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.
Given matched ceRNA and mRNA expression data, we infer 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).
data(BRCASampleData)By using WGCNA method [11], we identify co-expressed gene modules from matched ceRNA and mRNA expression data.
modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(80)],
mRExp[, seq_len(80)])## 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.modulegenes_WGCNA## 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.
modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)],
mRExp[seq_len(20), seq_len(15)],
iter.max = 2600)
modulegenes_GFABy using ‘igraph’ package [14], we infer gene modules from matched ceRNA and mRNA expression data. In the ‘igraph’ package, we can select “betweenness”, “greedy”, “infomap”, “prop”, “eigen”, “louvain” and “walktrap” methods for gene module identification. The default method is “greedy”.
modulegenes_igraph <- module_igraph(ceRExp[, seq_len(10)],
mRExp[, seq_len(10)])
modulegenes_igraph## 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, we can select FN [15], MCL [16], LINKCOMM [17] and MCODE [18] for gene module identification. The default method is MCL.
modulegenes_ProNet <- module_ProNet(ceRExp[, seq_len(10)],
mRExp[, seq_len(10)])
modulegenes_ProNet## 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], we infer gene modules from matched ceRNA and mRNA 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 (7 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)We Identify gene modules from matched ceRNA and mRNA 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”.
modulegenes_clust <- module_clust(ceRExp[, seq_len(30)],
mRExp[, seq_len(30)])
modulegenes_clust## GeneSetCollection
## names: Module 1, Module 2 (2 total)
## unique identifiers: ADAM21P1, ADORA2A-AS1, ..., FLVCR1 (19 total)
## types in collection:
## geneIdType: NullIdentifier (1 total)
## collectionType: NullCollection (1 total)We Identify gene modules from matched ceRNA and mRNA 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”.
modulegenes_biclust <- module_biclust(ceRExp[, seq_len(30)],
mRExp[, seq_len(30)])## Cycle: 0
Cycle: 20
Cycle: 40
Cycle: 60
Cycle: 80
Cycle: 100
Cycle: 120
Cycle: 140
Cycle: 160
Cycle: 180
Cycle: 200
Cycle: 220
Cycle: 240
Cycle: 260
Cycle: 280
Cycle: 300
Cycle: 320
Cycle: 340
Cycle: 360
Cycle: 380
Cycle: 400
Cycle: 420
Cycle: 440
Cycle: 460
Cycle: 480
Cycle: 500modulegenes_biclust## GeneSetCollection
## names: Module 1 (1 total)
## unique identifiers: COL10A1, FN1, ..., ANKRD36BP1 (8 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, we use the sensitivity canonical correlation (SCC), sensitivity distance correlation (SDC) and sensitivity RV coefficient (SRVC) methods to identify miRNA sponge modules. In addition, we also added the sponge module (SM) method proposed in [36] 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"We implement ‘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, we implement ‘module_CEA’ function to conduct cancer enrichment analysis by using a hypergeometric test.
modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(150)],
mRExp[, seq_len(150)])## 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.
# Using the built-in groundtruth from the miRspongeR package
library(miRspongeR)
Groundtruthcsv <- system.file("extdata", "Groundtruth.csv", package="miRspongeR")
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, we implement ‘module_Coexpress’ function 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, we perform 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] 0.2455236
##
## $`Statistical significance p-value`
## [1] 2.157184e-08To investigate the distribution of sharing miRNAs in the identified miRNA sponge modules, we implement ‘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, ceRExp, mRExp, 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), we implement ‘module_miRtarget’ function to predict miRNA-target interactions underlying in each miRNA sponge module.
# Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC)
miRSM_WGCNA_miRtarget <- module_miRtarget(miRSM_WGCNA_share_miRs, miRSM_WGCNA_SRVC_genes)To extract miRNA sponge interactions of each miRNA sponge module, we implement ‘module_miRsponge’ function to identify miRNA sponge interactions.
# Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC)
miRSM_WGCNA_miRsponge <- module_miRsponge(ceRExp, mRExp, miRSM_WGCNA_SRVC_genes)miRSM provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, 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.
[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.
sessionInfo()## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] NMF_0.26 cluster_2.1.4 rngtools_1.5.2
## [4] registry_0.5-1 miRSM_1.18.0 bigmemory_4.6.1
## [7] Biobase_2.60.0 BiocGenerics_0.46.0 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] IRanges_2.34.0 randomcoloR_1.1.0.1
## [3] GSEABase_1.62.0 progress_1.2.2
## [5] ppclust_1.1.0 nnet_7.3-18
## [7] Biostrings_2.68.0 vctrs_0.6.2
## [9] linkcomm_1.0-14 corpcor_1.6.10
## [11] energy_1.7-11 digest_0.6.31
## [13] png_0.1-8 shape_1.4.6
## [15] ppcor_1.1 MatrixCorrelation_0.10.0
## [17] isa2_0.3.6 ggrepel_0.9.3
## [19] deldir_1.0-6 parallelly_1.35.0
## [21] fabia_2.46.0 MASS_7.3-59
## [23] reshape2_1.4.4 httpuv_1.6.9
## [25] foreach_1.5.2 GFA_1.0.3
## [27] qvalue_2.32.0 withr_2.5.0
## [29] xfun_0.39 ggfun_0.0.9
## [31] ellipsis_0.3.2 ggpubr_0.6.0
## [33] survival_3.5-5 doRNG_1.8.6
## [35] memoise_2.0.1 biclust_2.0.3
## [37] kpeaks_1.1.0 gson_0.1.0
## [39] clusterProfiler_4.8.0 tidytree_0.4.2
## [41] GlobalOptions_0.1.2 V8_4.3.0
## [43] logging_0.10-108 Formula_1.2-5
## [45] prettyunits_1.1.1 promises_1.2.0.1
## [47] KEGGREST_1.40.0 scatterplot3d_0.3-43
## [49] httr_1.4.5 downloader_0.4
## [51] rstatix_0.7.2 globals_0.16.2
## [53] miRBaseConverter_1.24.0 rstudioapi_0.14
## [55] generics_0.1.3 DOSE_3.26.0
## [57] reactome.db_1.84.0 base64enc_0.1-3
## [59] curl_5.0.0 S4Vectors_0.38.0
## [61] zlibbioc_1.46.0 ggraph_2.1.0
## [63] polyclip_1.10-4 randomForest_4.7-1.1
## [65] GenomeInfoDbData_1.2.10 xtable_1.8-4
## [67] stringr_1.5.0 ade4_1.7-22
## [69] pracma_2.4.2 doParallel_1.0.17
## [71] evaluate_0.20 BiocFileCache_2.8.0
## [73] preprocessCore_1.62.0 hms_1.1.3
## [75] glmnet_4.1-7 GenomicRanges_1.52.0
## [77] irlba_2.3.5.1 bookdown_0.33
## [79] ggwordcloud_0.5.0 colorspace_2.1-0
## [81] filelock_1.0.2 magrittr_2.0.3
## [83] Rgraphviz_2.44.0 later_1.3.0
## [85] ggtree_3.8.0 viridis_0.6.2
## [87] modeltools_0.2-23 lattice_0.21-8
## [89] future.apply_1.10.0 s4vd_1.1-1
## [91] lhs_1.1.6 shadowtext_0.1.2
## [93] XML_3.99-0.14 cowplot_1.1.1
## [95] matrixStats_0.63.0 additivityTests_1.1-4.1
## [97] class_7.3-21 Hmisc_5.0-1
## [99] pillar_1.9.0 nlme_3.1-162
## [101] iterators_1.0.14 gridBase_0.4-7
## [103] compiler_4.3.0 RSpectra_0.16-1
## [105] stringi_1.7.12 gower_1.0.1
## [107] SummarizedExperiment_1.30.0 dendextend_1.17.1
## [109] lubridate_1.9.2 SPONGE_1.22.0
## [111] plyr_1.8.8 crayon_1.5.2
## [113] abind_1.4-5 gridGraphics_0.5-1
## [115] org.Hs.eg.db_3.17.0 graphlayouts_0.8.4
## [117] bit_4.0.5 dplyr_1.1.2
## [119] fastmatch_1.1-3 fastcluster_1.2.3
## [121] codetools_0.2-19 recipes_1.0.6
## [123] bslib_0.4.2 GetoptLong_1.0.5
## [125] mime_0.12 multtest_2.56.0
## [127] metR_0.14.0 splines_4.3.0
## [129] markdown_1.6 circlize_0.4.15
## [131] Rcpp_1.0.10 MetBrewer_0.2.0
## [133] gRbase_1.8.9 flexclust_1.4-1
## [135] PMA_1.2.1 dbplyr_2.3.2
## [137] HDO.db_0.99.1 interp_1.1-4
## [139] knitr_1.42 blob_1.2.4
## [141] utf8_1.2.3 clue_0.3-64
## [143] listenv_0.9.0 checkmate_2.1.0
## [145] tnet_3.0.16 expm_0.999-7
## [147] gsl_2.1-8 ggsignif_0.6.4
## [149] ggplotify_0.1.0 MCL_1.0
## [151] tibble_3.2.1 Matrix_1.5-4
## [153] tweenr_2.0.2 pkgconfig_2.0.3
## [155] tools_4.3.0 cachem_1.0.7
## [157] RSQLite_2.3.1 viridisLite_0.4.1
## [159] DBI_1.1.3 graphite_1.46.0
## [161] impute_1.74.0 fastmap_1.1.1
## [163] rmarkdown_2.21 scales_1.2.1
## [165] grid_4.3.0 broom_1.0.4
## [167] sass_0.4.5 patchwork_1.1.2
## [169] BiocManager_1.30.20 graph_1.78.0
## [171] carData_3.0-5 rpart_4.1.19
## [173] farver_2.1.1 BicARE_1.58.0
## [175] scatterpie_0.1.9 tidygraph_1.2.3
## [177] yaml_2.3.7 MatrixGenerics_1.12.0
## [179] foreign_0.8-84 cli_3.6.1
## [181] purrr_1.0.1 stats4_4.3.0
## [183] lifecycle_1.0.3 dbscan_1.1-11
## [185] caret_6.0-94 iBBiG_1.44.0
## [187] lava_1.7.2.1 backports_1.4.1
## [189] BiocParallel_1.34.0 annotate_1.78.0
## [191] timechange_0.2.0 gtable_0.3.3
## [193] rjson_0.2.21 ggridges_0.5.4
## [195] ape_5.7-1 parallel_4.3.0
## [197] pROC_1.18.0 jsonlite_1.8.4
## [199] bitops_1.0-7 ggplot2_3.4.2
## [201] bigmemory.sri_0.1.6 assertthat_0.2.1
## [203] bit64_4.0.5 Rtsne_0.16
## [205] yulab.utils_0.0.6 ReactomePA_1.44.0
## [207] subspace_1.0.4 jquerylib_0.1.4
## [209] GOSemSim_2.26.0 inaparc_1.2.0
## [211] timeDate_4022.108 lazyeval_0.2.2
## [213] shiny_1.7.4 dynamicTreeCut_1.63-1
## [215] rJava_1.0-6 htmltools_0.5.5
## [217] enrichplot_1.20.0 GO.db_3.17.0
## [219] rappdirs_0.3.3 glue_1.6.2
## [221] XVector_0.40.0 RCurl_1.98-1.12
## [223] rqubic_1.46.0 treeio_1.24.0
## [225] mclust_6.0.0 BiBitR_0.3.1
## [227] miRspongeR_2.4.0 gridExtra_2.3
## [229] boot_1.3-28.1 flashClust_1.01-2
## [231] igraph_1.4.2 R6_2.5.1
## [233] tidyr_1.3.0 SOMbrero_1.4-1
## [235] ggvis_0.4.8 aplot_0.1.10
## [237] GenomeInfoDb_1.36.0 ipred_0.9-14
## [239] DelayedArray_0.26.0 tidyselect_1.2.0
## [241] plotrix_3.8-2 WGCNA_1.72-1
## [243] htmlTable_2.4.1 ggforce_0.4.1
## [245] xml2_1.3.3 car_3.1-2
## [247] AnnotationDbi_1.62.0 future_1.32.0
## [249] ModelMetrics_1.2.2.2 munsell_0.5.0
## [251] data.table_1.14.8 htmlwidgets_1.6.2
## [253] fgsea_1.26.0 ComplexHeatmap_2.16.0
## [255] RColorBrewer_1.1-3 biomaRt_2.56.0
## [257] rlang_1.1.0 tidyverse_2.0.0
## [259] uuid_1.1-0 cvms_1.3.9
## [261] fansi_1.0.4 hardhat_1.3.0
## [263] prodlim_2023.03.31