Reproducible GSEA Benchmarking
28 April 2020
Abstract
The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated.
Package
GSEABenchmarkeR 1.8.0
Contents
1 Purpose of the package
The purpose of the GSEABenchmarkeR package is to compare the performance of different methods for gene set enrichment analysis across many gene expression datasets.
Users interested in conducting gene set enrichment analysis for a specific dataset of choice are recommended to use the EnrichmentBrowser package instead.
In other words,
if you are interested in analysing a particular microarray or RNA-seq dataset, e.g. a case-control study where you want to find out which GO terms / KEGG pathways are enriched for differentially expressed genes, i.e. your primary goal is biological interpretation of a specific dataset under investigation, then use the EnrichmentBrowser package.
if you want to assess the performance (runtime, type I error rate, etc) of different enrichment methods across many datasets and in certain simulated setups - i.e. your primary goal is to understand methodological aspects and compare methods against each other, then use the GSEABenchmarkeR package.
2 Setup
Although gene set enrichment analysis (GSEA) has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, the evaluation is commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards a novel method being presented.
As the evaluation of GSEA methods is thus typically based on self-defined standards, Mitrea et al. (2013) identified the lack of gold standards for consistent assessment and comparison of enrichment methods as a major bottleneck. Furthermore, it is often cumbersome to reproduce existing assessments for additional methods, as this typically involves considerable effort of data processing and method collection.
Leveraging the representative and extendable collection of enrichment methods available in the EnrichmentBrowser package, the GSEABenchmarkeR package facilitates efficient execution of these methods on comprehensive real data compendia. The compendia are curated collections of microarray and RNA-seq datasets investigating human diseases (mostly specific cancer types), for which disease-relevant gene sets have been defined a priori.
Consistently applied to these datasets, enrichment methods can then be subjected to a systematic and reproducible assessment of (i) computational runtime, (ii) statistical significance, especially how the fraction of significant gene sets relates to the fraction of differentially expressed genes, and (iii) phenotype relevance, i.e. whether enrichment methods produce gene set rankings in which phenotype-relevant gene sets accumulate at the top.
In the following, we demonstrate how the package can be used to
- load specific pre-defined and user-defined data compendia,
- carry out differential expression analysis across datasets,
- apply enrichment methods to multiple datasets, and
- benchmark results with respect to the chosen criteria.
We start by loading the package.
library(GSEABenchmarkeR)3 Expression data sources
The GSEABenchmarkeR package implements a general interface for loading compendia of expression datasets. This includes
- the pre-defined GEO2KEGG microarray compendium that consists of 42 datasets investigating a total of 19 different human diseases as collected by Tarca et al. (2012 and 2013),
- the pre-defined TCGA RNA-seq compendium, consisting of datasets from The Cancer Genome Atlas investigating a total of 34 different cancer types, and
- user-defined data from file.
In the following, we describe both pre-defined compendia in more detail and also demonstrate how user-defined data can be incorporated.
3.1 Microarray compendium
Although RNA-seq (read count data) has become the de facto standard for transcriptomic profiling, it is important to know that many methods for differential expression and gene set enrichment analysis have been originally developed for microarray data (intensity measurements). However, differences in data distribution assumptions (microarray: quasi-normal, RNA-seq: negative binomial) have made adaptations in differential expression analysis and, to some extent also in gene set enrichment analysis, necessary.
Nevertheless, the comprehensive collection and curation of microarray data in online repositories such as GEO still represent a valuable resource. In particular, Tarca et al. (2012 and 2013) compiled 42 datasets from GEO, each investigating a human disease for which a specific KEGG pathway exists.
These pathways are accordingly defined as the target pathways for the various enrichment methods when applied to the respective datasets. For instance, methods are expected to rank the Alzheimer’s disease pathway close to the top when applied to GSE1297, a case-control study of Alzheimer’s disease.
Furthermore, Tarca et al. made these datasets available in the Bioconductor packages KEGGdzPathwaysGEO and KEGGandMetacoreDzPathwaysGEO.
The GSEABenchmarkeR package simplifies access to the compendium and allows to load it into the workspace via
geo2kegg <- loadEData("geo2kegg")## Loading GEO2KEGG data compendium ...names(geo2kegg)## [1] "GSE1297" "GSE14762" "GSE15471"
## [4] "GSE16515" "GSE18842" "GSE19188"
## [7] "GSE19728" "GSE20153" "GSE20291"
## [10] "GSE21354" "GSE3467" "GSE3585"
## [13] "GSE3678" "GSE4107" "GSE5281_EC"
## [16] "GSE5281_HIP" "GSE5281_VCX" "GSE6956AA"
## [19] "GSE6956C" "GSE781" "GSE8671"
## [22] "GSE8762" "GSE9348" "GSE9476"
## [25] "GSE1145" "GSE11906" "GSE14924_CD4"
## [28] "GSE14924_CD8" "GSE16759" "GSE19420"
## [31] "GSE20164" "GSE22780" "GSE23878"
## [34] "GSE24739_G0" "GSE24739_G1" "GSE30153"
## [37] "GSE32676" "GSE38666_epithelia" "GSE38666_stroma"
## [40] "GSE4183" "GSE42057" "GSE7305"A specific dataset of the compendium can be obtained via
geo2kegg[[1]]## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22283 features, 16 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM21215 GSM21217 ... GSM21229 (16 total)
## varLabels: Sample Group
## varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## pubMedIds: 14769913
## Annotation: hgu133awhich returns, in this example, an ExpressionSet (documented in the
Biobase package) that contains expression levels of
22,283 probe sets measured for 16 patients.
To prepare the datasets for subsequent analysis, the GSEABenchmarkeR
package provides the function maPreproc.
The function invokes EnrichmentBrowser::probe2gene on each dataset
to summarize expression levels for probes annotated to the same gene.
Here, we apply the function to the first 5 datasets of the compendium.
geo2kegg <- maPreproc(geo2kegg[1:5])## Summarizing probe level expression ...Now,
geo2kegg[[1]]## class: SummarizedExperiment
## dim: 12990 16
## metadata(5): experimentData annotation protocolData dataId dataType
## assays(1): exprs
## rownames(12990): 780 5982 ... 388796 100505915
## rowData names(0):
## colnames(16): GSM21215 GSM21217 ... GSM21213 GSM21229
## colData names(2): Sample GROUPreturns a SummarizedExperiment that contains the summarized
expression values for 12,994 genes. Furthermore, sample groups
are defined in the colData column GROUP, yielding here 7 cases (1) and
9 controls (0).
se <- geo2kegg[[1]]
table(se$GROUP)##
## 0 1
## 9 7Note: The maPreproc returns datasets consistently mapped to NCBI Entrez
Gene IDs, which is compatible with most downstream applications. However,
mapping to a different ID type such as ENSEMBL IDs or HGNC symbols can also be
done using the function EnrichmentBrowser::idMap.
3.2 RNA-seq compendium
The Cancer Genome Atlas (TCGA) project performed a molecular investigation of various cancer types on an unprecedented scale including various genomic high-throughput technologies. In particular, transcriptomic profiling of the investigated cancer types has comprehensively been carried out with RNA-seq in tumor and adjacent normal tissue.
Among the various resources that redistribute TCGA data, Rahman et al. (2015) consistently preprocessed the RNA-seq data for 24 cancer types and made the data available in the GEO dataset GSE62944.
The GSE62944 compendium can be loaded using the loadEData function,
which provides the datasets ready for subsequent differential expression and
gene set enrichment analysis.
Here, we load the compendium into the workspace using only two of the datasets.
tcga <- loadEData("tcga", nr.datasets=2)## Loading TCGA data compendium ...## Cancer types with tumor samples:## ACC, BLCA, BRCA, CESC, COAD, DLBC, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PRAD, READ, SKCM, STAD, THCA, UCEC, UCS## Cancer types with adj. normal samples:## BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA, THYM, UCEC## Cancer types with sufficient tumor and adj. normal samples:## BLCA, BRCA## Creating a SummarizedExperiment for each of them ...## BLCA tumor: 19 adj.normal: 19## BRCA tumor: 113 adj.normal: 113names(tcga)## [1] "BLCA" "BRCA"For example, the breast cancer dataset contains RNA-seq read counts for roughly 20,000 genes measured in 1,119 tumor (1) and 113 adjacent normal (0) samples.
brca <- tcga[[2]]
brca## class: SummarizedExperiment
## dim: 12575 226
## metadata(3): annotation dataId dataType
## assays(1): exprs
## rownames(12575): 1 2 ... 23140 26009
## rowData names(1): SYMBOL
## colnames(226): TCGA-A7-A13G-01A-11R-A13Q-07
## TCGA-E9-A1N5-01A-11R-A14D-07 ... TCGA-BH-A18M-11A-33R-A12D-07
## TCGA-BH-A1EW-11B-33R-A137-07
## colData names(4): sample type GROUP BLOCKtable(brca$GROUP)##
## 0 1
## 113 1133.3 User-defined data compendium
With easy and fast access to the GEO2KEGG and TCGA compendia, enrichment methods can be directly applied and assessed on well-studied, standardized expression datasets. Nevertheless, benchmarking with the GSEABenchmarkeR package is designed to be extendable to additional datasets as well.
Therefore, the loadEData function also accepts a directory where datasets,
preferably of class SummarizedExperiment, have been saved as
RDS
files.
data.dir <- system.file("extdata", package="GSEABenchmarkeR")
edat.dir <- file.path(data.dir, "myEData")
edat <- loadEData(edat.dir)
names(edat)## [1] "GSE42057x" "GSE7305x"edat[[1]]## class: SummarizedExperiment
## dim: 50 136
## metadata(5): experimentData annotation protocolData dataType dataId
## assays(1): exprs
## rownames(50): 3310 7318 ... 123036 117157
## rowData names(0):
## colnames(136): GSM1031553 GSM1031554 ... GSM1031683 GSM1031684
## colData names(2): Sample GROUP4 Differential expression
To perform differential expression (DE) analysis between sample groups for
selected datasets of a compendium, the GSEABenchmarkeR package
provides the function runDE.
The function invokes EnrichmentBrowser::deAna on each dataset, which contrasts
the sample groups as defined in the GROUP variable.
Here, we apply the function to 5 datasets of the GEO2KEGG compendium.
geo2kegg <- runDE(geo2kegg, de.method="limma", padj.method="flexible")
rowData(geo2kegg[[1]], use.names=TRUE)## DataFrame with 12990 rows and 4 columns
## FC limma.STAT PVAL ADJ.PVAL
## <numeric> <numeric> <numeric> <numeric>
## 780 0.4321928 2.637471 0.0173020 0.0173020
## 5982 0.0350299 0.537762 0.5977238 0.5977238
## 3310 0.1673362 0.265332 0.7939500 0.7939500
## 7849 0.1450528 1.871193 0.0786615 0.0786615
## 2978 -0.1186515 -0.887362 0.3872871 0.3872871
## ... ... ... ... ...
## 8484 0.36666338 4.1947324 0.000610556 0.000610556
## 23779 0.08626413 1.2204288 0.238989684 0.238989684
## 79583 0.06415321 0.3448148 0.734469953 0.734469953
## 388796 -0.00387318 -0.0304941 0.976028791 0.976028791
## 100505915 -0.00572267 -0.0856389 0.932755722 0.932755722Note: DE studies typically report a gene as differentially expressed if the corresponding DE p-value, corrected for multiple testing, satisfies the chosen significance level. Enrichment methods that work directly on the list of DE genes are then substantially influenced by the multiple testing correction.
An example is the frequently used over-representation analysis (ORA), which assesses the overlap between the DE genes and a gene set under study based on the hypergeometric distribution (see the vignette of the EnrichmentBrowser package, Appendix A, for an introduction).
ORA is inapplicable if there are few genes satisfying the significance threshold, or if almost all genes are DE.
Using padj.method="flexible" accounts for these cases by applying multiple
testing correction in dependence on the observed degree of differential expression:
- the correction method from Benjamini and Hochberg (BH) is applied, if it renders \(\ge 1\%\) and \(\le 25\%\) of all measured genes as DE,
- the p-values are left unadjusted, if the BH correction results in \(<1\%\) DE genes, and
- the more stringent Bonferroni correction is applied, if the BH correction results in \(>25\%\) DE genes.
Note that resulting \(p\)-values are not further used for assessing the statistical significance of DE genes within or between datasets. They are solely used to determine which genes are included in the analysis with ORA - where the flexible correction ensures that the fraction of included genes is roughly in the same order of magnitude across datasets. Alternative strategies could also be applied (such as taking a constant number of genes for each dataset or generally excluding ORA methods from the assessment).
5 Enrichment analysis
In the following, we demonstrate how to carry out enrichment analysis in a
benchmark setup.
Therefore, we use the collection of human KEGG gene sets as obtained
with getGenesets from the EnrichmentBrowser
package.
library(EnrichmentBrowser)
kegg.gs <- getGenesets(org="hsa", db="kegg")At the core of applying a specific enrichment method to a single dataset is the
runEA function, which delegates execution of the chosen method to either
EnrichmentBrowser::sbea (set-based enrichment analysis) or
EnrichmentBrowser::nbea (network-based enrichment analysis).
In addition, it returns CPU time used and allows saving results for subsequent
assessment.
Here, we carry out ORA on the first dataset of the GEO2KEGG compendium.
kegg.ora.res <- runEA(geo2kegg[[1]], method="ora", gs=kegg.gs, perm=0)
kegg.ora.res## $ora
## $ora$GSE1297
## $ora$GSE1297$runtime
## elapsed
## 0.91
##
## $ora$GSE1297$ranking
## DataFrame with 330 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa00190_Oxidative_phosphorylation 106
## 2 hsa05016_Huntington_disease 225
## 3 hsa05012_Parkinson_disease 115
## 4 hsa05010_Alzheimer_disease 289
## 5 hsa04714_Thermogenesis 188
## ... ... ...
## 326 hsa03040_Spliceosome 113
## 327 hsa01521_EGFR_tyrosine_kinase_inhibitor_resistance 77
## 328 hsa04914_Progesterone-mediated_oocyte_maturation 77
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00524_Neomycin,_kanamycin_and_gentamicin_biosynthesis 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 55 1.55e-12
## 2 93 1.69e-12
## 3 56 2.69e-11
## 4 104 1.43e-09
## 5 68 8.96e-07
## ... ... ...
## 326 12 0.999
## 327 6 1.000
## 328 5 1.000
## 329 0 1.000
## 330 0 1.000The function runEA can also be used to carry out several methods on multiple
datasets.
As an example, we carry out ORA and
CAMERA
on 5 datasets of the GEO2KEGG compendium saving the results in a temporary
directory.
res.dir <- tempdir()
res <- runEA(geo2kegg, methods=c("ora", "camera"),
gs=kegg.gs, perm=0, save2file=TRUE, out.dir=res.dir)
res$ora[1:2]## $GSE1297
## $GSE1297$runtime
## elapsed
## 0.41
##
## $GSE1297$ranking
## DataFrame with 330 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa00190_Oxidative_phosphorylation 106
## 2 hsa05016_Huntington_disease 225
## 3 hsa05012_Parkinson_disease 115
## 4 hsa05010_Alzheimer_disease 289
## 5 hsa04714_Thermogenesis 188
## ... ... ...
## 326 hsa03040_Spliceosome 113
## 327 hsa01521_EGFR_tyrosine_kinase_inhibitor_resistance 77
## 328 hsa04914_Progesterone-mediated_oocyte_maturation 77
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00524_Neomycin,_kanamycin_and_gentamicin_biosynthesis 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 55 1.55e-12
## 2 93 1.69e-12
## 3 56 2.69e-11
## 4 104 1.43e-09
## 5 68 8.96e-07
## ... ... ...
## 326 12 0.999
## 327 6 1.000
## 328 5 1.000
## 329 0 1.000
## 330 0 1.000
##
##
## $GSE14762
## $GSE14762$runtime
## elapsed
## 0.46
##
## $GSE14762$ranking
## DataFrame with 331 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa04145_Phagosome 144
## 2 hsa05150_Staphylococcus_aureus_infection 83
## 3 hsa05140_Leishmaniasis 73
## 4 hsa05416_Viral_myocarditis 57
## 5 hsa04514_Cell_adhesion_molecules_(CAMs) 141
## ... ... ...
## 327 hsa00440_Phosphonate_and_phosphinate_metabolism 6
## 328 hsa00750_Vitamin_B6_metabolism 6
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00400_Phenylalanine,_tyrosine_and_tryptophan_biosynthesis 5
## 331 hsa00471_D-Glutamine_and_D-glutamate_metabolism 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 45 3.07e-14
## 2 28 3.37e-10
## 3 25 2.11e-09
## 4 21 9.31e-09
## 5 35 2.26e-08
## ... ... ...
## 327 0 1
## 328 0 1
## 329 0 1
## 330 0 1
## 331 0 1Note: saving the results to file is typically recommended when carrying out several methods on multiple datasets for subsequent assessment. This makes results, potentially obtained from time-consuming computations, persistent across R sessions. In case of unexpected errors, this also allows resumption from the point of failure.
5.1 User-defined enrichment methods
User-defined enrichment methods can easily be plugged into the benchmarking framework. For demonstration, we define a dummy enrichment method that randomly draws p-values from a uniform distribution.
method <- function(se, gs)
{
ps <- runif(length(gs))
names(ps) <- names(gs)
return(ps)
}We then execute this method on two datasets of the GEO2KEGG compendium using runEA
as before.
res <- runEA(geo2kegg[1:2], method, kegg.gs)
res## $method
## $method$GSE1297
## $method$GSE1297$runtime
## elapsed
## 0.14
##
## $method$GSE1297$ranking
## DataFrame with 330 rows and 2 columns
## GENE.SET PVAL
## <character> <numeric>
## 1 hsa05223_Non-small_cell_lung_cancer 0.00185
## 2 hsa05016_Huntington_disease 0.00923
## 3 hsa00410_beta-Alanine_metabolism 0.01180
## 4 hsa04060_Cytokine-cytokine_receptor_interaction 0.01400
## 5 hsa04929_GnRH_secretion 0.01690
## ... ... ...
## 326 hsa04931_Insulin_resistance 0.973
## 327 hsa05145_Toxoplasmosis 0.983
## 328 hsa01523_Antifolate_resistance 0.993
## 329 hsa00190_Oxidative_phosphorylation 0.998
## 330 hsa04330_Notch_signaling_pathway 0.999
##
##
## $method$GSE14762
## $method$GSE14762$runtime
## elapsed
## 0.17
##
## $method$GSE14762$ranking
## DataFrame with 331 rows and 2 columns
## GENE.SET PVAL
## <character> <numeric>
## 1 hsa03450_Non-homologous_end-joining 0.0101
## 2 hsa05164_Influenza_A 0.0116
## 3 hsa05222_Small_cell_lung_cancer 0.0158
## 4 hsa04144_Endocytosis 0.0181
## 5 hsa05203_Viral_carcinogenesis 0.0251
## ... ... ...
## 327 hsa04122_Sulfur_relay_system 0.988
## 328 hsa05217_Basal_cell_carcinoma 0.992
## 329 hsa04657_IL-17_signaling_pathway 0.994
## 330 hsa04630_JAK-STAT_signaling_pathway 0.998
## 331 hsa04550_Signaling_pathways_regulating_pluripotency_of_stem_cells 0.9996 Benchmarking
Once methods have been applied to a chosen benchmark compendium, they can be subjected to a comparative assessment of runtime, statistical significance, and phenotype relevance.
To demonstrate how each criterion can be evaluated, we consider the example of the previous section where we applied ORA and CAMERA on 5 datasets of the GEO2KEGG compendium.
However, note that this minimal example is used to illustrate the basic functionality in a time-saving manner - as generally intended in a vignette. To draw conclusions on the individual performance of both methods, a more comprehensive assessment, involving application to the full compendium, should be carried out.
6.1 Runtime
Runtime, i.e. CPU time used, is an important measure of the applicability of a method. For enrichment methods, runtime mainly depends on whether methods rely on permutation testing, and how computationally intensive recomputation of the respective statistic in each permutation is (see Figure 4 in Geistlinger et al., 2016).
To obtain the runtime from the application of ORA and CAMERA to 5 datasets of the
GEO2KEGG compendium, we can use the readResults function as we have saved
results to the indicated result directory in the above call of runEA.
ea.rtimes <- readResults(res.dir, names(geo2kegg),
methods=c("ora", "camera"), type="runtime")
ea.rtimes## $ora
## GSE1297 GSE14762 GSE15471 GSE16515 GSE18842
## 0.41 0.46 0.50 0.57 0.55
##
## $camera
## GSE1297 GSE14762 GSE15471 GSE16515 GSE18842
## 0.27 0.28 0.37 0.30 0.64For visualization of assessment results, the bpPlot function can be used to
create customized boxplots for specific benchmark criteria.
bpPlot(ea.rtimes, what="runtime")
As both methods are simple gene set tests without permutation, they are among the fastest in the field - with CAMERA being roughly twice as fast as ORA.
mean(ea.rtimes$ora) / mean(ea.rtimes$camera)## [1] 1.338716.2 Fraction of significant gene sets
The statistical accuracy of the significance estimation in gene set tests has been repeatedly debated. For example, systematic inflation of statistical significance in ORA due to an unrealistic independence assumption between genes is well-documented (Goeman and Buehlmann, 2007). On the other hand, the permutation procedure incorporated in many gene set tests has been shown to be biased (Efron and Tibshirani, 2007), and also inaccurate if permutation \(p\)-values are reported as zero (Phipson and Smyth, 2010).
These shortcomings can lead to inappropriately large fractions of significant gene sets, and can considerably impair prioritization of gene sets in practice. It is therefore important to evaluate resulting fractions of significant gene sets in comparison to other methods and with respect to the fraction of differentially expressed genes as a baseline.
We use the readResults function to obtain the saved gene set rankings of ORA
and CAMERA when applied to 5 datasets of the GEO2KEGG compendium (see above call
of runEA).
ea.ranks <- readResults(res.dir, names(geo2kegg),
methods=c("ora", "camera"), type="ranking")
lengths(ea.ranks)## ora camera
## 5 5ea.ranks$ora[1:2]## $GSE1297
## DataFrame with 330 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa00190_Oxidative_phosphorylation 106
## 2 hsa05016_Huntington_disease 225
## 3 hsa05012_Parkinson_disease 115
## 4 hsa05010_Alzheimer_disease 289
## 5 hsa04714_Thermogenesis 188
## ... ... ...
## 326 hsa03040_Spliceosome 113
## 327 hsa01521_EGFR_tyrosine_kinase_inhibitor_resistance 77
## 328 hsa04914_Progesterone-mediated_oocyte_maturation 77
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00524_Neomycin,_kanamycin_and_gentamicin_biosynthesis 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 55 1.55e-12
## 2 93 1.69e-12
## 3 56 2.69e-11
## 4 104 1.43e-09
## 5 68 8.96e-07
## ... ... ...
## 326 12 0.999
## 327 6 1.000
## 328 5 1.000
## 329 0 1.000
## 330 0 1.000
##
## $GSE14762
## DataFrame with 331 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa04145_Phagosome 144
## 2 hsa05150_Staphylococcus_aureus_infection 83
## 3 hsa05140_Leishmaniasis 73
## 4 hsa05416_Viral_myocarditis 57
## 5 hsa04514_Cell_adhesion_molecules_(CAMs) 141
## ... ... ...
## 327 hsa00440_Phosphonate_and_phosphinate_metabolism 6
## 328 hsa00750_Vitamin_B6_metabolism 6
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00400_Phenylalanine,_tyrosine_and_tryptophan_biosynthesis 5
## 331 hsa00471_D-Glutamine_and_D-glutamate_metabolism 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 45 3.07e-14
## 2 28 3.37e-10
## 3 25 2.11e-09
## 4 21 9.31e-09
## 5 35 2.26e-08
## ... ... ...
## 327 0 1
## 328 0 1
## 329 0 1
## 330 0 1
## 331 0 1The evalNrSigSets calculates the percentage of significant gene sets given a
significance level alpha and a multiple testing correction method padj.
We can visualize assessment results as before using bpPlot, which demonstrates
here that CAMERA produces substantially larger fractions of significant
gene sets than ORA.
sig.sets <- evalNrSigSets(ea.ranks, alpha=0.05, padj="BH")
sig.sets## ora camera
## GSE1297 2.727273 17.87879
## GSE14762 13.293051 33.83686
## GSE15471 4.531722 21.14804
## GSE16515 2.719033 19.33535
## GSE18842 1.510574 22.05438bpPlot(sig.sets, what="sig.sets")
6.3 Phenotype relevance
As introduced above, Tarca et al. (2012 and 2013) also assigned a target pathway to each dataset of the GEO2KEGG compendium, which is considered highly-relevant for the respective phenotype investigated. However, the relation between dataset, investigated phenotype, and assigned target pathway is not always clear-cut. In addition, there is typically more than one pathway that is considered relevant for the investigated phenotype.
On the other hand, evaluations of published enrichment methods often conclude on phenotype relevance, if there is any association between top-ranked gene sets and the investigated phenotype.
A more systematic approach is used in the MalaCards database of human diseases. Here, relevance of GO and KEGG gene sets is summarized from (i) experimental evidence and (ii) co-citation with the respective disease in the literature.
6.3.1 MalaCards disease relevance rankings
The GSEABenchmarkeR package provides MalaCards relevance rankings for the diseases investigated in the datasets of the GEO2KEGG and TCGA compendia. Here, we load the relevance rankings for KEGG gene sets and demonstrate how they can be incorporated in the assessment of phenotype relevance.
We note that the relevance rankings contain different numbers of gene sets for different diseases, because only gene sets for which evidence/association with the respective disease has been found are listed in a ranking.
For demonstration, we inspect the relevance rankings for Alzheimer’s disease (ALZ) and breast cancer (BRCA) containing 57 and 142 gene sets, respectively.
mala.kegg.file <- file.path(data.dir, "malacards", "KEGG.rds")
mala.kegg <- readRDS(mala.kegg.file)
sapply(mala.kegg, nrow)## ACC ALZ BLCA BRCA CESC CHOL CML COAD CRC DCM DLBC DMND ESCA GBM HNSC HUNT
## 9 57 65 142 22 33 56 28 161 23 52 99 90 99 72 34
## KICH KIRC KIRP LAML LES LGG LIHC LUAD LUSC MESO OV PAAD PARK PCPG PDCO PRAD
## 4 8 8 108 49 24 98 54 23 3 31 70 39 12 31 12
## READ SARC SKCM STAD TGCT THCA THYM UCEC UCS UVM
## 2 73 42 24 24 81 61 90 29 55mala.kegg$ALZ## DataFrame with 57 rows and 4 columns
## TITLE REL.SCORE MATCHED.GENES
## <character> <numeric> <integer>
## hsa05010 Alzheimers disease 84.12 28
## hsa04932 Non-alcoholic fatty liver disease (NAFLD) 84.12 7
## hsa04726 Serotonergic synapse 49.19 8
## hsa04728 Dopaminergic synapse 49.19 8
## hsa04713 Circadian entrainment 49.19 5
## ... ... ... ...
## hsa05310 Asthma 9.81 1
## hsa05416 Viral myocarditis 9.81 2
## hsa05330 Allograft rejection 9.81 1
## hsa05332 Graft-versus-host disease 9.81 2
## hsa05321 Inflammatory bowel disease (IBD) 9.81 2
## TOTAL.GENES
## <integer>
## hsa05010 177
## hsa04932 160
## hsa04726 115
## hsa04728 130
## hsa04713 98
## ... ...
## hsa05310 35
## hsa05416 64
## hsa05330 41
## hsa05332 45
## hsa05321 67mala.kegg$BRCA## DataFrame with 142 rows and 4 columns
## TITLE REL.SCORE MATCHED.GENES TOTAL.GENES
## <character> <numeric> <integer> <integer>
## hsa05210 Colorectal cancer 166.1 35 70
## hsa05213 Endometrial cancer 166.1 23 61
## hsa05221 Acute myeloid leukemia 166.1 16 60
## hsa05218 Melanoma 166.1 35 78
## hsa05215 Prostate cancer 166.1 40 95
## ... ... ... ... ...
## hsa05020 Prion diseases 13.23 6 43
## hsa05144 Malaria 11.34 6 55
## hsa05143 African trypanosomiasis 11.28 5 36
## hsa04720 Long-term potentiation 10.93 7 67
## hsa05134 Legionellosis 10.81 6 596.3.2 Mapping between dataset ID and disease code
To obtain the relevance ranking of the respective disease investigated when
assessing results on a specific dataset, a mapping between dataset and
investigated disease is required.
The function readDataId2diseaseCodeMap reads such a mapping from a tabular
text file and turns it into a named vector - where the elements correspond to
the disease codes and the names to the dataset IDs.
Here, we read the mapping between GSE ID and disease code for the GEO2KEGG compendium.
d2d.file <- file.path(data.dir, "malacards", "GseId2Disease.txt")
d2d.map <- readDataId2diseaseCodeMap(d2d.file)
head(d2d.map)## GSE1145 GSE11906 GSE1297 GSE14762 GSE14924_CD4 GSE14924_CD8
## "DCM" "PDCO" "ALZ" "KIRC" "LAML" "LAML"6.3.3 Relevance score of a gene set ranking
To evaluate the phenotype relevance of a gene set ranking obtained from the
application of an enrichment method to an expression dataset, the function
evalRelevance assesses whether the ranking accumulates phenotype-relevant gene
sets (i.e. gene sets with high relevance scores) at the top.
Therefore, the function first transforms the ranks from the enrichment analysis
to weights - where the greater the weight of a gene set, the more it is ranked
towards the top of the GSEA ranking.
These weights are then multiplied by the corresponding relevance scores and
summed.
Here, we use evalRelevance to assess whether ORA, when applied to the GSE1297
dataset, recovers Alzheimer-relevant KEGG pathways.
ea.ranks$ora$GSE1297## DataFrame with 330 rows and 4 columns
## GENE.SET NR.GENES
## <character> <numeric>
## 1 hsa00190_Oxidative_phosphorylation 106
## 2 hsa05016_Huntington_disease 225
## 3 hsa05012_Parkinson_disease 115
## 4 hsa05010_Alzheimer_disease 289
## 5 hsa04714_Thermogenesis 188
## ... ... ...
## 326 hsa03040_Spliceosome 113
## 327 hsa01521_EGFR_tyrosine_kinase_inhibitor_resistance 77
## 328 hsa04914_Progesterone-mediated_oocyte_maturation 77
## 329 hsa00232_Caffeine_metabolism 5
## 330 hsa00524_Neomycin,_kanamycin_and_gentamicin_biosynthesis 5
## NR.SIG.GENES PVAL
## <numeric> <numeric>
## 1 55 1.55e-12
## 2 93 1.69e-12
## 3 56 2.69e-11
## 4 104 1.43e-09
## 5 68 8.96e-07
## ... ... ...
## 326 12 0.999
## 327 6 1.000
## 328 5 1.000
## 329 0 1.000
## 330 0 1.000obs.score <- evalRelevance(ea.ranks$ora$GSE1297, mala.kegg$ALZ)
obs.score## [1] 835.60716.3.4 Random relevance score distribution
To assess the significance of the observed relevance score of an enrichment
method applied to a specific dataset, i.e. to assess how likely it is to
observe a relevance score equal or greater than the one obtained, the function
compRand repeatedly applies evalRelevance to randomly drawn gene set rankings.
For demonstration, we compute relevance scores for 50 random gene set rankings and calculate the p-value as for a permutation test. This demonstrates that the relevance score obtained from applying ORA to GSE1297 significantly exceeds random scores.
gs.names <- ea.ranks$ora$GSE1297$GENE.SET
gs.ids <- substring(gs.names, 1, 8)
rand.scores <- compRand(mala.kegg$ALZ, gs.ids, perm=50)
summary(rand.scores)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 525.1 597.4 642.5 642.6 691.5 766.5(sum(rand.scores >= obs.score) + 1) / 51## [1] 0.019607846.3.5 Theoretical optimum
The observed relevance score can be used to compare phenotype relevance of two or more methods when applied to one particular dataset. However, as the number of gene sets in the relevance rankings differs between phenotypes (see above Section 5.3.1 MalaCards disease relevance rankings), comparison between datasets is not straightforward as resulting relevance scores scale differently.
Therefore, the function compOpt applies evalRelevance to the theoretically
optimal case in which the enrichment analysis ranking is identical to the
relevance score ranking. The ratio between observed and optimal score can then
be used to compare observed scores between datasets.
Here, we compute the optimal score for the Alzheimer relevance ranking, which indicates that the score observed for ORA, when applied to GSE1297, is about 68% of the optimal score.
opt.score <- compOpt(mala.kegg$ALZ, gs.ids)
opt.score## [1] 1228.75round(obs.score / opt.score * 100, digits=2)## [1] 686.3.6 Cross-dataset relevance score distribution
Evaluation of phenotype relevance with evalRelevance can also be done for
several methods applied across multiple datasets.
This allows to assess whether certain enrichment methods tend to produce
rankings of higher phenotype relevance than other methods when applied to a
compendium of datasets.
As explained in the previous section, observed relevance scores are always
expressed in relation to the respective optimal score.
For demonstration, we use evalRelevance to evaluate phenotype relevance of the
gene set rankings produced by ORA and CAMERA when applied to 5 datasets of the
GEO2KEGG compendium.
We can visualize assessment results as before using bpPlot, which demonstrates
here that ORA tends to recover more phenotype-relevant gene sets than CAMERA.
all.kegg.res <- evalRelevance(ea.ranks, mala.kegg, d2d.map[names(geo2kegg)])
bpPlot(all.kegg.res, what="rel.sets")
6.3.7 User-defined relevance rankings
It is also possible to refine the integrated MalaCards relevance rankings or to incorporate relevance rankings for additional datasets.
For demonstration, we modify the KEGG relevance ranking for Alzheimer’s disease by providing a random relevance score for each gene set.
rel.ranks <- mala.kegg$ALZ[,1:2]
rel.ranks$REL.SCORE <- runif(nrow(rel.ranks), min=1, max=100)
rel.ranks$REL.SCORE <- round(rel.ranks$REL.SCORE, digits = 2)
ind <- order(rel.ranks$REL.SCORE, decreasing = TRUE)
rel.ranks <- rel.ranks[ind,]
rel.ranks## DataFrame with 57 rows and 2 columns
## TITLE REL.SCORE
## <character> <numeric>
## hsa04728 Dopaminergic synapse 98.18
## hsa05211 Renal cell carcinoma 96.51
## hsa04721 Synaptic vesicle cycle 91.75
## hsa04660 T cell receptor signaling pathway 91.44
## hsa05330 Allograft rejection 88.34
## ... ... ...
## hsa04725 Cholinergic synapse 9.40
## hsa04724 Glutamatergic synapse 8.34
## hsa05320 Autoimmune thyroid disease 6.94
## hsa05321 Inflammatory bowel disease (IBD) 3.97
## hsa04215 Apoptosis 3.23We can then compute the aggregated relevance score of the ORA ranking according
to the updated relevance ranking using evalRelevance as before.
evalRelevance(ea.ranks$ora$GSE1297, rel.ranks)## [1] 1732.6977 Advanced
7.1 Caching
Preparing an expression data compendium for benchmarking of enrichment methods can be time-consuming. In case of the GEO2KEGG compendium, it requires to summarize probe level expression on gene level and to subsequently carry out differential expression analysis for each dataset.
To flexibly save and restore an already processed expression data compendium,
we can use the cacheResource function which builds on functionality of the
BiocFileCache package.
cacheResource(geo2kegg, rname="geo2kegg")This adds the selected 5 datasets of the GEO2KEGG compendium (as processed throughout this vignette) to the cache, and allows to restore it at a later time via
geo2kegg <- loadEData("geo2kegg", cache=TRUE)## Loading GEO2KEGG data compendium ...names(geo2kegg)## [1] "GSE1297" "GSE14762" "GSE15471" "GSE16515" "GSE18842"Note: to obtain the original unprocessed version of the compendium, set the
cache argument of the loadEData function to FALSE.
To clear the cache (use with care):
cache.dir <- rappdirs::user_cache_dir("GSEABenchmarkeR")
bfc <- BiocFileCache::BiocFileCache(cache.dir)
BiocFileCache::removebfc(bfc)7.2 Parallel computation
Leveraging functionality from BiocParallel, parallel computation
of the functions maPreproc, runDE, and
especially runEA, when applied to multiple datasets is straightforward.
Internally, these functions call BiocParallel::bplapply, which triggers parallel
computation as configured in the first element of BiocParallel::registered().
As a result, parallel computation is implicitly incorporated in the above calls
of these functions when carried out on a multi-core machine.
See the vignette of the BiocParallel package for an introduction.
Inspecting
BiocParallel::registered()## $SnowParam
## class: SnowParam
## bpisup: FALSE; bpnworkers: 4; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE
## bpexportglobals: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: SOCK
##
## $SerialParam
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE
## bpexportglobals: TRUE
## bplogdir: NA
## bpresultdir: NAshows that the execution uses a MulticoreParam per default (on Windows:
a SnowParam), where the bpnworkers attribute indicates the number of cores
involved in the computation.
To change the execution mode of functions provided in the
GSEABenchmarkeR package, accordingly configured computation
parameters of class BiocParallelParam can either directly be registered via
BiocParallel::register, or supplied with the parallel argument of the
respective function.
For demonstration, we configure here a BiocParallelParam to display a progress
bar
bp.par <- BiocParallel::registered()[[1]]
BiocParallel::bpprogressbar(bp.par) <- TRUEand supply runDE with the updated computation parameter.
geo2kegg <- runDE(geo2kegg, parallel=bp.par)##
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computer cluster, should consult the vignette of the BiocParallel
package to similarly configure a BiocParallelParam for that purpose.