1. Quick Start: miRNA-oriented enrichment analysis with miREA
Introduction
miREA performs microRNA-oriented enrichment analysis by modeling miRNA-gene interaction (MGI) networks directly, rather than collapsing them to miRNA- or gene-level sets. Conventional node-centric enrichment approaches treat miRNAs and genes as independent units, which discards the inherent many-to-many structure of miRNA-gene regulation and can bias functional interpretation. miREA instead models the regulatory edges themselves.
The package implements:
- Four node-based methods:
TG_ORA,TG_Score(target-gene-based),MiR_ORA,MiR_Score(miRNA-based). - Five edge-based methods:
Edge_ORA,Edge_Score1D,Edge_Score2D,Edge_Topology,Edge_Network, spanning over-representation, scoring-based, topology-aware, and network-propagation approaches.
All nine methods share a common workflow: prepare method-specific
input data with getAllInputData(), run the selected methods
with miREA(), and visualize results with
plotSummary(), plotHeatmap(), or
plotHeatmapSankey().
Relation to existing packages
Several Bioconductor/CRAN packages address related but distinct
questions. clusterProfiler
performs over-representation and gene set enrichment analysis, but for
gene enrichment analysis. It tests a list of genes against pathway gene
sets, with no special focus on miRNAs. CePa
incorporates pathway topology via node centrality measures but operates
purely on gene-level and node-level, with no focus on miRNAs as well.
multiMiR
and similar miRNA-target database packages retrieve curated or predicted
miRNA-gene interactions but do not provide an
enrichment/significance-testing framework over them. miRNApath
performs over-representation analysis by expanding each miRNA-gene pair
into its own hypergeometric test unit, using a single ORA-style method.
MIRit is a
comprehensive, actively maintained framework for integrative miRNA-mRNA
analysis, covering differential expression, correlation-based
integration, and gene-set enrichment of miRNA targets. Here, we present
miREA, to our knowledge the first miRNA-oriented enrichment package that
adopts an MGI (miRNA-gene interaction) edge-centric enrichment. It spans
over-representation, scoring, topology-aware, and network-propagation
methods, and uses expression-informed edge scores rather than a plain
membership test to characterize miRNA function at the pathway level.
Internally, its ORA and GSEA-style methods (TG_ORA,
MiR_ORA, Edge_ORA, TG_Score,
MiR_Score, Edge_Score1D) reuse clusterProfiler’s
enricher()/GSEA() rather than reimplementing
them.
Installation
Example data
This vignette uses a small, self-contained example dataset bundled
with the package (see ?miREA_example_data), derived from
the real bladder urothelial carcinoma (BLCA) data used in the miREA
manuscript. It covers 16 bladder-cancer-specific MSigDB gene sets and 99
TCGA samples (80 tumor and 19 normal, used for differential expression).
The 80 tumor samples are also used for miRNA-gene correlation. For the
full datasets used in the manuscript, see the Zenodo deposit.
library(miREA)
data(toy_pathway)
data(toy_gene_DEdata)
data(toy_mir_DEdata)
data(toy_gene_mat)
data(toy_mir_mat)
data(toy_background_MGI)
data(toy_background_GGI)
head(toy_pathway)
#> pathway gene
#> 1 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ABCF2
#> 2 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ABI3BP
#> 3 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ACOT7
#> 4 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ACOX3
#> 5 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ACSF2
#> 6 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ACSL1
dim(toy_gene_mat)
#> [1] 34721 80
dim(toy_mir_mat)
#> [1] 995 80Step 1: Prepare input data
getAllInputData() builds the method-specific input
formats (DE gene/miRNA lists, ranked score vectors, MGI dictionaries,
GGI networks, etc.) from the raw expression and differential-expression
tables. Here we request all nine methods at once. See
?default_method_list for the full list of supported method
names.
input_data <- getAllInputData(
methods = default_method_list,
pathway = toy_pathway,
mir_DEdata = toy_mir_DEdata,
gene_DEdata = toy_gene_DEdata,
background_MGI = toy_background_MGI,
background_GGI = toy_background_GGI,
GGI_source = "Omnipath",
gene_mat = toy_gene_mat,
mir_mat = toy_mir_mat,
scoreFun = "rank"
)
#> Generate all input data...
#> Start generate expression data...
#> Start generate pathway data...
#> Start generate pathway_GGI data...
#> Start generate GGI data for whole network...
#> Start generate MGI data for whole network...
#> Start generate dict_MGI data for permutation...
#> All the input data is ready!
names(input_data)
#> [1] "raw_data" "background_MGI" "background_GGI" "data"
#> [5] "pathway" "pathway_GGI" "GGI" "MGI"
#> [9] "dict_MGI"Interoperability with standard Bioconductor classes
gene_mat/mir_mat also accept a SummarizedExperiment
object in place of a raw matrix/dataframe (the first assay is used), and
pathway accepts a GSEABase
GeneSetCollection in place of the two-column dataframe.
This lets miREA plug directly into analyses that already represent
expression data and gene sets in these standard classes, instead of
requiring a manual conversion step.
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
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#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#>
#> Attaching package: 'generics'
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#> IQR, mad, sd, var, xtabs
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#> data
#> The following objects are masked from 'package:base':
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#> order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#> rbind, Reduce, rownames, sapply, saveRDS, scale, sequence, table,
#> tapply, transform, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
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#> findMatches
#> The following objects are masked from 'package:base':
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#> expand.grid, I, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
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#> The following object is masked from 'package:MatrixGenerics':
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#> rowMedians
#> The following objects are masked from 'package:matrixStats':
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#> anyMissing, rowMedians
library(GSEABase)
#> Loading required package: annotate
#> Loading required package: AnnotationDbi
#> Loading required package: XML
#> Loading required package: graph
#>
#> Attaching package: 'graph'
#> The following object is masked from 'package:XML':
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#> addNode
gene_se <- SummarizedExperiment(assays = list(expr = as.matrix(toy_gene_mat)))
mir_se <- SummarizedExperiment(assays = list(expr = as.matrix(toy_mir_mat)))
pathway_gsc <- GeneSetCollection(Map(function(genes, name) GeneSet(genes, setName = name),
split(toy_pathway$gene, toy_pathway$pathway),
names(split(toy_pathway$gene, toy_pathway$pathway))
))
input_data_bioc <- getAllInputData(
methods = "Edge_ORA", pathway = pathway_gsc,
mir_DEdata = toy_mir_DEdata, gene_DEdata = toy_gene_DEdata,
background_MGI = toy_background_MGI,
gene_mat = gene_se, mir_mat = mir_se
)
#> Generate all input data...
#> Start generate expression data...
#> Start generate pathway data...
#> All the input data is ready!
identical(input_data_bioc$data$Edge_ORA, input_data$data$Edge_ORA)
#> [1] TRUEStep 2: Run enrichment analysis
miREA() requires a background for each
target (TG, MiR, Edge) unless you
already supplied one via getAllInputData()’s
background_MGI. miREA does not ship a bundled default
background. Build one from your own background_MGI/background_GGI, or
download the full background used in the manuscript from the Zenodo
deposit linked above.
background <- list(
TG = unique(toy_pathway$gene),
MiR = unique(input_data$pathway$MiR$miRNA),
Edge = unique(paste0(toy_background_MGI$miRNA, ":", toy_background_MGI$gene))
)
result <- miREA(
methods = default_method_list,
input_data = input_data,
background = background,
minSize = NULL,
maxSize = NULL,
pvalueType = list(TG = "std", MiR = "std", Edge = "neg"),
pAdjMethod = "BH",
pvalueCutoff = 1,
iter = 20,
ncores = 1
)
#> Start Enrichment analysis...
#> === Start time: 2026-10-16 21:31:35 ===
#>
#> Start TG-ORA analysis ...
#>
#> TG-ORA analysis have finished!
#> - TG_ORA finished, time cost: 0.638999999999999 s.
#>
#> Start TG-Score analysis ...
#> Warning in gsea(geneList = geneList, gene_sets = geneSets, weight = weight, :
#> For some pathways, in reality P-values are less than 1e-10. You can set the eps
#> argument to zero for better estimation.
#> TG-Score analysis have finished!
#> - TG_Score finished, time cost: 0.483000000000001 s.
#>
#> Start MiR-ORA analysis ...
#> 4 of 412 miRNAs are not included in the background. Those are:
#> hsa-miR-127-3p hsa-miR-517a-3p hsa-miR-517b-3p hsa-miR-1307-5p
#> Those miRNAs are deleted.
#> MiR-ORA analysis have finished!
#> - MiR_ORA finished, time cost: 0.0219999999999985 s.
#>
#> Start MiR-Score analysis ...
#> Warning in prepare_gsea_inputs(geneList, scoreType, exponent): There are ties
#> in the preranked stats (0.3% of the list). The order of those tied genes will
#> be arbitrary, which may produce unexpected results.
#> MiR-Score analysis have finished!
#> - MiR_Score finished, time cost: 0.776 s.
#>
#> Start Edge-ORA analysis ...
#> Edge-ORA analysis have finished!
#> - Edge_ORA finished, time cost: 0.0800000000000018 s.
#>
#> Start Edge-Score1D analysis ...
#> Warning in prepare_gsea_inputs(geneList, scoreType, exponent): There are ties
#> in the preranked stats (0.31% of the list). The order of those tied genes will
#> be arbitrary, which may produce unexpected results.
#> Warning in prepare_gsea_inputs(geneList, scoreType, exponent): For some
#> pathways, in reality P-values are less than 1e-10. You can set the eps argument
#> to zero for better estimation.
#> Edge-Score1D analysis have finished!
#> - Edge_Score1D finished, time cost: 25.22 s.
#>
#> Start Edge_Score2D analysis ...
#> Generate random MGI set radius on 1 cores...
#> Calculate pathway radius and siginificant level...
#> 1 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_1_DN : 0.937
#> 2 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_3_UP : 0.9997
#> 3 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2B : 0
#> 4 / 16 , WU_SILENCED_BY_METHYLATION_IN_BLADDER_CANCER : 1e-04
#> 5 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_3_DN : 0
#> 6 / 16 , OSMAN_BLADDER_CANCER_UP : 0
#> 7 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_1_UP : 0
#> 8 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2A_DN : 0
#> 9 / 16 , OSMAN_BLADDER_CANCER_DN : 0.9856
#> 10 / 16 , KEGG_BLADDER_CANCER : 0.9492
#> 11 / 16 , LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q : 0
#> 12 / 16 , LINDGREN_BLADDER_CANCER_HIGH_RECURRENCE : 0
#> 13 / 16 , WP_BLADDER_CANCER : 0
#> 14 / 16 , HP_BLADDER_NEOPLASM : 0.1978
#> 15 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2A_UP : 0.0184
#> 16 / 16 , WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER : 0.5434
#> Edge-Score2D analysis have finished!
#> - Edge_Score2D finished, time cost: 0.745999999999995 s.
#>
#> Start Edge-Topology analysis ...
#> Start analysis on 16 pathways...
#> Warning in Edge_Topology(MGI = input_data$data$Edge_Topology, background = background$Edge, : You are use unparallel computing. It could cause huge amount of time!!!
#>
#> For parallel computing, please specify [ncores] parameter to be larger than 1 (We highly recommend that)!!!
#> Calculate pathway scores and siginificant level on 1 cores...
#> 1 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_1_DN : 0.523
#> 2 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_1_UP : 0.8618
#> 3 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2A_DN : 0.0793
#> 4 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2A_UP : 1
#> 5 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_2B : 0.0425
#> 6 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_3_DN : 0.7068
#> 7 / 16 , LINDGREN_BLADDER_CANCER_CLUSTER_3_UP : 0.0688
#> 8 / 16 , LINDGREN_BLADDER_CANCER_HIGH_RECURRENCE : 0.8988
#> 9 / 16 , LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q : 0.2517
#> 10 / 16 , OSMAN_BLADDER_CANCER_DN : 0.6807
#> 11 / 16 , OSMAN_BLADDER_CANCER_UP : 0.5325
#> 12 / 16 , WU_SILENCED_BY_METHYLATION_IN_BLADDER_CANCER : 0.879
#> 13 / 16 , KEGG_BLADDER_CANCER : 0.8879
#> 14 / 16 , WP_BLADDER_CANCER : 0.7026
#> 15 / 16 , WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER : 0.2177
#> 16 / 16 , HP_BLADDER_NEOPLASM : 0.2336
#> Edge-Topology analysis have finished!
#> - Edge_Topology finished, time cost: 9.943 s.
#>
#> Start Edge-Network analysis ...
#> Build restart list...
#> Size of sparse restart_matrix: 2.58535003662109 MB.
#> Warning in Edge_Network(DEMGI = input_data$data$Edge_Network, pathway_gene = input_data$pathway$TG, : You are use unparallel computing. It could cause huge amount of time!!!
#>
#> For parallel computing, please specify [ncores] parameter to be larger than 1 (We highly recommend that)!!!
#> Build transition matrix...
#> Size of transition_matrix: 8.83169555664062 MB.
#> Execute RWR with 20 iterations on 1 cores...
#> Summarise by pathways...
#> Edge-Network analysis have finished!
#> - Edge_Network finished, time cost: 2.396 s.
#>
#> miREA analysis have finished!...
#> === End time: 2026-10-16 21:32:22 ===
result$valid_methods
#> [1] "TG_ORA" "TG_Score" "MiR_ORA" "MiR_Score"
#> [5] "Edge_ORA" "Edge_Score1D" "Edge_Score2D" "Edge_Topology"
#> [9] "Edge_Network"miREA() returns a miREAResult object, a
lightweight S4 container for the per-method result tables, elapsed
times, and call parameters, with $ access kept for the four
fields above for convenience.
result
#> miREAResult object
#> Methods completed: TG_ORA, TG_Score, MiR_ORA, MiR_Score, Edge_ORA, Edge_Score1D, Edge_Score2D, Edge_Topology, Edge_Network
#> Pathways tested: 16result$result is a named list with one data.frame per
method. For example, the TG_ORA result:
head(result$result$TG_ORA)
#> pathway
#> OSMAN_BLADDER_CANCER_DN OSMAN_BLADDER_CANCER_DN
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
#> KEGG_BLADDER_CANCER KEGG_BLADDER_CANCER
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
#> Description
#> OSMAN_BLADDER_CANCER_DN OSMAN_BLADDER_CANCER_DN
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
#> KEGG_BLADDER_CANCER KEGG_BLADDER_CANCER
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
#> GeneRatio
#> OSMAN_BLADDER_CANCER_DN 124/575
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 95/575
#> KEGG_BLADDER_CANCER 14/575
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 6/575
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 34/575
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 105/575
#> BgRatio
#> OSMAN_BLADDER_CANCER_DN 421/2157
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 325/2157
#> KEGG_BLADDER_CANCER 42/2157
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 16/2157
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 115/2157
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 374/2157
#> RichFactor
#> OSMAN_BLADDER_CANCER_DN 0.2945368
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 0.2923077
#> KEGG_BLADDER_CANCER 0.3333333
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 0.3750000
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 0.2956522
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 0.2807487
#> FoldEnrichment
#> OSMAN_BLADDER_CANCER_DN 1.104897
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 1.096535
#> KEGG_BLADDER_CANCER 1.250435
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 1.406739
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 1.109081
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 1.053174
#> oddsRatio
#> OSMAN_BLADDER_CANCER_DN 1.189575
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 1.163406
#> KEGG_BLADDER_CANCER 1.385027
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 1.657645
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 1.164601
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 1.090445
#> zScore
#> OSMAN_BLADDER_CANCER_DN 1.4460567
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 1.1382020
#> KEGG_BLADDER_CANCER 0.9879141
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 0.9842891
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 0.7246468
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 0.6817292
#> p_value
#> OSMAN_BLADDER_CANCER_DN 0.08379445
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 0.14242841
#> KEGG_BLADDER_CANCER 0.20584038
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 0.23507258
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 0.26579549
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 0.26711766
#> padj
#> OSMAN_BLADDER_CANCER_DN 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 0.7123137
#> KEGG_BLADDER_CANCER 0.7123137
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 0.7123137
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 0.7123137
#> qvalue
#> OSMAN_BLADDER_CANCER_DN 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 0.7123137
#> KEGG_BLADDER_CANCER 0.7123137
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 0.7123137
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 0.7123137
#> geneID
#> OSMAN_BLADDER_CANCER_DN ARIH2/MRPS14/RPS6KA5/EPB41/CYTIP/SKAP1/TUBD1/ZNF852/CSTF3/VAMP1/ZNF398/RAD50/ARFGAP2/UST/MPRIP/HIF1AN/H2AC6/PHF20/SMARCA2/CDK12/MAML2/PML/ELL3/CTDSP2/ZNF763/ARID1A/UBA52/GNAS/GRIPAP1/PPP2CA/ZNF506/MRPS30/PIGL/RPLP2/TRIM38/CELF1/ASXL1/MYO1F/ZNF275/SIAH1/SKI/DIDO1/AHCYL1/NMT2/SSU72/RPL13/ZNF230/DMAC1/NSD3/SENP1/KMT5B/H2BC21/CARD11/ZNF791/ZNF780B/SAMD4B/SERINC3/ZNF493/DIABLO/BACH2/METTL4/ST13/WIPF2/CYB561D1/UBE2B/TMEM63A/TENT4A/LYRM9/WIPI2/CEP57/IDS/RPL15/MRPS25/RPS15/CAMK1D/RPL28/RPL37/ABL2/PRMT2/ZNF544/MARCHF6/ATL2/DPF2/ENOSF1/MLH3/ZNF879/RIPK1/ARHGEF2/GOSR1/RPS11/RHOH/OSBPL2/CLIP1/VWA8/LEPROTL1/TGFBR2/MDM4/FCMR/RABGAP1L/USP36/RPS6/RPS16/RPL13A/ARHGAP45/CHTOP/FLVCR1/STRBP/CELF2/NISCH/CACNA1A/HMGN4/PPM1K/SS18L2/SAFB/GNG7/ASXL2/TTF1/PRUNE1/RASGRF2/ATP8B2/NUDT5/RPL36/KCMF1/MYL12A
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP TM4SF1/XPOT/TRIP13/CTPS1/CLPTM1L/ARRB2/SAE1/NEDD9/CSTF3/PRPS2/BIRC5/KPNA4/NXT2/USP14/NCOA6/UCK2/PPM1D/PLK1/SCML1/ATP6V1C1/TYMS/YWHAQ/LIG1/IMPA2/WDHD1/PANK1/ZWINT/C5orf22/KIF14/OSBP/BRCA1/RBBP4/ACOT2/AKAP12/NEMP1/ZNF148/TWSG1/MELK/TCF19/CENPA/AK2/PSMC4/TK1/CKS1B/CHAF1A/CDC25A/GGCT/GMNN/SMIM29/RPA3/KIF11/RACGAP1/PXMP4/GPATCH11/POP4/SLFN13/TARS1/RBBP7/TXNRD1/PARD6B/SLC25A40/E2F1/SPATA13/DAB2/ZYG11B/OTULIN/SLC39A8/WDR53/RAD1/PAK1IP1/NAA50/NCAPG2/ZNF587/RRP1/FAM200B/EPHX1/NQO2/CENPN/TFRC/H2BC5/BRIX1/ITPR1/LNPK/DEK/MCM8/CDC6/KIF2C/TTC39A/DBF4/NEU1/GCA/MOBP/TSC22D2/RBM38/ASS1
#> KEGG_BLADDER_CANCER RPS6KA5/TP53/FGFR3/HRAS/DAPK3/EGFR/PGF/E2F1/CDKN2A/E2F3/RASSF1/MAP2K2/EGF/E2F2
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER GCC2/MYRIP/SYTL2/SYTL5/MLPH/MADD
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q DOCK8/SPTLC1/FUBP3/ABL1/SMARCA2/NUP214/DUSP22/MTAP/RNF38/RBM18/TOPORS/ASTN2/ANP32B/ANK2/CDK9/TOR1B/PPP6C/TGFBR1/RAD23B/PSMD5/ACO1/CREBL2/ANKH/SLC35D2/TDRP/MSANTD3/CUL5/SETX/VPS13A/KLHL9/RARA/TNFRSF10C/ASS1/APTX
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN ILF3/TRIP13/CTPS1/ARRB2/SAE1/TMEM108/NEDD9/FYN/CACUL1/BIRC5/RASSF5/MAN1C1/ST3GAL2/ABCF2/UCK2/ENO2/IDO1/PLK1/PRRC1/NSD2/PRSS8/SUSD6/WDHD1/ZWINT/CELSR3/VASN/KIF14/BRCA1/KLHL6/AKAP12/ZNF148/CLN3/CD80/MELK/TCF19/SNX5/SSR1/CENPA/PSMA1/PALM2AKAP2/TK1/CKS1B/CDC25A/HILPDA/VPS37C/CDK9/NCKIPSD/KIF11/RACGAP1/H1-0/RELL1/RAI14/CLIC4/SLC44A2/JPT1/MAP3K14/CERS2/E2F1/SPATA13/MACROH2A1/DAB2/CGN/ZNF703/CDC25B/NCAPG2/HMGCS1/RRP1/ABI3BP/YPEL3/HDGF/EPHX1/PEA15/ERBB3/DLGAP4/SBNO2/CENPN/HEYL/TFRC/TIMP2/CDCA7/LCOR/RBM28/POLE/NUDT1/GFOD1/MCM8/CDC6/KIF2C/TTC39A/TJP3/NEU1/SH3BP5/DYRK2/NAV2/SFMBT1/ADAM15/MOBP/GNL3L/APLNR/STAT5B/PRUNE1/EBF1/PITX2/TRIM11/SLC1A3
#> Count
#> OSMAN_BLADDER_CANCER_DN 124
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 95
#> KEGG_BLADDER_CANCER 14
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER 6
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q 34
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 105All per-method results can also be flattened into a single dataframe
(with a method column) via
as.data.frame():
head(as.data.frame(result))
#> method
#> OSMAN_BLADDER_CANCER_DN...1 TG_ORA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 TG_ORA
#> KEGG_BLADDER_CANCER...3 TG_ORA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 TG_ORA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 TG_ORA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 TG_ORA
#> pathway
#> OSMAN_BLADDER_CANCER_DN...1 OSMAN_BLADDER_CANCER_DN
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
#> KEGG_BLADDER_CANCER...3 KEGG_BLADDER_CANCER
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
#> Description
#> OSMAN_BLADDER_CANCER_DN...1 OSMAN_BLADDER_CANCER_DN
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
#> KEGG_BLADDER_CANCER...3 KEGG_BLADDER_CANCER
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
#> GeneRatio
#> OSMAN_BLADDER_CANCER_DN...1 124/575
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 95/575
#> KEGG_BLADDER_CANCER...3 14/575
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 6/575
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 34/575
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 105/575
#> BgRatio
#> OSMAN_BLADDER_CANCER_DN...1 421/2157
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 325/2157
#> KEGG_BLADDER_CANCER...3 42/2157
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 16/2157
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 115/2157
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 374/2157
#> RichFactor
#> OSMAN_BLADDER_CANCER_DN...1 0.2945368
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 0.2923077
#> KEGG_BLADDER_CANCER...3 0.3333333
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 0.3750000
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 0.2956522
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 0.2807487
#> FoldEnrichment
#> OSMAN_BLADDER_CANCER_DN...1 1.104897
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 1.096535
#> KEGG_BLADDER_CANCER...3 1.250435
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 1.406739
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 1.109081
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 1.053174
#> oddsRatio
#> OSMAN_BLADDER_CANCER_DN...1 1.189575
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 1.163406
#> KEGG_BLADDER_CANCER...3 1.385027
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 1.657645
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 1.164601
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 1.090445
#> zScore
#> OSMAN_BLADDER_CANCER_DN...1 1.4460567
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 1.1382020
#> KEGG_BLADDER_CANCER...3 0.9879141
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 0.9842891
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 0.7246468
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 0.6817292
#> p_value
#> OSMAN_BLADDER_CANCER_DN...1 0.08379445
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 0.14242841
#> KEGG_BLADDER_CANCER...3 0.20584038
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 0.23507258
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 0.26579549
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 0.26711766
#> padj
#> OSMAN_BLADDER_CANCER_DN...1 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 0.7123137
#> KEGG_BLADDER_CANCER...3 0.7123137
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 0.7123137
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 0.7123137
#> qvalue
#> OSMAN_BLADDER_CANCER_DN...1 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 0.7123137
#> KEGG_BLADDER_CANCER...3 0.7123137
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 0.7123137
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 0.7123137
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 0.7123137
#> geneID
#> OSMAN_BLADDER_CANCER_DN...1 ARIH2/MRPS14/RPS6KA5/EPB41/CYTIP/SKAP1/TUBD1/ZNF852/CSTF3/VAMP1/ZNF398/RAD50/ARFGAP2/UST/MPRIP/HIF1AN/H2AC6/PHF20/SMARCA2/CDK12/MAML2/PML/ELL3/CTDSP2/ZNF763/ARID1A/UBA52/GNAS/GRIPAP1/PPP2CA/ZNF506/MRPS30/PIGL/RPLP2/TRIM38/CELF1/ASXL1/MYO1F/ZNF275/SIAH1/SKI/DIDO1/AHCYL1/NMT2/SSU72/RPL13/ZNF230/DMAC1/NSD3/SENP1/KMT5B/H2BC21/CARD11/ZNF791/ZNF780B/SAMD4B/SERINC3/ZNF493/DIABLO/BACH2/METTL4/ST13/WIPF2/CYB561D1/UBE2B/TMEM63A/TENT4A/LYRM9/WIPI2/CEP57/IDS/RPL15/MRPS25/RPS15/CAMK1D/RPL28/RPL37/ABL2/PRMT2/ZNF544/MARCHF6/ATL2/DPF2/ENOSF1/MLH3/ZNF879/RIPK1/ARHGEF2/GOSR1/RPS11/RHOH/OSBPL2/CLIP1/VWA8/LEPROTL1/TGFBR2/MDM4/FCMR/RABGAP1L/USP36/RPS6/RPS16/RPL13A/ARHGAP45/CHTOP/FLVCR1/STRBP/CELF2/NISCH/CACNA1A/HMGN4/PPM1K/SS18L2/SAFB/GNG7/ASXL2/TTF1/PRUNE1/RASGRF2/ATP8B2/NUDT5/RPL36/KCMF1/MYL12A
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 TM4SF1/XPOT/TRIP13/CTPS1/CLPTM1L/ARRB2/SAE1/NEDD9/CSTF3/PRPS2/BIRC5/KPNA4/NXT2/USP14/NCOA6/UCK2/PPM1D/PLK1/SCML1/ATP6V1C1/TYMS/YWHAQ/LIG1/IMPA2/WDHD1/PANK1/ZWINT/C5orf22/KIF14/OSBP/BRCA1/RBBP4/ACOT2/AKAP12/NEMP1/ZNF148/TWSG1/MELK/TCF19/CENPA/AK2/PSMC4/TK1/CKS1B/CHAF1A/CDC25A/GGCT/GMNN/SMIM29/RPA3/KIF11/RACGAP1/PXMP4/GPATCH11/POP4/SLFN13/TARS1/RBBP7/TXNRD1/PARD6B/SLC25A40/E2F1/SPATA13/DAB2/ZYG11B/OTULIN/SLC39A8/WDR53/RAD1/PAK1IP1/NAA50/NCAPG2/ZNF587/RRP1/FAM200B/EPHX1/NQO2/CENPN/TFRC/H2BC5/BRIX1/ITPR1/LNPK/DEK/MCM8/CDC6/KIF2C/TTC39A/DBF4/NEU1/GCA/MOBP/TSC22D2/RBM38/ASS1
#> KEGG_BLADDER_CANCER...3 RPS6KA5/TP53/FGFR3/HRAS/DAPK3/EGFR/PGF/E2F1/CDKN2A/E2F3/RASSF1/MAP2K2/EGF/E2F2
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 GCC2/MYRIP/SYTL2/SYTL5/MLPH/MADD
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 DOCK8/SPTLC1/FUBP3/ABL1/SMARCA2/NUP214/DUSP22/MTAP/RNF38/RBM18/TOPORS/ASTN2/ANP32B/ANK2/CDK9/TOR1B/PPP6C/TGFBR1/RAD23B/PSMD5/ACO1/CREBL2/ANKH/SLC35D2/TDRP/MSANTD3/CUL5/SETX/VPS13A/KLHL9/RARA/TNFRSF10C/ASS1/APTX
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 ILF3/TRIP13/CTPS1/ARRB2/SAE1/TMEM108/NEDD9/FYN/CACUL1/BIRC5/RASSF5/MAN1C1/ST3GAL2/ABCF2/UCK2/ENO2/IDO1/PLK1/PRRC1/NSD2/PRSS8/SUSD6/WDHD1/ZWINT/CELSR3/VASN/KIF14/BRCA1/KLHL6/AKAP12/ZNF148/CLN3/CD80/MELK/TCF19/SNX5/SSR1/CENPA/PSMA1/PALM2AKAP2/TK1/CKS1B/CDC25A/HILPDA/VPS37C/CDK9/NCKIPSD/KIF11/RACGAP1/H1-0/RELL1/RAI14/CLIC4/SLC44A2/JPT1/MAP3K14/CERS2/E2F1/SPATA13/MACROH2A1/DAB2/CGN/ZNF703/CDC25B/NCAPG2/HMGCS1/RRP1/ABI3BP/YPEL3/HDGF/EPHX1/PEA15/ERBB3/DLGAP4/SBNO2/CENPN/HEYL/TFRC/TIMP2/CDCA7/LCOR/RBM28/POLE/NUDT1/GFOD1/MCM8/CDC6/KIF2C/TTC39A/TJP3/NEU1/SH3BP5/DYRK2/NAV2/SFMBT1/ADAM15/MOBP/GNL3L/APLNR/STAT5B/PRUNE1/EBF1/PITX2/TRIM11/SLC1A3
#> Count
#> OSMAN_BLADDER_CANCER_DN...1 124
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 95
#> KEGG_BLADDER_CANCER...3 14
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 6
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 34
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 105
#> setSize
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> enrichmentScore
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> NES rank
#> OSMAN_BLADDER_CANCER_DN...1 NA NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA NA
#> KEGG_BLADDER_CANCER...3 NA NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA NA
#> leading_edge
#> OSMAN_BLADDER_CANCER_DN...1 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 <NA>
#> KEGG_BLADDER_CANCER...3 <NA>
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 <NA>
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 <NA>
#> core_enrichment
#> OSMAN_BLADDER_CANCER_DN...1 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 <NA>
#> KEGG_BLADDER_CANCER...3 <NA>
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 <NA>
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 <NA>
#> log2err
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> MGISize
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> score
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> rand_score_dist
#> OSMAN_BLADDER_CANCER_DN...1 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 <NA>
#> KEGG_BLADDER_CANCER...3 <NA>
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 <NA>
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 <NA>
#> nMoreExtreme
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> TopMGI
#> OSMAN_BLADDER_CANCER_DN...1 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 <NA>
#> KEGG_BLADDER_CANCER...3 <NA>
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 <NA>
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 <NA>
#> n_MGI nPerm
#> OSMAN_BLADDER_CANCER_DN...1 NA NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA NA
#> KEGG_BLADDER_CANCER...3 NA NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA NA
#> z
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NA
#> contribute_edge
#> OSMAN_BLADDER_CANCER_DN...1 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 <NA>
#> KEGG_BLADDER_CANCER...3 <NA>
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 <NA>
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 <NA>
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 <NA>
#> n_contribute_edge
#> OSMAN_BLADDER_CANCER_DN...1 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_3_UP...2 NA
#> KEGG_BLADDER_CANCER...3 NA
#> WP_DEREGULATION_OF_RAB_AND_RAB_EFFECTOR_GENES_IN_BLADDER_CANCER...4 NA
#> LINDGREN_BLADDER_CANCER_WITH_LOH_IN_CHR9Q...5 NA
#> LINDGREN_BLADDER_CANCER_CLUSTER_1_DN...6 NAEach method only reports pathways with pvalue and padj below
pvalueCutoff. We use pvalueCutoff = 1
throughout this vignette so every pathway is returned. The exception is
the ORA methods (TG_ORA, MiR_ORA,
Edge_ORA). These methods only report pathways with at least
one overlapping differentially expressed gene, miRNA, or edge, so some
pathways can still be missing even at pvalueCutoff = 1. For
example, Edge_ORA returns 9 of 16 pathways here.
Step 3: Visualize results
plotSummary() compares results across all requested
methods: positive enrichment rates, a PCA of per-method pathway
rankings, pairwise overlap between methods, and a p-value density
plot.
#> Visualization finished!
#> ***If you want to save them to pdf files directly, please specify the [plot_path] parameter!***
#> $summary
#>
#> $pvalue
plotHeatmap() and plotHeatmapSankey()
visualize a single edge-based method’s result as a miRNA-by-gene heatmap
(optionally combined with a Sankey diagram showing the
miRNA-gene-pathway flow). Only the five edge-based methods are
supported, one at a time.
plotHeatmap(method = "Edge_Score1D", result = result, input_data = input_data,
penrichCutoff = 0.05, n_pathway = 5, n_mir_heatmap = 10)
#> Visualization finished!
#> ***If you want to save them to pdf files, please specify the [plot_path] parameter!***
#> [[1]]
#>
#> [[2]]
#> [[2]][[1]]
#> A single legend
#>
#> [[2]][[2]]
#> A single legend
#>
#> [[2]][[3]]
#> A single legend
#>
#> [[2]][[4]]
#> A single legend
#>
#> [[2]][[5]]
#> A single legend
#>
#> [[2]][[6]]
#> A single legendplotHeatmapSankey(method = "Edge_Score1D", result = result, input_data = input_data,
penrichCutoff = 0.05, n_pathway = 5, n_mir_heatmap = 10,
n_mir_sankey = 5)
#> Visualization finished!
#> ***If you want to save them to pdf files, please specify the [plot_path] parameter!***
#> [[1]]
#>
#> [[2]]
#> [[2]][[1]]
#> A single legend
#>
#> [[2]][[2]]
#> A single legend
#>
#> [[2]][[3]]
#> A single legend
#>
#> [[2]][[4]]
#> A single legend
#>
#> [[2]][[5]]
#> A single legend
#>
#> [[2]][[6]]
#> A single legendChoosing methods for your own data
miREA supports 4 node-based and 5 edge-based methods. You do not need
to run all nine. Select the method(s) most appropriate for your dataset
and research question. See the method performance comparisons in the
miREA manuscript (miREA’s
companion bioRxiv
preprint) for guidance. Edge_Topology in particular can
be computationally expensive on large, real-sized pathway/background
data. Consider excluding it or increasing ncores for large
analyses.
Session info
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 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-r0.3.32.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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] GSEABase_1.75.0 graph_1.91.0
#> [3] annotate_1.91.0 XML_3.99-0.25
#> [5] AnnotationDbi_1.75.2 SummarizedExperiment_1.43.0
#> [7] Biobase_2.73.2 GenomicRanges_1.65.4
#> [9] Seqinfo_1.3.2 IRanges_2.47.5
#> [11] S4Vectors_0.51.10 BiocGenerics_0.59.12
#> [13] generics_0.1.4 MatrixGenerics_1.25.0
#> [15] matrixStats_1.5.0 miREA_0.99.2
#> [17] BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3 jsonlite_2.0.0
#> [4] shape_1.4.6.1 tidydr_0.0.7 magrittr_2.0.5
#> [7] ggtangle_0.1.4 farver_2.1.2 rmarkdown_2.32
#> [10] GlobalOptions_0.1.4 fs_2.1.0 vctrs_0.7.3
#> [13] memoise_2.0.1 ggtree_4.3.0 htmltools_0.5.9
#> [16] S4Arrays_1.13.2 BiocBaseUtils_1.15.1 SparseArray_1.13.4
#> [19] gridGraphics_0.5-1 sass_0.4.10 bslib_0.12.0
#> [22] htmlwidgets_1.6.4 plyr_1.8.9 httr2_1.3.0
#> [25] cachem_1.1.0 buildtools_1.0.0 igraph_2.3.4
#> [28] lifecycle_1.0.5 iterators_1.0.14 pkgconfig_2.0.3
#> [31] gson_0.2.2 Matrix_1.7-6 R6_2.6.1
#> [34] fastmap_1.2.0 clue_0.3-68 digest_0.6.39
#> [37] aplot_0.3.2 enrichplot_1.99.8 colorspace_2.1-4
#> [40] ggnewscale_0.5.2 patchwork_1.3.2 aisdk_1.4.12
#> [43] ps_1.9.3 RSQLite_3.53.3 labeling_0.4.3
#> [46] polyclip_1.10-7 httr_1.4.9 abind_1.4-8
#> [49] compiler_4.6.1 withr_3.0.3 bit64_4.8.6
#> [52] fontquiver_0.2.1 doParallel_1.0.17 S7_0.2.2
#> [55] DBI_1.3.0 ggforce_0.5.0 MASS_7.3-66
#> [58] rappdirs_0.3.4 DelayedArray_0.39.8 rjson_0.2.23
#> [61] tools_4.6.1 otel_0.2.0 scatterpie_0.2.6
#> [64] ape_5.8-1 glue_1.8.1 callr_3.8.0
#> [67] nlme_3.1-171 GOSemSim_2.39.4 grid_4.6.1
#> [70] cluster_2.1.8.3 reshape2_1.4.5 gtable_0.3.6
#> [73] tidyr_1.3.2 data.table_1.18.6.1 XVector_0.53.0
#> [76] ggrepel_0.9.8 foreach_1.5.2 pillar_1.11.1
#> [79] stringr_1.6.0 yulab.utils_0.2.5 splines_4.6.1
#> [82] circlize_0.4.18 tweenr_2.0.3 dplyr_1.2.1
#> [85] treeio_1.37.1 lattice_0.23-1 bit_4.6.0
#> [88] tidyselect_1.2.1 fontLiberation_0.1.0 GO.db_3.23.1
#> [91] ComplexHeatmap_2.29.0 maketools_1.3.2 Biostrings_2.81.9
#> [94] knitr_1.52 fontBitstreamVera_0.1.1 RhpcBLASctl_0.23-42
#> [97] xfun_0.61 stringi_1.8.9 lazyeval_0.2.3
#> [100] ggfun_0.2.2 yaml_2.3.12 evaluate_1.0.5
#> [103] codetools_0.2-20 qvalue_2.45.0 gdtools_0.5.2
#> [106] tibble_3.3.1 BiocManager_1.30.27 ggplotify_0.1.4
#> [109] cli_3.6.6 systemfonts_1.3.2 xtable_1.8-8
#> [112] processx_3.9.0 jquerylib_0.1.4 Rcpp_1.1.2
#> [115] png_0.1-9 parallel_4.6.1 ggplot2_4.0.3
#> [118] blob_1.3.0 clusterProfiler_4.21.3 ggalluvial_0.12.6
#> [121] DOSE_4.7.3 tidytree_0.4.8 ggiraph_0.9.6
#> [124] enrichit_0.2.6 scales_1.4.0 purrr_1.2.2
#> [127] crayon_1.5.3 GetoptLong_1.1.1 rlang_1.3.0
#> [130] KEGGREST_1.53.6