6.2 rappdirs_0.3.3
## [83] fansi_1.0.6 dplyr_1.1.4
## [85] gtable_0.3.5 sass_0.4.9
## [87] digest_0.6.35 TH.data_1.1-2
## [89] multtest_2.60.0 memoise_2.0.1
## [91] htmltools_0.5.8.1 lifecycle_1.0.4
## [93] httr_1.4.7 mime_0.12
## [95] MASS_7.3-60.2 bit64_4.0.5Carlson, Marc. 2019a. Org.Hs.eg.db: Genome Wide Annotation for Human.
———. 2019b. Org.Mm.eg.db: Genome Wide Annotation for Mouse.
———. 2019c. Org.Rn.eg.db: Genome Wide Annotation for Rat.
Edgington, Eugene S. 1972. “An Additive Method for Combining Probability Values from Independent Experiments.” The Journal of Psychology 80 (2): 351–63.
Fisher, S R A. 1932. Statistical Methods for Research Workers - Revised and Enlarged. Edinburgh, London.
Joanna Zyla, January Weiner, Michal Marczyk, and Joanna Polanska. 2017. “Ranking Metrics in Gene Set Enrichment Analysis: Do They Matter?” BMC Bioinformatics 18 (256). https://doi.org/https://doi.org/10.1186/s12859-017-1674-0.
Pagès, Hervé, Marc Carlson, Seth Falcon, and Nianhua Li. 2019. AnnotationDbi: Manipulation of Sqlite-Based Annotations in Bioconductor.
Quirós, P M, M A Prado, N Zamboni, D D’Amico, R W Williams, D Finley, S P Gygi, and J Auwerx. 2017. “Multi-Omics Analysis Identifies ATF4 as a Key Regulator of the Mitochondrial Stress Response in Mammals.” J Cell Biol 216 (7): 2027–45. https://doi.org/10.1083/jcb.201702058.
Sales, Gabriele, Enrica Calura, and Chiara Romualdi. 2018. “metaGraphite - a New Layer of Pathway Annotation to Get Metabolite Networks.” Bioinformatics. https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty719/5090451.
Stouffer, Samuel A, Edward A Suchman, Leland C DeVinney, Shirley A Star, and Robin M Williams Jr. 1949. “The American Soldier: Adjustment During Army Life.(studies in Social Psychology in World War Ii), Vol. 1.”