MicroRNAs (miRNAs) play key roles in many biological processes including cancers [1-5]. Thus, uncovering miRNA functions and regulatory mechanisms is important for gene diagnosis and therapy.
Previous studies [6-9] have shown that a pool of coding and non-coding RNAs that shares common miRNA biding sites competes with each other, thus alter miRNA activity. The corresponding regulatory mechanism is named competing endogenous RNA (ceRNA) hypothesis [10]. These RNAs are called ceRNAs or miRNA sponges or miRNA decoys, and include long non-coding RNAs (lncRNAs), pseudogenes, circular RNAs (circRNAs) and messenger RNAs (mRNAs), etc. To study the module-level properties of miRNA sponges, it is necessary to identify miRNA sponge modules. The miRNA sponge modules will help to reveal the biological mechanism in cancer.
To speed up the research of miRNA sponge modules, we develop an
R/Bioconductor package miRSM to infer miRNA sponge modules.
Unlike the existing R/Bioconductor packages (miRspongeR
and SPONGE),
miRSM focuses on identifying miRNA sponge modules by
integrating expression data and miRNA-target binding information instead
of miRNA sponge interaction networks. In addition to identifying miRNA
sponge modules in the form of external competition (e.g. a group of
lncRNAs compete with a group of mRNAs), miRSM can also
infer miRNA sponge modules in the form of internal competition (e.g. a
group of mRNAs compete with another group of mRNAs). Moreover,
miRSM can infer miRNA sponge modules at both single-sample
and multi-sample levels.
Given matched ceRNA and mRNA expression data or single gene
expression data, miRSM infers gene modules by using several
methods from 21 packages, including WGCNA,
GFA, igraph, ProNet,
NMF, stats, flashClust,
dbscan, subspace, mclust,
SOMbrero, ppclust, biclust,
runibic, iBBiG, fabia,
BicARE, isa2, s4vd,
BiBitR and rqubic. We assemble these methods
into 7 functions: module_WGCNA, module_GFA,
module_igraph, module_ProNet, module_NMF,
module_clust and module_biclust.
The BRCA sample data includes matched miRNA, lncRNA, mRNA expression data, putative miRNA-target binding information and BRCA-related genes (lncRNAs and mRNAs).
By using WGCNA method [11], miRSM identifies
co-expressed gene modules from matched ceRNA and mRNA expression data or
single gene expression data.
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0755 0.331 -0.10700 54.100 65.50000 83.70
## 2 2 0.0476 -0.260 0.09470 28.000 33.30000 56.60
## 3 3 0.1720 -0.315 0.40700 17.100 18.20000 41.80
## 4 4 0.2940 -0.400 0.26500 11.400 10.30000 32.80
## 5 5 0.5200 -0.518 0.41100 7.970 5.99000 26.50
## 6 6 0.7650 -0.599 0.72800 5.770 3.55000 21.90
## 7 7 0.8470 -0.644 0.87100 4.290 2.14000 18.30
## 8 8 0.7110 -0.740 0.70500 3.260 1.31000 15.40
## 9 9 0.1560 -1.720 0.07550 2.520 0.81700 13.10
## 10 10 0.1690 -1.850 0.08770 1.980 0.52000 11.30
## 11 12 0.8620 -0.954 0.89900 1.260 0.22000 8.43
## 12 14 0.8510 -1.030 0.85100 0.844 0.09870 6.44
## 13 16 0.1800 -1.910 0.00065 0.584 0.04630 4.99
## 14 18 0.1850 -1.870 0.01020 0.417 0.02160 3.91
## 15 20 0.9260 -1.030 0.96000 0.305 0.00998 3.11
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.## GeneSetCollection
## names: Module 1 (1 total)
## unique identifiers: A2M-AS1, EMX2OS, ..., GRASP (36 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)The gene modules are identified by using GFA method [12, 13] from matched ceRNA and mRNA expression data or single gene expression data.
modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)],
mRExp[seq_len(20), seq_len(15)],
iter.max = 3000)## [1] "Running GFA with 2 data sources paired in one mode."
## [1] "Initializing GFA with 10 components."
## [1] "Learning: 100/3000 - K=6 - 2024-11-20 06:28:56.035906"
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## [1] "Learning: 3000/3000 - K=1 - 2024-11-20 06:28:56.905543"
## [1] "Starting convergence check"
## [1] "Convergence diagnostic: 0.06"
## [1] "Values significantly greater than 0.05 imply a non-converged model."## GeneSetCollection
## names: (0 total)
## unique identifiers: (0 total)
## types in collection:
## geneIdType: (0 total)
## collectionType: (0 total)By using igraph package [14], miRSM infers
gene modules from matched ceRNA and mRNA expression data or single gene
expression data. In the igraph package, users can select
“betweenness”, “greedy”, “infomap”, “prop”, “eigen”, “louvain” and
“walktrap” methods for gene module identification. The default method is
“greedy”.
## GeneSetCollection
## names: Module 1, Module 2 (2 total)
## unique identifiers: A2M-AS1, ABCA11P, ..., E2F8 (19 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)In the ProNet package, users can select FN [15], MCL
[16], LINKCOMM [17] and MCODE [18] for gene module identification from
matched ceRNA and mRNA expression data or single gene expression data.
The default method is MCL.
## GeneSetCollection
## names: Module 1, Module 2 (2 total)
## unique identifiers: A2M-AS1, ACVR2B-AS1, ..., E2F7 (12 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)By using NMF package [20], users infer gene modules from
matched ceRNA and mRNA expression data or single gene expression data.
In the NMF package, we can select “brunet”, “Frobenius”,
“KL”, “lee”, “nsNMF”, “offset”, “siNMF”, “snmf/l” and “snmf/r” methods
for gene module identification. The default method is “brunet”.
# Reimport NMF package to avoid conflicts with DelayedArray package
library(NMF)
modulegenes_NMF <- module_NMF(ceRExp[, seq_len(10)],
mRExp[, seq_len(10)])
modulegenes_NMF## GeneSetCollection
## names: Module 1 (1 total)
## unique identifiers: A2M-AS1, ABCA11P, ..., EBF1 (6 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)miRSM Identifies gene modules from matched ceRNA and
mRNA expression data or single gene expression data using a series of
clustering packages, including stats [21],
flashClust [22], dbscan [23],
subspace [24], mclust [25],
SOMbrero [26] and ppclust [27]. The clustering
methods include “kmeans”, “hclust”, “dbscan”, “clique”, “gmm”, “som” and
“fcm”. The default method is “kmeans”.
## GeneSetCollection
## names: Module 1, Module 2, Module 3, Module 4 (4 total)
## unique identifiers: A2M-AS1, ACVR2B-AS1, ..., FEN1 (38 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)miRSM Identifies gene modules from matched ceRNA and
mRNA expression data or single gene expression data using a series of
biclustering packages, including biclust [28],
iBBiG [29], fabia [30], BicARE
[31], isa2 [32], s4vd [33],
BiBitR [34] and rqubic [35]. The biclustering
methods include “BCBimax”, “BCCC”, “BCPlaid”, “BCQuest”, “BCSpectral”,
“BCXmotifs”, “iBBiG”, “fabia”, “fabiap”, “fabias”, “mfsc”, “nmfdiv”,
“nmfeu”, “nmfsc”, “FLOC”, “isa”, “BCs4vd”, “BCssvd”, “bibit” and
“quBicluster”. The default method is “fabia”.
## Cycle: 0Cycle: 20Cycle: 40Cycle: 60Cycle: 80Cycle: 100Cycle: 120Cycle: 140Cycle: 160Cycle: 180Cycle: 200Cycle: 220Cycle: 240Cycle: 260Cycle: 280Cycle: 300Cycle: 320Cycle: 340Cycle: 360Cycle: 380Cycle: 400Cycle: 420Cycle: 440Cycle: 460Cycle: 480Cycle: 500## GeneSetCollection
## names: Module 1 (1 total)
## unique identifiers: FHL1, FIGF, ..., ECE2 (26 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)The identified gene modules are regarded as candidate miRNA sponge
modules. Based on the candidate miRNA sponge modules, miRSM
uses the sensitivity canonical correlation (SCC), sensitivity distance
correlation (SDC), sensitivity RV coefficient (SRVC), sensitivity
similarity index (SSI), sensitivity generalized coefficient of
determination (SGCD) and sensitivity Coxhead’s or Rozeboom’s coefficient
(SCRC) methods to identify miRNA sponge modules. In addition, the sponge
module (SM) method proposed in [36] is also added to predict miRNA
sponge modules.
modulegenes_igraph <- module_igraph(ceRExp[, seq_len(10)],
mRExp[, seq_len(10)])
# Identify miRNA sponge modules using sensitivity RV coefficient (SRVC)
miRSM_igraph_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget,
modulegenes_igraph,
num_shared_miRNAs = 3, pvalue.cutoff = 0.05,
method = "SRVC", MC.cutoff = 0.8,
SMC.cutoff = 0.01, RV_method = "RV")
miRSM_igraph_SRVC## [1] "No miRNA sponge modules identified!"miRSM uses statistical perturbation strategy to infer
sample-specific miRNA sponge modules. By using the statistical
perturbation strategy, miRSM identifies differential miRNA
sponge modules between two cases (all samples and all samples except
sample k).
nsamples <- 3
modulegenes_all <- module_igraph(ceRExp[, 151:300], mRExp[, 151:300])
modulegenes_exceptk <- lapply(seq(nsamples), function(i)
module_WGCNA(ceRExp[-i, seq(150)],
mRExp[-i, seq(150)]))## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0965 0.328 0.1180 98.900 119.0000 155.00
## 2 2 0.0678 -0.225 0.3120 50.900 60.7000 105.00
## 3 3 0.2200 -0.355 0.2670 31.000 33.3000 78.10
## 4 4 0.3780 -0.461 0.2450 20.500 19.0000 60.70
## 5 5 0.5620 -0.557 0.4370 14.200 11.1000 48.30
## 6 6 0.7680 -0.620 0.7580 10.200 6.6600 39.10
## 7 7 0.8290 -0.635 0.8600 7.550 4.1500 32.00
## 8 8 0.7280 -0.746 0.7440 5.710 2.5800 26.50
## 9 9 0.1570 -1.810 0.0595 4.390 1.6100 22.10
## 10 10 0.1630 -1.800 0.0635 3.440 1.0200 18.60
## 11 12 0.7620 -0.929 0.7250 2.210 0.4260 13.70
## 12 14 0.9630 -0.894 0.9590 1.500 0.1980 10.40
## 13 16 0.9500 -0.911 0.9400 1.060 0.0905 8.04
## 14 18 0.9540 -0.886 0.9590 0.778 0.0420 6.32
## 15 20 0.9290 -0.924 0.9260 0.590 0.0205 5.43
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0915 0.319 0.0654 98.600 118.0000 154.00
## 2 2 0.0803 -0.240 0.2250 50.600 60.3000 104.00
## 3 3 0.2310 -0.355 0.2720 30.700 33.0000 77.80
## 4 4 0.3820 -0.468 0.2380 20.300 19.0000 60.40
## 5 5 0.5450 -0.570 0.4170 14.100 11.0000 48.10
## 6 6 0.7630 -0.617 0.7560 10.100 6.5200 38.90
## 7 7 0.8230 -0.661 0.8530 7.450 3.9300 31.90
## 8 8 0.7330 -0.752 0.7510 5.620 2.4300 26.40
## 9 9 0.1580 -1.790 0.0651 4.330 1.5400 22.00
## 10 10 0.1660 -1.860 0.0649 3.390 0.9870 18.60
## 11 12 0.1750 -1.890 0.0725 2.180 0.4010 13.60
## 12 14 0.9710 -0.897 0.9700 1.480 0.1850 10.30
## 13 16 0.9780 -0.909 0.9740 1.050 0.0868 7.96
## 14 18 0.9550 -0.888 0.9650 0.769 0.0410 6.26
## 15 20 0.9520 -0.915 0.9540 0.583 0.0196 5.37
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0797 0.302 0.0932 99.100 119.0000 155.00
## 2 2 0.0610 -0.231 0.2280 51.000 60.9000 105.00
## 3 3 0.2110 -0.356 0.2630 31.100 33.3000 78.40
## 4 4 0.4050 -0.469 0.2650 20.600 18.9000 61.00
## 5 5 0.5180 -0.563 0.3810 14.300 11.1000 48.60
## 6 6 0.7660 -0.620 0.7560 10.300 6.6400 39.40
## 7 7 0.8370 -0.636 0.8740 7.600 4.0500 32.30
## 8 8 0.7960 -0.741 0.8130 5.740 2.5000 26.70
## 9 9 0.6690 -0.842 0.6500 4.430 1.5700 22.40
## 10 10 0.1640 -1.840 0.0645 3.470 0.9900 18.80
## 11 12 0.1750 -1.900 0.0704 2.230 0.4150 13.90
## 12 14 0.9230 -0.920 0.9100 1.510 0.1920 10.50
## 13 16 0.9620 -0.912 0.9510 1.070 0.0921 8.14
## 14 18 0.9450 -0.893 0.9470 0.788 0.0436 6.41
## 15 20 0.9300 -0.910 0.9330 0.597 0.0211 5.39
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.miRSM_SRVC_all <- miRSM(miRExp, ceRExp[, 151:300], mRExp[, 151:300],
miRTarget, modulegenes_all,
method = "SRVC", SMC.cutoff = 0.01,
RV_method = "RV")
miRSM_SRVC_exceptk <- lapply(seq(nsamples), function(i) miRSM(miRExp[-i, ],
ceRExp[-i, seq(150)], mRExp[-i, seq(150)],
miRTarget, modulegenes_exceptk[[i]],
method = "SRVC",
SMC.cutoff = 0.01, RV_method = "RV"))
Modulegenes_all <- miRSM_SRVC_all[[2]]
Modulegenes_exceptk <- lapply(seq(nsamples), function(i) miRSM_SRVC_exceptk[[i]][[2]])
Modules_SS <- miRSM_SS(Modulegenes_all, Modulegenes_exceptk)
Modules_SS## $`Sample 1`
## $`Sample 1`$`miRSM 1`
## $`Sample 1`$`miRSM 1`$ceRNA
## [1] "LINC00998" "LINC01006" "LINC01091" "LINC01128" "LINC01140"
## [6] "LINC-PINT" "LOH12CR2" "MALAT1" "MBL1P" "MBNL1-AS1"
## [11] "MEG3" "MIR22HG" "MIR99AHG" "MIRLET7BHG" "NBR2"
## [16] "NCF1B" "NEAT1" "NKAPP1" "NPY6R" "NR2F1-AS1"
## [21] "NUDT16P1" "PAX8-AS1" "PAXIP1-AS2" "PLEKHA8P1" "PPP1R3E"
## [26] "PSMG3-AS1" "PTENP1" "RAMP2-AS1" "RAPGEF4-AS1" "RP9P"
## [31] "RPL31P11" "SCARNA7" "SCARNA9" "SFTA1P" "SLC25A5-AS1"
## [36] "SMAD5-AS1" "SMIM8" "SNAI3-AS1" "ST7" "ST7-AS1"
## [41] "ST7-OT4" "STAG3L4" "TDGF1P3" "TPTEP1" "WDFY3-AS2"
## [46] "WWC2-AS2" "ZFHX4-AS1" "ZNF192P1" "ZNF280D"
##
## $`Sample 1`$`miRSM 1`$mRNA
## [1] "KL" "KLB" "KLF11" "KLF15" "KLF4"
## [6] "KLF6" "KLF8" "KLF9" "KLHDC1" "KLHL3"
## [11] "KLHL31" "LARP6" "LCN10" "LDB2" "LEPR"
## [16] "LETMD1" "LGR4" "LHFP" "LHX6" "LIFR"
## [21] "LIMS2" "LMO2" "LONRF1" "LPL" "LRCH1"
## [26] "LRCH2" "LRRC2" "LRRC34" "LRRN3" "LYVE1"
## [31] "MAOA" "MAP1LC3C" "MAP3K8" "MAP7D3" "MASP1"
## [36] "MSRB3" "MTMR10" "MTURN" "MYCT1" "MYL9"
## [41] "MYOC" "MYOCD" "NAALAD2" "NAT8L" "NATD1"
## [46] "NDEL1" "NDN" "NECAB1" "NFIA" "NLGN1"
## [51] "NLRP1" "NMNAT2" "NMT2" "NMUR1" "NNAT"
## [56] "NOTCH4" "NPR1" "NR3C1" "NRN1" "OGN"
## [61] "OSBPL1A" "P2RX6" "PALM2-AKAP2" "PARK2" "RAPGEF2"
## [66] "RB1" "RBMS2" "RBMS3" "REV3L" "RGL1"
##
##
## $`Sample 1`$`miRSM 2`
## $`Sample 1`$`miRSM 2`$ceRNA
## [1] "A2M-AS1" "EMX2OS" "GGTA1P" "LINC00961"
##
## $`Sample 1`$`miRSM 2`$mRNA
## [1] "C10orf10" "C10orf54" "C14orf180" "C17orf51" "EBF1" "EBF3"
## [7] "FBLN5" "FERMT2" "FGF2" "FGF9" "FHL1" "FHL5"
## [13] "FIGF" "FIGN" "FLRT2" "FOXN3" "FOXO1" "FREM1"
## [19] "FRMD1" "FRMD4A" "FZD4" "GABARAPL1" "GALNT15" "GCOM1"
## [25] "GDF10" "GHR" "GID4" "GIMAP6" "GIMAP8" "GIPC2"
## [31] "GLYAT" "GNAI1" "GNAL" "GPAM" "GPD1" "GPIHBP1"
## [37] "GPLD1" "GPR146" "GPR17" "GPX3" "GRASP" "GYG2"
## [43] "GYPC" "HIF3A" "HN1L" "HSPB6" "HSPB7" "HYAL1"
## [49] "IGF1" "IGF2" "IGSF10" "IL33" "INMT" "IQSEC3"
## [55] "ITIH5" "ITM2A" "KANK1" "KCNAB1" "KCNB1" "KCNIP2"
## [61] "KCTD12" "KIAA0355" "KIAA0408"
##
##
##
## $`Sample 2`
## $`Sample 2`$`miRSM 1`
## $`Sample 2`$`miRSM 1`$ceRNA
## [1] "LINC00998" "LINC01006" "LINC01091" "LINC01128" "LINC01140"
## [6] "LINC-PINT" "LOH12CR2" "MALAT1" "MBL1P" "MBNL1-AS1"
## [11] "MEG3" "MIR22HG" "MIR99AHG" "MIRLET7BHG" "NBR2"
## [16] "NCF1B" "NEAT1" "NKAPP1" "NPY6R" "NR2F1-AS1"
## [21] "NUDT16P1" "PAX8-AS1" "PAXIP1-AS2" "PLEKHA8P1" "PPP1R3E"
## [26] "PSMG3-AS1" "PTENP1" "RAMP2-AS1" "RAPGEF4-AS1" "RP9P"
## [31] "RPL31P11" "SCARNA7" "SCARNA9" "SFTA1P" "SLC25A5-AS1"
## [36] "SMAD5-AS1" "SMIM8" "SNAI3-AS1" "ST7" "ST7-AS1"
## [41] "ST7-OT4" "STAG3L4" "TDGF1P3" "TPTEP1" "WDFY3-AS2"
## [46] "WWC2-AS2" "ZFHX4-AS1" "ZNF192P1" "ZNF280D"
##
## $`Sample 2`$`miRSM 1`$mRNA
## [1] "KL" "KLB" "KLF11" "KLF15" "KLF4"
## [6] "KLF6" "KLF8" "KLF9" "KLHDC1" "KLHL3"
## [11] "KLHL31" "LARP6" "LCN10" "LDB2" "LEPR"
## [16] "LETMD1" "LGR4" "LHFP" "LHX6" "LIFR"
## [21] "LIMS2" "LMO2" "LONRF1" "LPL" "LRCH1"
## [26] "LRCH2" "LRRC2" "LRRC34" "LRRN3" "LYVE1"
## [31] "MAOA" "MAP1LC3C" "MAP3K8" "MAP7D3" "MASP1"
## [36] "MSRB3" "MTMR10" "MTURN" "MYCT1" "MYL9"
## [41] "MYOC" "MYOCD" "NAALAD2" "NAT8L" "NATD1"
## [46] "NDEL1" "NDN" "NECAB1" "NFIA" "NLGN1"
## [51] "NLRP1" "NMNAT2" "NMT2" "NMUR1" "NNAT"
## [56] "NOTCH4" "NPR1" "NR3C1" "NRN1" "OGN"
## [61] "OSBPL1A" "P2RX6" "PALM2-AKAP2" "PARK2" "RAPGEF2"
## [66] "RB1" "RBMS2" "RBMS3" "REV3L" "RGL1"
##
##
## $`Sample 2`$`miRSM 2`
## $`Sample 2`$`miRSM 2`$ceRNA
## [1] "A2M-AS1" "EMX2OS" "GGTA1P" "LINC00961"
##
## $`Sample 2`$`miRSM 2`$mRNA
## [1] "C10orf10" "C10orf54" "C14orf180" "C17orf51" "EBF1" "EBF3"
## [7] "FBLN5" "FBXO31" "FERMT2" "FGF2" "FGF9" "FHL1"
## [13] "FHL5" "FIGF" "FIGN" "FLRT2" "FOXN3" "FOXO1"
## [19] "FREM1" "FRMD1" "FRMD4A" "FZD4" "GABARAPL1" "GALNT15"
## [25] "GCOM1" "GDF10" "GDF5" "GHR" "GID4" "GIMAP6"
## [31] "GIMAP8" "GIPC2" "GLYAT" "GNAI1" "GNAL" "GPAM"
## [37] "GPD1" "GPIHBP1" "GPLD1" "GPR146" "GPR17" "GPX3"
## [43] "GRASP" "GYG2" "GYPC" "HIF3A" "HN1L" "HSPB6"
## [49] "HSPB7" "HYAL1" "IGF1" "IGF2" "IGSF10" "IL33"
## [55] "INMT" "IQSEC3" "ITIH5" "ITM2A" "KANK1" "KAT2B"
## [61] "KCNAB1" "KCNB1" "KCNIP2" "KCTD12" "KIAA0355" "KIAA0408"
## [67] "KIF26A"
##
##
##
## $`Sample 3`
## $`Sample 3`$`miRSM 1`
## $`Sample 3`$`miRSM 1`$ceRNA
## [1] "LINC00998" "LINC01006" "LINC01091" "LINC01128" "LINC01140"
## [6] "LINC-PINT" "LOH12CR2" "MALAT1" "MBL1P" "MBNL1-AS1"
## [11] "MEG3" "MIR22HG" "MIR99AHG" "MIRLET7BHG" "NBR2"
## [16] "NCF1B" "NEAT1" "NKAPP1" "NPY6R" "NR2F1-AS1"
## [21] "NUDT16P1" "PAX8-AS1" "PAXIP1-AS2" "PLEKHA8P1" "PPP1R3E"
## [26] "PSMG3-AS1" "PTENP1" "RAMP2-AS1" "RAPGEF4-AS1" "RP9P"
## [31] "RPL31P11" "SCARNA7" "SCARNA9" "SFTA1P" "SLC25A5-AS1"
## [36] "SMAD5-AS1" "SMIM8" "SNAI3-AS1" "ST7" "ST7-AS1"
## [41] "ST7-OT4" "STAG3L4" "TDGF1P3" "TPTEP1" "WDFY3-AS2"
## [46] "WWC2-AS2" "ZFHX4-AS1" "ZNF192P1" "ZNF280D"
##
## $`Sample 3`$`miRSM 1`$mRNA
## [1] "KL" "KLB" "KLF11" "KLF15" "KLF4"
## [6] "KLF6" "KLF8" "KLF9" "KLHDC1" "KLHL3"
## [11] "KLHL31" "LARP6" "LCN10" "LDB2" "LEPR"
## [16] "LETMD1" "LGR4" "LHFP" "LHX6" "LIFR"
## [21] "LIMS2" "LMO2" "LONRF1" "LPL" "LRCH1"
## [26] "LRCH2" "LRRC2" "LRRC34" "LRRN3" "LYVE1"
## [31] "MAOA" "MAP1LC3C" "MAP3K8" "MAP7D3" "MASP1"
## [36] "MSRB3" "MTMR10" "MTURN" "MYCT1" "MYL9"
## [41] "MYOC" "MYOCD" "NAALAD2" "NAT8L" "NATD1"
## [46] "NDEL1" "NDN" "NECAB1" "NFIA" "NLGN1"
## [51] "NLRP1" "NMNAT2" "NMT2" "NMUR1" "NNAT"
## [56] "NOTCH4" "NPR1" "NR3C1" "NRN1" "OGN"
## [61] "OSBPL1A" "P2RX6" "PALM2-AKAP2" "PARK2" "RAPGEF2"
## [66] "RB1" "RBMS2" "RBMS3" "REV3L" "RGL1"
##
##
## $`Sample 3`$`miRSM 2`
## $`Sample 3`$`miRSM 2`$ceRNA
## [1] "A2M-AS1" "EMX2OS" "GGTA1P" "LINC00961"
##
## $`Sample 3`$`miRSM 2`$mRNA
## [1] "C10orf10" "C10orf54" "C14orf180" "C17orf51" "EBF1" "EBF3"
## [7] "FBLN5" "FERMT2" "FGF2" "FGF9" "FHL1" "FHL5"
## [13] "FIGF" "FIGN" "FLRT2" "FOXN3" "FOXO1" "FREM1"
## [19] "FRMD1" "FRMD4A" "FZD4" "GABARAPL1" "GALNT15" "GCOM1"
## [25] "GDF10" "GDF5" "GHR" "GID4" "GIMAP6" "GIMAP8"
## [31] "GIPC2" "GLYAT" "GNAI1" "GNAL" "GPAM" "GPD1"
## [37] "GPIHBP1" "GPLD1" "GPR146" "GPR17" "GPX3" "GRASP"
## [43] "GSTM5" "GYG2" "GYPC" "HIF3A" "HN1L" "HSPB6"
## [49] "HSPB7" "HYAL1" "IGF1" "IGF2" "IGSF10" "IL33"
## [55] "INMT" "IQSEC3" "ITIH5" "ITM2A" "KANK1" "KCNAB1"
## [61] "KCNB1" "KCNIP2" "KCTD12" "KIAA0355" "KIAA0408" "KIF26A"miRSM implements module_FA function to conduct
functional analysis of miRNA sponge modules. The functional analysis
includes two types: functional enrichment analysis (FEA) and disease
enrichment analysis (DEA). Functional enrichment analysis includes GO,
KEGG and Reactome enrichment analysis. The ontology databases used
contain GO: Gene Ontology database (http://www.geneontology.org/), KEGG: Kyoto Encyclopedia
of Genes and Genomes Pathway Database (http://www.genome.jp/kegg/), and Reactome: Reactome
Pathway Database (http://reactome.org/). Disease enrichment analysis
includes DO, DGN and NCG enrichment analysis. The disease databases used
include DO: Disease Ontology database (http://disease-ontology.org/), DGN: DisGeNET database
(http://www.disgenet.org/) and NCG: Network of Cancer
Genes database (http://ncg.kcl.ac.uk/).
modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(150)],
mRExp[, seq_len(150)])
# Identify miRNA sponge modules using sensitivity RV coefficient (SRVC)
miRSM_WGCNA_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget,
modulegenes_WGCNA, method = "SRVC",
SMC.cutoff = 0.01, RV_method = "RV")
miRSM_WGCNA_SRVC_genes <- miRSM_WGCNA_SRVC[[2]]
miRSM_WGCNA_SRVC_FEA <- module_FA(miRSM_WGCNA_SRVC_genes, Analysis.type = 'FEA')
miRSM_WGCNA_SRVC_DEA <- module_FA(miRSM_WGCNA_SRVC_genes, Analysis.type = 'DEA')To investigate whether the identified miRNA sponge modules are
functionally associated with cancer of interest, miRSM
implements module_CEA function to conduct cancer enrichment
analysis by using a hypergeometric test.
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0895 0.309 0.1170 98.900 119.0000 155.00
## 2 2 0.0596 -0.227 0.2320 50.800 60.6000 105.00
## 3 3 0.2130 -0.351 0.2590 30.900 33.1000 78.20
## 4 4 0.3920 -0.466 0.2520 20.400 18.8000 60.80
## 5 5 0.5420 -0.569 0.4120 14.200 11.0000 48.40
## 6 6 0.7680 -0.619 0.7610 10.200 6.6000 39.20
## 7 7 0.8130 -0.651 0.8450 7.530 4.0200 32.10
## 8 8 0.7970 -0.740 0.8160 5.690 2.4800 26.60
## 9 9 0.6740 -0.843 0.6600 4.380 1.5500 22.20
## 10 10 0.1650 -1.840 0.0659 3.430 0.9810 18.70
## 11 12 0.1760 -1.910 0.0729 2.200 0.4040 13.80
## 12 14 0.9700 -0.898 0.9690 1.490 0.1860 10.40
## 13 16 0.9630 -0.922 0.9530 1.060 0.0876 8.05
## 14 18 0.9490 -0.889 0.9570 0.777 0.0419 6.33
## 15 20 0.9310 -0.919 0.9270 0.589 0.0198 5.38
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.# Identify miRNA sponge modules using sensitivity RV coefficient (SRVC)
miRSM_WGCNA_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget,
modulegenes_WGCNA, method = "SRVC",
SMC.cutoff = 0.01, RV_method = "RV")
miRSM_WGCNA_SRVC_genes <- miRSM_WGCNA_SRVC[[2]]
miRSM.CEA.pvalue <- module_CEA(ceRExp, mRExp, BRCA_genes, miRSM_WGCNA_SRVC_genes)
miRSM.CEA.pvalue## miRSM 1
## 0.2835354The function module_Validate is implemented to validate the miRNA sponge interactions existed in each miRNA sponge module. The built-in high-confidence groundtruth of miRNA sponge interactions is obtained from miRSponge (http://bio-bigdata.hrbmu.edu.cn/miRSponge/), LncACTdb 3.0 (http://bio-bigdata.hrbmu.edu.cn/LncACTdb/), LncCeRBase (http://www.insect-genome.com/LncCeRBase/front/).
If you want to use low-confidence groundtruth of miRNA sponge interactions for validation, ENCORI (https://rnasysu.com/encori/) is suggested. For example, by using web API of ENCORI, the mRNA related miRNA sponge interactions are from https://rna.sysu.edu.cn/encori/api/ceRNA/?assembly=hg38&geneType=mRNA&ceRNA=all&miRNAnum=1&pval=0.01&fdr=0.01&pancancerNum=1, the lncRNA related miRNA sponge interactions are from https://rna.sysu.edu.cn/encori/api/ceRNA/?assembly=hg38&geneType=lncRNA&ceRNA=all&miRNAnum=1&pval=0.01&fdr=0.01&pancancerNum=1, and the pseudogene related miRNA sponge interactions are from https://rna.sysu.edu.cn/encori/api/ceRNA/?assembly=hg38&geneType=pseudogene&ceRNA=all&miRNAnum=1&pval=0.01&fdr=0.01&pancancerNum=1.
# Using the built-in groundtruth from the miRSM package
Groundtruthcsv <- system.file("extdata", "Groundtruth_high.csv", package="miRSM")
Groundtruth <- read.csv(Groundtruthcsv, header=TRUE, sep=",")
# Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC)
miRSM.Validate <- module_Validate(miRSM_WGCNA_SRVC_genes, Groundtruth)To evaluate whether the ceRNAs and mRNAs in the miRNA sponge modules
are not randomly co-expressed, miRSM implements
module_Coexpress function to calculate average (mean and
median) absolute Pearson correlation of all the ceRNA-mRNA pairs in each
miRNA sponge module to see the overall co-expression level between the
ceRNAs and mRNAs in the miRNA sponge module. For each miRNA sponge
module, miRSM performs a permutation test by generating
random modules (the parameter resample is the number of random
modules to be generated) with the same number of ceRNAs and mRNAs for it
to compute the statistical significance p-value of the co-expression
level.
# Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC)
miRSM_WGCNA_Coexpress <- module_Coexpress(ceRExp, mRExp, miRSM_WGCNA_SRVC_genes, resample = 10, method = "mean", test.method = "t.test")
miRSM_WGCNA_Coexpress## $`Real miRNA sponge modules`
## [1] 0.7512547
##
## $`Random miRNA sponge modules`
## [1] 0.22727
##
## $`Statistical significance p-value`
## [1] 4.916008e-10To investigate the distribution of sharing miRNAs in the identified
miRNA sponge modules, miRSM implements
module_miRdistribute function. The miRNA distribution analysis
can understand whether the sharing miRNAs act as crosslinks across
different miRNA sponge modules.
# Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC)
miRSM_WGCNA_share_miRs <- share_miRs(miRExp, miRTarget, miRSM_WGCNA_SRVC_genes)
miRSM_WGCNA_miRdistribute <- module_miRdistribute(miRSM_WGCNA_share_miRs)
head(miRSM_WGCNA_miRdistribute)## miRNA Module ID Number of modules
## [1,] "hsa-let-7b-5p" "miRSM 1" "1"
## [2,] "hsa-let-7d-5p" "miRSM 1" "1"
## [3,] "hsa-let-7e-5p" "miRSM 1" "1"
## [4,] "hsa-miR-125a-5p" "miRSM 1" "1"
## [5,] "hsa-miR-148b-3p" "miRSM 1" "1"
## [6,] "hsa-miR-149-5p" "miRSM 1" "1"Since the identified miRNA sponge modules and their sharing miRNAs
can also be used to predict miRNA-target interactions (including
miRNA-ceRNA and miRNA-mRNA interactions), miRSM implements
module_miRtarget function to predict miRNA-target interactions
underlying in each miRNA sponge module.
To extract miRNA sponge interactions of each miRNA sponge module,
miRSM implements module_miRsponge function to
identify miRNA sponge interactions.
miRSM provides several functions to study miRNA sponge
modules at single-sample and multi-sample levels, including popular
methods for inferring gene modules (candidate miRNA sponge or ceRNA
modules), and two functions to identify miRNA sponge modules at
single-sample and multi-sample levels, as well as several functions to
conduct modular analysis of miRNA sponge modules. It could provide a
useful tool for the research of miRNA sponge modules at single-sample
and multi-sample levels.
[1] Ambros V. microRNAs: tiny regulators with great potential. Cell, 2001, 107:823–6.
[2] Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004, 116:281–97.
[3] Du T, Zamore PD. Beginning to understand microRNA function. Cell Research, 2007, 17:661–3.
[4] Esquela-Kerscher A, Slack FJ. Oncomirs—microRNAs with a role in cancer. Nature Reviews Cancer, 2006, 6:259–69.
[5] Lin S, Gregory RI. MicroRNA biogenesis pathways in cancer. Nature Reviews Cancer, 2015, 15:321–33.
[6] Cesana M, Cacchiarelli D, Legnini I, et al. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell, 2011, 147:358–69.
[7] Poliseno L, Salmena L, Zhang J, et al. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 2010, 465:1033–8.
[8] Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature, 2013, 495:384–8.
[9] Memczak S, Jens M, Elefsinioti A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 2013, 495:333–8.
[10] Salmena L, Poliseno L, Tay Y, et al. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell, 2011, 146(3):353-8.
[11] Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9:559.
[12] Bunte K, Lepp"{a}aho E, Saarinen I, Kaski S. Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics, 2016, 32(16):2457-63.
[13] Lepp"{a}aho E, Ammad-ud-din M, Kaski S. GFA: exploratory analysis of multiple data sources with group factor analysis. J Mach Learn Res., 2017, 18(39):1-5.
[14] Csardi G, Nepusz T. The igraph software package for complex network research, InterJournal, Complex Systems, 2006:1695.
[15] Clauset A, Newman ME, Moore C. Finding community structure in very large networks. Phys Rev E Stat Nonlin Soft Matter Phys., 2004, 70(6 Pt 2):066111.
[16] Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res., 2002, 30(7):1575-84.
[17] Kalinka AT, Tomancak P. linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics, 2011, 27(14):2011-2.
[18] Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4:2.
[19] Zhang Y, Phillips CA, Rogers GL, Baker EJ, Chesler EJ, Langston MA. On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types. BMC Bioinformatics, 2014, 15:110.
[20] Gaujoux R, Seoighe C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics, 2010, 11:367.
[21] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2018.
[22] Langfelder P, Horvath S. Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software. 2012, 46(11):1-17.
[23] Hahsler M, Piekenbrock M. dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms. R package version 1.1-2, 2018.
[24] Cebeci Z, Yildiz F, Kavlak AT, Cebeci C, Onder H. ppclust: Probabilistic and Possibilistic Cluster Analysis. R package version 0.1.1, 2018.
[25] Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1, 2016, pp. 205-233.
[26] Villa-Vialaneix N, Bendhaiba L, Olteanu M. SOMbrero: SOM Bound to Realize Euclidean and Relational Outputs. R package version 1.2-3, 2018.
[27] Cebeci Z, Yildiz F, Kavlak AT, Cebeci C, Onder H. ppclust: Probabilistic and Possibilistic Cluster Analysis. R package version 0.1.2, 2019.
[28] Kaiser S, Santamaria R, Khamiakova T, Sill M, Theron R, Quintales L, Leisch F, De TE. biclust: BiCluster Algorithms. R package version 1.2.0., 2015.
[29] Gusenleitner D, Howe EA, Bentink S, Quackenbush J, Culhane AC. iBBiG: iterative binary bi-clustering of gene sets. Bioinformatics, 2012, 28(19):2484-92.
[30] Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, Kasim A, Khamiakova T, Van Sanden S, Lin D, Talloen W, Bijnens L, G"{o}hlmann HW, Shkedy Z, Clevert DA. FABIA: factor analysis for bicluster acquisition. Bioinformatics, 2010, 26(12):1520-7.
[31] Yang J, Wang H, Wang W, Yu, PS. An improved biclustering method for analyzing gene expression. Int J Artif Intell Tools, 2005, 14(5): 771-789.
[32] Bergmann S, Ihmels J, Barkai N. Iterative signature algorithm for the analysis of large-scale gene expression data. Phys Rev E Stat Nonlin Soft Matter Phys., 2003, 67(3 Pt 1):031902.
[33] Sill M, Kaiser S, Benner A, Kopp-Schneider A. Robust biclustering by sparse singular value decomposition incorporating stability selection. Bioinformatics, 2011, 27(15):2089-97.
[34] Rodriguez-Baena DS, Perez-Pulido AJ, Aguilar-Ruiz JS. A biclustering algorithm for extracting bit-patterns from binary datasets. Bioinformatics, 2011, 27(19):2738-45.
[35] Li G, Ma Q, Tang H, Paterson AH, Xu Y. QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Res., 2009, 37(15):e101.
[36] Zhang J, Le TD, Liu L, Li J. Identifying miRNA sponge modules using biclustering and regulatory scores. BMC Bioinformatics, 2017, 18(Suppl 3):44.
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
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## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] NMF_0.28 cluster_2.1.6 rngtools_1.5.2
## [4] registry_0.5-1 miRSM_2.3.0 Biobase_2.67.0
## [7] BiocGenerics_0.53.3 generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.5 matrixStats_1.4.1
## [3] enrichplot_1.27.1 httr_1.4.7
## [5] RColorBrewer_1.1-3 doParallel_1.0.17
## [7] dynamicTreeCut_1.63-1 tools_4.4.2
## [9] backports_1.5.0 utf8_1.2.4
## [11] R6_2.5.1 lazyeval_0.2.2
## [13] MatrixCorrelation_0.10.0 ppclust_1.1.0.1
## [15] withr_3.0.2 graphite_1.53.0
## [17] prettyunits_1.2.0 gridExtra_2.3
## [19] preprocessCore_1.69.0 flexclust_1.4-2
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## [151] modeltools_0.2-23 Formula_1.2-5
## [153] GenomeInfoDbData_1.2.13 ggplotify_0.1.2
## [155] patchwork_1.3.0 munsell_0.5.1
## [157] Rcpp_1.0.13-1 biclust_2.0.3.1
## [159] ape_5.8 viridis_0.6.5
## [161] stringi_1.8.4 ggraph_2.2.1
## [163] zlibbioc_1.52.0 MASS_7.3-61
## [165] plyr_1.8.9 org.Hs.eg.db_3.20.0
## [167] parallel_4.4.2 randomcoloR_1.1.0.1
## [169] ggrepel_0.9.6 GFA_1.0.5
## [171] deldir_2.0-4 Biostrings_2.75.1
## [173] graphlayouts_1.2.1 splines_4.4.2
## [175] gridtext_0.1.5 multtest_2.63.0
## [177] subspace_1.0.4 hms_1.1.3
## [179] igraph_2.1.1 fastcluster_1.2.6
## [181] markdown_1.13 buildtools_1.0.0
## [183] reshape2_1.4.4 stats4_4.4.2
## [185] XML_3.99-0.17 evaluate_1.0.1
## [187] BiocManager_1.30.25 httpuv_1.6.15
## [189] foreach_1.5.2 tweenr_2.0.3
## [191] s4vd_1.1-1 tidyr_1.3.1
## [193] purrr_1.0.2 polyclip_1.10-7
## [195] isa2_0.3.6 ggplot2_3.5.1
## [197] gridBase_0.4-7 ReactomePA_1.51.0
## [199] ggforce_0.4.2 xtable_1.8-4
## [201] reactome.db_1.89.0 RSpectra_0.16-2
## [203] tidytree_0.4.6 later_1.3.2
## [205] viridisLite_0.4.2 class_7.3-22
## [207] gsl_2.1-8 tibble_3.2.1
## [209] clusterProfiler_4.15.0 aplot_0.2.3
## [211] memoise_2.0.1 AnnotationDbi_1.69.0
## [213] IRanges_2.41.1 GSEABase_1.69.0