Microarray data

To demonstrate the package’s functionality for microarray data, we consider expression measurements of patients with acute lymphoblastic leukemia [1]. A frequent chromosomal defect found among these patients is a translocation, in which parts of chromosome 9 and 22 swap places. This results in the oncogenic fusion gene BCR/ABL created by positioning the ABL1 gene on chromosome 9 to a part of the BCR gene on chromosome 22.

We load the ALL dataset

library(ALL) 
data(ALL)

and select B-cell ALL patients with and without the BCR/ABL fusion as described previously [2].

ind.bs <- grep("^B", ALL$BT)
ind.mut <- which(ALL$mol.biol %in% c("BCR/ABL", "NEG"))
sset <- intersect(ind.bs, ind.mut)
all.eset <- ALL[, sset]

We can now access the expression values, which are intensity measurements on a log-scale for 12,625 probes (rows) across 79 patients (columns).

dim(all.eset) 
#> Features  Samples 
#>    12625       79
exprs(all.eset)[1:4,1:4]
#>              01005    01010    03002    04007
#> 1000_at   7.597323 7.479445 7.567593 7.905312
#> 1001_at   5.046194 4.932537 4.799294 4.844565
#> 1002_f_at 3.900466 4.208155 3.886169 3.416923
#> 1003_s_at 5.903856 6.169024 5.860459 5.687997

As we often have more than one probe per gene, we summarize the gene expression values as the average of the corresponding probe values.

allSE <- probe2gene(all.eset)
head(rownames(allSE))
#> [1] "5595" "7075" "1557" "643"  "1843" "4319"

Note, that the mapping from the probe to gene is done automatically as long as you have the corresponding annotation package, here the hgu95av2.db package, installed. Otherwise, the mapping can be manually defined in the rowData slot.

rowData(se)
#> DataFrame with 1000 rows and 1 column
#>          ENTREZID
#>       <character>
#> 3075         3075
#> 572           572
#> 4267         4267
#> 26             26
#> 51384       51384
#> ...           ...
#> 5295         5295
#> 2966         2966
#> 9140         9140
#> 5558         5558
#> 1956         1956

RNA-seq data

To demonstrate the functionality of the package for RNA-seq data, we consider transcriptome profiles of four primary human airway smooth muscle cell lines in two conditions: control and treatment with dexamethasone [3].

We load the airway dataset

library(airway) 
data(airway)

For further analysis, we remove genes with very low read counts and measurements that are not mapped to an ENSEMBL gene ID.

airSE <- airway[grep("^ENSG", rownames(airway)),] 
airSE <- airSE[rowSums(assay(airSE)) > 4,]
dim(airSE) 
#> [1] 25133     8
assay(airSE)[1:4,1:4]
#>                 SRR1039508 SRR1039509 SRR1039512 SRR1039513
#> ENSG00000000003        679        448        873        408
#> ENSG00000000419        467        515        621        365
#> ENSG00000000457        260        211        263        164
#> ENSG00000000460         60         55         40         35

Obtaining gene sets

The function getGenesets can be used to download gene sets from databases such as GO and KEGG. We can use the function to download all KEGG pathways for a chosen organism (here: Homo sapiens) as gene sets.

 kegg.gs <- getGenesets(org = "hsa", db = "kegg")

Analogously, the function getGenesets can be used to retrieve GO terms of a selected ontology (here: biological process, BP) as defined in the GO.db annotation package.

 go.gs <- getGenesets(org = "hsa", db = "go", onto = "BP", mode = "GO.db")

If provided a file, the function parses user-defined gene sets from GMT file format. Here, we use this functionality for reading a list of already downloaded KEGG gene sets for Homo sapiens containing NCBI Entrez Gene IDs.

gmt.file <- file.path(data.dir, "hsa_kegg_gs.gmt")
hsa.gs <- getGenesets(gmt.file) 
length(hsa.gs) 
#> [1] 39
hsa.gs[1:2]
#> $hsa05416_Viral_myocarditis
#>  [1] "100509457" "101060835" "1525"      "1604"      "1605"      "1756"     
#>  [7] "1981"      "1982"      "25"        "2534"      "27"        "3105"     
#> [13] "3106"      "3107"      "3108"      "3109"      "3111"      "3112"     
#> [19] "3113"      "3115"      "3117"      "3118"      "3119"      "3122"     
#> [25] "3123"      "3125"      "3126"      "3127"      "3133"      "3134"     
#> [31] "3135"      "3383"      "3683"      "3689"      "3908"      "4624"     
#> [37] "4625"      "54205"     "5551"      "5879"      "5880"      "5881"     
#> [43] "595"       "60"        "637"       "6442"      "6443"      "6444"     
#> [49] "6445"      "71"        "836"       "841"       "842"       "857"      
#> [55] "8672"      "940"       "941"       "942"       "958"       "959"      
#> 
#> $`hsa04622_RIG-I-like_receptor_signaling_pathway`
#>  [1] "10010"  "1147"   "1432"   "1540"   "1654"   "23586"  "26007"  "29110" 
#>  [9] "338376" "340061" "3439"   "3440"   "3441"   "3442"   "3443"   "3444"  
#> [17] "3445"   "3446"   "3447"   "3448"   "3449"   "3451"   "3452"   "3456"  
#> [25] "3467"   "3551"   "3576"   "3592"   "3593"   "3627"   "3661"   "3665"  
#> [33] "4214"   "4790"   "4792"   "4793"   "5300"   "54941"  "55593"  "5599"  
#> [41] "5600"   "5601"   "5602"   "5603"   "56832"  "57506"  "5970"   "6300"  
#> [49] "64135"  "64343"  "6885"   "7124"   "7186"   "7187"   "7189"   "7706"  
#> [57] "79132"  "79671"  "80143"  "841"    "843"    "8517"   "8717"   "8737"  
#> [65] "8772"   "9140"   "9474"   "9636"   "9641"   "9755"

Note #1: Use getGenesets with db = "msigdb" to obtain gene set collections for 11 different species from the Molecular Signatures Database (MSigDB). Analogously, getGenesets with db = "enrichr" allows to obtain gene set libraries from the comprehensive Enrichr collection for 5 different species.

Note #2: The idMap function can be used to map gene sets from NCBI Entrez Gene IDs to other common gene ID types such as ENSEMBL gene IDs or HGNC symbols as described in Section @ref(sec:idmap).

Set-based enrichment analysis

Currently, the following set-based enrichment methods are supported

sbeaMethods()
#>  [1] "ora"        "safe"       "gsea"       "gsa"        "padog"     
#>  [6] "globaltest" "roast"      "camera"     "gsva"       "samgs"     
#> [11] "ebm"        "mgsa"

• ORA: Overrepresentation Analysis (simple and frequently used test based on the hypergeometric distribution, see [4] for a critical review),

• SAFE: Significance Analysis of Function and Expression (resampling version of ORA implements additional test statistics, e.g. Wilcoxon’s rank sum, and allows us to estimate the significance of gene sets by sample permutation; implemented in the safe package),

• GSEA: Gene Set Enrichment Analysis (frequently used and widely accepted, uses a Kolmogorov–Smirnov statistic to test whether the ranks of the p-values of genes in a gene set resemble a uniform distribution [5]),

• PADOG: Pathway Analysis with Down-weighting of Overlapping Genes (incorporates gene weights to favor genes appearing in few pathways versus genes that appear in many pathways; implemented in the PADOG package),

• ROAST: ROtAtion gene Set Test (uses rotation instead of permutation for assessment of gene set significance; implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively),

• CAMERA: Correlation Adjusted MEan RAnk gene set test (accounts for inter-gene correlations as implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively),

• GSA: Gene Set Analysis (differs from GSEA by using the maxmean statistic, i.e. the mean of the positive or negative part of the gene scores in the gene set; implemented in the GSA package),

• GSVA: Gene Set Variation Analysis (transforms the data from a gene by sample matrix to a gene set by sample matrix, thereby allowing the evaluation of gene set enrichment for each sample; implemented in the GSVA package)

• GLOBALTEST: Global testing of groups of genes (general test of groups of genes for association with a response variable; implemented in the globaltest package),

• SAMGS: Significance Analysis of Microarrays on Gene Sets (extending the SAM method for single genes to gene set analysis [6]),

• EBM: Empirical Brown’s Method (combines p-values of genes in a gene set using Brown’s method to combine p-values from dependent tests; implemented in EmpiricalBrownsMethod),

• MGSA: Model-based Gene Set Analysis (Bayesian modeling approach taking set overlap into account by working on all sets simultaneously, thereby reducing the number of redundant sets; implemented in mgsa).

See also Appendix @ref(sec:primer) for a comprehensive introduction on underlying statistical concepts.

Guidelines for input and method selection

We recently performed a comprehensive assessment of the available set-based enrichment methods, and identified significant differences in runtime and applicability to RNA-seq data, the fraction of enriched gene sets depending on the null hypothesis tested, and the detection of relevant processes [7]. Based on these results, we make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance:

• for the exploratory analysis of simple gene lists, we recommend ORA given its ease of applicability, fast runtime, and evident relevance of resulting gene set rankings provided that input gene list and reference gene list are chosen carefully and remembering ORA’s propensity for type I error rate inflation when genes tend to be co-expressed within sets.

• for the analysis of pre-ranked gene lists accompanied by gene scores such as fold changes, alternatives to ORA such as pre-ranked GSEA or pre-ranked CAMERA exists.

• for expression-based enrichment analysis on the full expression matrix, we recommend providing normalized log2 intensities for microarray data, and logTPMs (or logRPKMs / logFPKMs) for RNA-seq data. When given raw read counts, we recommend applying a variance-stabilizing transformation such as voom to arrive at library-size normalized logCPMs.

• if the question of interest is to test for association of any gene in the set with the phenotype (self-contained null hypothesis), we recommend ROAST or GSVA that both test a directional hypothesis (genes in the set tend to be either predominantly up- or down-regulated). Both methods can be applied for simple or extended experimental designs, where ROAST is the more natural choice for the comparison of sample groups and also allows one to test a mixed hypothesis (genes in the set tend to be differentially expressed, regardless of the direction). The main strength of GSVA lies in its capabilities for analyzing single samples.

• if the question of interest is to test for an excess differential expression in a gene set relative to genes outside the set (competitive null hypothesis), which we believe comes closest to the expectations and intuition of most end users when performing GSEA, we recommend PADOG, which is slower to run but resolves major shortcomings of ORA, and has desirable properties for the analyzed criteria and when compared to other competitive methods. However, PADOG is limited to testing a mixed hypothesis in a comparison of two sample groups, optionally including paired samples or sample batches. Therefore, we recommend the highly customizable SAFE for testing a directional hypothesis or in situations of more complex experimental designs such as comparisons between multiple groups, continuous phenotypes or the presence of covariates.

See also Reference [7] for the results of the benchmarking study and the GSEABenchmarkeR package for a general framework for reproducible benchmarking of gene set enrichment methods.

sbea.res <- sbea(method = "ora", se = allSE, gs = hsa.gs, perm = 0, alpha = 0.1) 
gsRanking(sbea.res)
#> DataFrame with 4 rows and 4 columns
#>                 GENE.SET  NR.GENES NR.SIG.GENES      PVAL
#>              <character> <numeric>    <numeric> <numeric>
#> 1 hsa05130_Pathogenic_..        44            5    0.0298
#> 2 hsa05206_MicroRNAs_i..       133           10    0.0371
#> 3 hsa04622_RIG-I-like_..        55            5    0.0681
#> 4 hsa04670_Leukocyte_t..        94            7    0.0810

 airSE <- normalize(airSE, norm.method = "vst")

air.res <- sbea(method = "gsea", se = airSE, gs = hsa.gs) 
gsRanking(sbea.res)

Result exploration

The result of every enrichment analysis is a ranking of gene sets by the corresponding p-value. The gsRanking function displays by default only those gene sets satisfying the chosen significance level α, but we can use to obtain the full ranking.

 gsRanking(sbea.res, signif.only = FALSE)
#> DataFrame with 39 rows and 4 columns
#>                   GENE.SET  NR.GENES NR.SIG.GENES      PVAL
#>                <character> <numeric>    <numeric> <numeric>
#> 1   hsa05130_Pathogenic_..        44            5    0.0298
#> 2   hsa05206_MicroRNAs_i..       133           10    0.0371
#> 3   hsa04622_RIG-I-like_..        55            5    0.0681
#> 4   hsa04670_Leukocyte_t..        94            7    0.0810
#> 5   hsa05100_Bacterial_i..        64            5    0.1130
#> ...                    ...       ...          ...       ...
#> 35       hsa05218_Melanoma        58            0         1
#> 36  hsa05150_Staphylococ..        46            0         1
#> 37  hsa03420_Nucleotide_..        41            0         1
#> 38  hsa03030_DNA_replica..        33            0         1
#> 39  hsa03410_Base_excisi..        27            0         1

While such a ranked list is the standard output of existing enrichment tools, the package EnrichmentBrowser provides visualization and interactive exploration of resulting gene sets far beyond that point. Using the eaBrowse function creates a HTML summary from which each gene set can be inspected in detail (this builds on functionality from the ReportingTools package).

The various options are described in Figure @ref(fig:oraRes).

 eaBrowse(sbea.res)

(#fig:oraRes) ORA result view For each significant gene set in the ranking, the user can select to view (1) a gene report, that lists all genes of a set along with fold change and DE p-value, (2) interactive overview plots such as heatmap, p-value distribution, and volcano plot, (3) the pathway in KEGG with differentially expressed genes highlighted in red.

Network-based enrichment analysis

Having found sets of genes that are differentially regulated in the ALL data, we are now interested in whether these findings can be supported by known regulatory interactions.

For example, we want to know whether transcription factors and their target genes are expressed in accordance to the connecting regulations (activation/inhibition). Such information is usually given in a gene regulatory network derived from specific experiments or compiled from the literature ([8] for an example).

There are well-studied processes and organisms for which comprehensive and well-annotated regulatory networks are available, e.g. the RegulonDB for E. coli and Yeastract for S. cerevisiae. However, there are also cases where such a network is missing. A basic workaround is to compile a network from regulations in pathway databases such as KEGG.

hsa.grn <- compileGRN(org="hsa", db="kegg")
head(hsa.grn)
#>      FROM    TO          TYPE
#> [1,] "10000" "100132074" "-" 
#> [2,] "10000" "1026"      "+" 
#> [3,] "10000" "1026"      "-" 
#> [4,] "10000" "1027"      "-" 
#> [5,] "10000" "10488"     "+" 
#> [6,] "10000" "107"       "+"

Now, we are able to perform enrichment analysis using the compiled network. Currently, the following network-based enrichment analysis methods are supported

 nbeaMethods()
#> [1] "ggea"        "spia"        "pathnet"     "degraph"     "ganpa"      
#> [6] "cepa"        "topologygsa" "netgsa"      "neat"

• GGEA: Gene Graph Enrichment Analysis (evaluates the consistency of known regulatory interactions with the observed expression data [9]),

• SPIA: Signaling Pathway Impact Analysis (combines ORA with the the probability that expression changes are propagated across the pathway topology; implemented in the SPIA package),

• PathNet: Pathway Analysis using Network Information (applies ORA on combined evidence for the observed signal for gene nodes and the signal implied by connected neighbors in the network; implemented in the PathNet package),

• DEGraph: Differential expression testing for gene graphs (multivariate testing of differences in mean incorporating underlying graph structure; implemented in the DEGraph package),

• TopologyGSA: Topology-based Gene Set Analysis (uses Gaussian graphical models to incorporate the dependence structure among genes as implied by pathway topology; implemented in the topologyGSA package),

• GANPA: Gene Association Network-based Pathway Analysis (incorporates network-derived gene weights in the enrichment analysis; implemented in the GANPA package),

• CePa: Centrality-based Pathway enrichment (incorporates network centralities as node weights mapped from differentially expressed genes in pathways; implemented in the CePa package),

• NetGSA: Network-based Gene Set Analysis (incorporates external information about interactions among genes as well as novel interactions learned from data; implemented in the NetGSA package),

For demonstration, we perform GGEA using the compiled KEGG regulatory network.

nbea.res <- nbea(method="ggea", se=allSE, gs=hsa.gs, grn=hsa.grn) 
gsRanking(nbea.res)
#> DataFrame with 8 rows and 5 columns
#>                 GENE.SET   NR.RELS RAW.SCORE NORM.SCORE      PVAL
#>              <character> <numeric> <numeric>  <numeric> <numeric>
#> 1 hsa04622_RIG-I-like_..        36     13.70      0.382  0.000999
#> 2 hsa05416_Viral_myoca..         7      3.29      0.470  0.003000
#> 3 hsa05217_Basal_cell_..        17      6.60      0.388  0.007990
#> 4 hsa04390_Hippo_signa..        66     23.20      0.351  0.007990
#> 5 hsa04520_Adherens_ju..        12      4.86      0.405  0.008990
#> 6 hsa05134_Legionellosis        18      6.71      0.373  0.020000
#> 7 hsa05412_Arrhythmoge..         5      2.14      0.429  0.023000
#> 8 hsa04621_NOD-like_re..        40     13.90      0.349  0.031000

The resulting ranking lists, for each statistically significant gene set, the number of relations of the network involving a member of the gene set under study (NR.RELS), the sum of consistencies over the relations of the set (RAW.SCORE), the score normalized by induced network size (NORM.SCORE = RAW.SCORE / NR.RELS), and the statistical significance of each gene set based on a permutation approach.

A GGEA graph for a gene set depicts the consistency of each interaction in the set. Nodes (genes) are colored according to the expression (up-/down-regulated) and edges (interactions) are colored according to consistency, i.e. how well the interaction type (activation/inhibition) is reflected in the correlation of the observed expression of both interaction partners.

par(mfrow=c(1,2))
ggeaGraph( gs=hsa.gs[["hsa05217_Basal_cell_carcinoma"]], grn=hsa.grn, se=allSE) 
ggeaGraphLegend()

User-defined enrichment methods

The goal of the EnrichmentBrowser package is to provide frequently used enrichment methods. However, it is also possible to exploit its visualization capabilities with user-defined set-based enrichment methods.

This requires implementing a function that takes the characteristic arguments se (expression data) and gs (gene sets).

In addition, it must return a numeric vector ps storing the resulting p-value for each gene set in gs. The p-value vector must also be named accordingly (i.e.names(ps) == names(gs)).

Let us consider the following dummy enrichment method, which randomly renders five gene sets significant and the remaining insignificant.

 dummySBEA <- function(se, gs) {
   sig.ps <- sample(seq(0, 0.05, length = 1000), 5) 
   insig.ps <- sample(seq(0.1, 1, length = 1000), length(gs) - 5) 
   ps <- sample(c(sig.ps, insig.ps), length(gs)) 
   names(ps) <- names(gs) 
   return(ps)
 }

We can plug this method into sbea as before.

sbea.res2 <- sbea(method = dummySBEA, se = allSE, gs = hsa.gs) 
gsRanking(sbea.res2)
#> DataFrame with 5 rows and 2 columns
#>                 GENE.SET      PVAL
#>              <character> <numeric>
#> 1      hsa05218_Melanoma    0.0030
#> 2 hsa05206_MicroRNAs_i..    0.0198
#> 3 hsa05010_Alzheimer's..    0.0290
#> 4 hsa04066_HIF-1_signa..    0.0440
#> 5 hsa04380_Osteoclast_..    0.0449

As described in the previous section, it is also possible to analogously plugin user-defined network-based enrichment methods into nbea.

Where does it all come from?

Test whether known biological functions or processes are over-represented (= enriched) in an experimentally-derived gene list, e.g. a list of differentially expressed (DE) genes. See [4] for a critical review.

Example: Transcriptomic study, in which 12,671 genes have been tested for differential expression between two sample conditions and 529 genes were found DE.

Among the DE genes, 28 are annotated to a specific functional gene set, which contains in total 170 genes. This setup corresponds to a 2 × 2 contingency table,

deTable <- matrix(c(28, 142, 501, 12000), 
                  nrow = 2, 
                  dimnames = list(c("DE", "Not.DE"), c("In.gene.set", "Not.in.gene.set"))) 
deTable
#>        In.gene.set Not.in.gene.set
#> DE              28             501
#> Not.DE         142           12000

where the overlap of 28 genes can be assessed based on the hypergeometric distribution. This corresponds to a one-sided version of Fisher’s exact test, yielding here a significant enrichment.

fisher.test(deTable, alternative = "greater")
#> 
#>  Fisher's Exact Test for Count Data
#> 
#> data:  deTable
#> p-value = 4.088e-10
#> alternative hypothesis: true odds ratio is greater than 1
#> 95 percent confidence interval:
#>  3.226736      Inf
#> sample estimates:
#> odds ratio 
#>   4.721744

This basic principle is at the foundation of major public and commercial enrichment tools such as DAVID and Pathway Studio.

Although gene set enrichment methods have been primarily developed and applied on transcriptomic data, they have recently been modified, extended and applied also in other fields of genomic and biomedical research. This includes novel approaches for functional enrichment analysis of proteomic and metabolomic data as well as genomic regions and disease phenotypes [10–13].

Gene sets, pathways & regulatory networks

Gene sets are simple lists of usually functionally related genes without further specification of relationships between genes.

Pathways can be interpreted as specific gene sets, typically representing a group of genes that work together in a biological process. Pathways are commonly divided in metabolic and signaling pathways. Metabolic pathways such as glycolysis represent biochemical substrate conversions by specific enzymes. Signaling pathways such as the MAPK signaling pathway describe signal transduction cascades from receptor proteins to transcription factors, resulting in activation or inhibition of specific target genes.

Gene regulatory networks describe the interplay and effects of regulatory factors (such as transcription factors and microRNAs) on the expression of their target genes.

Resources

GO and KEGG annotations are most frequently used for the enrichment analysis of functional gene sets. Despite an increasing number of gene set and pathway databases, they are typically the first choice due to their long-standing curation and availability for a wide range of species.

Gene Ontology (GO) consists of three major sub-ontologies that classify gene products according to molecular function (MF), biological process (BP), and cellular component (CC). Each ontology consists of GO terms that define MFs, BPs, or CCs to which specific genes are annotated. The terms are organized in a directed acyclic graph, where the edges between the terms represent relationships of different types. They relate the terms according to a parent-child scheme, i.e. parent terms denote more general entities, whereas child terms represent more specific entities.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of manually drawn pathway maps representing molecular interaction and reaction networks. These pathways cover a wide range of biochemical processes that can be divided into 7 broad categories: metabolism, genetic and environmental information processing, cellular processes, organismal systems, human diseases, and drug development. Metabolism and drug development pathways differ from pathways of the other 5 categories by illustrating reactions between chemical compounds. Pathways of the other 5 categories illustrate molecular interactions between genes and gene products.

Gene set analysis vs. gene set enrichment analysis

The two predominantly used enrichment methods are:

• Overrepresentation analysis (ORA), testing whether a gene set contains disproportional many genes of significant expression change, based on the procedure outlined in section @ref(sec:ora),

• Gene set enrichment analysis (GSEA), testing whether genes of a gene set accumulate at the top or bottom of the full gene vector ordered by direction and magnitude of expression change [5].

However, the term gene set enrichment analysis nowadays subsumes a general strategy implemented by a wide range of methods [14]. Those methods have in common the same goal, although the approach and statistical model can vary substantially [4, 15].

To better distinguish from the specific method, some authors use the term gene set analysis to denote the general strategy. However, there is also a specific method of this name [16].

Underlying null: competitive vs. self-contained

Goeman and Buehlmann, 2007, classified existing enrichment methods into competitive and self-contained based on the underlying null hypothesis [4].

• Competitive null hypothesis: the genes in the set of interest are at most as often DE as the genes not in the set,

• Self-contained null hypothesis: no genes in the set of interest are DE.

Although the authors argue that a self-contained null is closer to the the actual question of interest, the vast majority of enrichment methods is competitive.

Goeman and Buehlmann further raise several critical issues concerning the 2 × 2 ORA:

• a rather arbitrary classification of genes in DE / not DE,

• based on gene sampling, although sampling of subjects is appropriate,

• unrealistic independence assumption between genes, resulting in highly anti-conservative p-values.

With regard to these statistical concerns, GSEA is considered superior:

• takes all measured genes into account,

• subject sampling via permutation of class labels,

• the incorporated permutation procedure implicitly accounts for correlations between genes.

However, the simplicity and general applicability of ORA is unmet by subsequent methods improving on these issues. For instance, GSEA requires the expression data as input, which is not available for gene lists derived from other experiment types. On the other hand, the involved sample permutation procedure has been proven inaccurate and time-consuming [16–18].

Generations: ora, fcs & topology-based

Khatri et al., 2012, have taken a slightly different approach by classifying methods along the timeline of development into three generations [15]:

1. Generation: ORA methods based on the 2 × 2 contingency table test,

2. Generation: functional class scoring (FCS) methods such as GSEA, which compute gene set (= functional class) scores by summarizing per-gene DE statistics,

3. Generation: topology-based methods, explicitly taking into account interactions between genes as defined in signaling pathways and gene regulatory networks ([9] for an example).

Although topology-based (also: network-based) methods appear to be the most realistic, their straightforward application can be impaired by features that are not detectable on the transcriptional level (such as protein-protein interactions) and insufficient network knowledge [8, 19].

Given the individual benefits and limitations of existing methods, cautious interpretation of results is required to derive valid conclusions. Whereas no single method is best suited for all application scenarios, applying multiple methods can be beneficial. This has been shown to filter out spurious hits of individual methods, thereby reducing the outcome to gene sets accumulating evidence from different methods [20, 21].