Working with SpidermiR package
2020-10-01
Contents
- Introduction
- Installation
SpidermiRquery: Searching networkSpidermiRdownload: Downloading network dataSpidermiRdownload_net: Download networkSpidermiRdownload_miRNAprediction: Downloading miRNA predicted data targetSpidermiRdownload_miRNAvalidate: Downloading miRNA validated data targetSpidermiRdownload_miRNAextra_cir:Download Extracellular Circulating microRNAsSpidermiRdownload_pharmacomir: Download Pharmaco-miR Verified Sets
SpidermiRprepare: Preparing the dataSpidermiRanalyze: : Analyze data from network dataSpidermiRanalyze_direct_net: Searching by biomarkers of interest with direct interactionSpidermiRanalyze_direct_subnetwork: Network composed by only the nodes in a set of biomarkers of interestSpidermiRanalyze_subnetwork_neigh: Network composed by the nodes in the list of BI and all the edges among this brunch of nodes.SpidermiRanalyze_degree_centrality: Ranking degree centrality genesSpidermiRanalyze_Community_detection: Find community detectionSpidermiRanalyze_Community_detection_net: Community detectionSpidermiRanalyze_Community_detection_bi: Community detection from a set of biomarkers of interest
SpidermiRvisualize: To visualize the networkSpidermiRvisualize_plot_target: Visualize the plot with miRNAs and the number of their targets in the network.SpidermiRvisualize_degree_dist: plots the degree distribution of the networkSpidermiRvisualize_adj_matrix: plots the adjacency matrix of the networkSpidermiRvisualize_3Dbarplot: 3D barplot
Features databases SpidermiR:- References
Introduction
Biological systems are composed of multiple layers of dynamic interaction networks. These networks can be decomposed, for example, into: co-expression, physical, co-localization, genetic, pathway, and shared protein domains.
GeneMania provides us with an enormous collection of data sets for interaction network studies (Warde-Farley D, Donaldson S, Comes O, Zuberi K, Badrawi R, and others 2010). The data can be accessed and downloaded from different database, using a web portal. But currently, there is not a R-package to query and download these data.
An important regulatory mechanism of these network data involves microRNAs (miRNAs). miRNAs are involved in various cellular functions, such as differentiation, proliferation, and tumourigenesis. However, our understanding of the processes regulated by miRNAs is currently limited and the integration of miRNA data in these networks provides a comprehensive genome-scale analysis of miRNA regulatory networks.Actually, GeneMania doesn’t integrate the information of miRNAs and their interactions in the network.
SpidermiR allows the user to query, prepare, download network data (e.g. from GeneMania), and to integrate this information with miRNA data
with the possibility to analyze
these downloaded data directly in one single R package.
This techincal report gives a short overview of the essential SpidermiR methods and their application.
Installation
To install use the code below.
SpidermiRquery: Searching network
You can easily search GeneMania data using the SpidermiRquery function.
SpidermiRquery_species: Searching by species
The user can query the species supported by GeneMania, using the function SpidermiRquery_species:
The list of species is shown below:
| tabOrgd[, 2] | |
|---|---|
| 1 | Arabidopsis_thaliana |
| 2 | Caenorhabditis_elegans |
| 3 | Danio_rerio |
| 4 | Drosophila_melanogaster |
| 5 | Escherichia_coli |
| 6 | Homo_sapiens |
| 7 | Mus_musculus |
| 8 | Rattus_norvegicus |
| 9 | Saccharomyces_cerevisiae |
SpidermiRquery_networks_type: Searching by network categories
The user can query the network types supported by GeneMania for a specific specie, using the function SpidermiRquery_networks_type. The user can select a specific specie using an index obtained by the function SpidermiRquery_species (e.g. organismID=org[6,] is the input for Homo_sapiens,organismID=org[9,] is the input for Saccharomyces cerevisiae )
The list of network categories in Saccharomyces cerevisiae is shown below:
## [1] "Co-localization" "Predicted" "Co-expression"
## [4] "Physical Interactions" "Genetic Interactions" "Shared protein domains"
## [7] "Other"SpidermiRquery_spec_networks: Searching by species, and network categories
You can filter the search by species using organism ID (above reported), and the network category. The network category can be filtered using the following parameters:
- COexp Co-expression
- PHint Physical_interactions
- COloc Co-localization
- GENint Genetic_interactions
- PATH Pathway
- SHpd Shared_protein_domains
- pred predicted
The databases, which data are collected, are the output of this step. An example is shown below ( for Shared protein domains in Saccharomyces_cerevisiae data are collected in INTERPRO, and PFAM):
## [1] "http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.INTERPRO.txt"
## [2] "http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.PFAM.txt"SpidermiRdownload: Downloading network data
The user in this step can download the data, as previously queried.
SpidermiRdownload_net: Download network
The user can download the data (previously queried) with SpidermiRdownload_net.
## [1] "Downloading: http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.INTERPRO.txt ... reference n. 1 of 2"
## [1] "Downloading: http://genemania.org/data/current/Saccharomyces_cerevisiae/Shared_protein_domains.PFAM.txt ... reference n. 2 of 2"The list of SpidermiRdownload_net is shown below:
## List of 2
## $ :'data.frame': 47523 obs. of 3 variables:
## ..$ Gene_A: chr [1:47523] "Q0050" "Q0050" "Q0055" "Q0050" ...
## ..$ Gene_B: chr [1:47523] "Q0055" "Q0060" "Q0060" "Q0065" ...
## ..$ Weight: num [1:47523] 0.39 0.09 0.15 0.09 0.15 0.23 0.1 0.17 0.18 0.18 ...
## $ :'data.frame': 30228 obs. of 3 variables:
## ..$ Gene_A: chr [1:30228] "Q0050" "Q0055" "Q0055" "Q0060" ...
## ..$ Gene_B: chr [1:30228] "Q0055" "Q0060" "Q0065" "Q0065" ...
## ..$ Weight: num [1:30228] 0.39 0.12 0.12 0.22 0.14 0.14 0.14 0.15 0.15 0.34 ...SpidermiRdownload_miRNAprediction: Downloading miRNA predicted data target
The user can download the predicted miRNA-gene from 4 databases:DIANA, Miranda, PicTar and TargetScan using miRNAtap (Pajak M, Simpson TI 2019).
SpidermiRdownload_miRNAextra_cir:Download Extracellular Circulating microRNAs
The user can download extracellular circulating miRNAs from miRandola database
SpidermiRdownload_pharmacomir: Download Pharmaco-miR Verified Sets
The user can download Pharmaco-miR predicted Sets from a fisher test. This function will give an information about the significant interactions between the miRNAs and the drug of interest (in this case tamoxifen). p-value and FDR will be visualized. The fisher test will be calculated considering the interactions drug and gene derived by DGIdb and MATADOR database (Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, Wollam A, Spies NC, Griffith OL, Griffith M. 2018) (GUnther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P, Preissner R. 2008).
list<-SpidermiRdownload_miRNAvalidate(validated)
drug="TAMOXIFEN"
drug_genetarget<-SpidermiRdownload_pharmacomir(list[1:100,],drug="TAMOXIFEN")## [1] "processing...... hsa-let-7a....n. 1 of......3"
## [1] "processing...... hsa-let-7a-3p....n. 2 of......3"
## [1] "processing...... hsa-let-7a-5p....n. 3 of......3"The output generated by SpidermiRdownload_pharmacomir will give a table with 6 columns: in the first column miRNAs associated with the drug of interest, in the second column the number of drug targets, the number of miRNA targets, the common genes between drug targets and miRNA targets, p-value and FDR.
| drug_target | miRNA_target | common_gene | Pvalue | FDR |
|---|
The list of drugs can be chosen between the drugs generated by the function SpidermiRdownload_drug_gene:
SpidermiRprepare: Preparing the data
SpidermiRprepare_NET: Prepare matrix of gene network with Ensembl Gene ID, and gene symbols
SpidermiRprepare_NET reads network data from SpidermiRdownload_net and enables user to prepare them for downstream analysis. In particular, it prepares matrix of gene network mapping Ensembl Gene ID to gene symbols. Gene symbols are needed to integrate miRNAdata.
## [1] "Preprocessing of the network n. 1 of 2"
## [1] "Preprocessing of the network n. 2 of 2"The network with gene symbols ID is shown below:
| Gene_A | Gene_B | Weight | gene_symbolA | gene_symbolB |
|---|---|---|---|---|
| Q0050 | Q0055 | 0.39 | AI1 | AI2 |
| Q0050 | Q0060 | 0.09 | AI1 | AI3 |
| Q0055 | Q0060 | 0.15 | AI2 | AI3 |
| Q0050 | Q0065 | 0.09 | AI1 | AI4 |
| Q0055 | Q0065 | 0.15 | AI2 | AI4 |
SpidermiRanalyze: : Analyze data from network data
SpidermiRanalyze_direct_net: Searching by biomarkers of interest with direct interaction
Starting from a set of biomarkers of interest (BI), genes, miRNA or both, given by the user, this function finds sub-networks including all direct interactions involving at least one of the BI.
biomark_of_interest<-c("hsa-let-7a","CDC34","hsa-miR-27a","PEX7","EPT1","FOX","hsa-miR-5a")
miRNA_NET <-data.frame(V1=c('hsa-let-7a','CASP3','BRCA','hsa-miR-7a','hsa-miR-5a','SMAD','SOX'),V2=c('CASP3','TAMOXIFEN','MYC','PTEN','FOX','HIF1','P53'),stringsAsFactors=FALSE)
GIdirect_net<-SpidermiRanalyze_direct_net(data=miRNA_NET,BI=biomark_of_interest)## [1] "CDC34 is not in the network or please check the correct name"
## [1] "hsa-miR-27a is not in the network or please check the correct name"
## [1] "PEX7 is not in the network or please check the correct name"
## [1] "EPT1 is not in the network or please check the correct name"The data frame of SpidermiRanalyze_direct_net, GIdirect_net, is shown below:
## 'data.frame': 2 obs. of 2 variables:
## $ V1: chr "hsa-let-7a" "hsa-miR-5a"
## $ V2: chr "CASP3" "FOX"SpidermiRanalyze_direct_subnetwork: Network composed by only the nodes in a set of biomarkers of interest
Starting from BI, this function finds sub-networks including all direct interactions involving only BI.
SpidermiRanalyze_subnetwork_neigh: Network composed by the nodes in the list of BI and all the edges among this brunch of nodes.
Starting from BI, this function finds sub-networks including all direct and indirect interactions involving at least one of BI.
SpidermiRanalyze_degree_centrality: Ranking degree centrality genes
This function finds the number of direct neighbours of a node in a network and allows the selection of those nodes with a number of direct neighbours higher than a selected cut-off.
SpidermiRanalyze_Community_detection: Find community detection
This function find the communities in the network, and describes them in terms of number of community elements (both genes and miRNAs). The function uses one of the algorithms currently implemented in (Csardi G, Nepusz T. 2006), selected by the user according to the user need.
The user can choose the algorithm in order to calculate the community structure:
- EB edge.betweenness.community
- FC fastgreedy.community
- WC walktrap.community
- SC spinglass.community
- LE leading.eigenvector.community
- LP label.propagation.community
SpidermiRanalyze_Community_detection_net: Community detection
Starting from one community to which some BI belong (the output of the previously described function) this function describes the community as network of elements (both genes and miRNAs).
SpidermiRanalyze_Community_detection_bi: Community detection from a set of biomarkers of interest
Starting from the community to which BI belong (the output of the previously described function), this function indicates if a set of BI is included within such community.
SpidermiRvisualize: To visualize the network
SpidermiRvisualize_plot_target: Visualize the plot with miRNAs and the number of their targets in the network.
For each BI of a community, the user can visualize a plot showing the number of direct neighbours of such BI (the degree centrality of such BI).
## NULLSpidermiRvisualize_degree_dist: plots the degree distribution of the network
This function plots the cumulative frequency distribution of degree centrality of a community.
SpidermiRvisualize_adj_matrix: plots the adjacency matrix of the network
It plots the adjacency matrix of the community, representing the degree of connections among the nodes.
SpidermiRvisualize_3Dbarplot: 3D barplot
It plots a summary representation of the networks with the number of edges, nodes and miRNAs.
Features databases SpidermiR:
Features of databases integrated in SpidermiR are:
Session Information ******
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SpidermiR_1.18.2 miRNAtap_1.22.0 AnnotationDbi_1.50.3
## [4] IRanges_2.22.2 S4Vectors_0.26.1 Biobase_2.48.0
## [7] BiocGenerics_0.34.0 BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] proto_1.0.0 R.utils_2.10.1
## [3] tidyselect_1.1.0 RSQLite_2.2.1
## [5] htmlwidgets_1.5.1 grid_4.0.2
## [7] BiocParallel_1.22.0 scatterpie_0.1.5
## [9] devtools_2.3.2 munsell_0.5.0
## [11] chron_2.3-56 withr_2.3.0
## [13] colorspace_1.4-1 GOSemSim_2.14.2
## [15] highr_0.8 knitr_1.30
## [17] rstudioapi_0.11 ggsignif_0.6.0
## [19] DOSE_3.14.0 pathview_1.28.1
## [21] labeling_0.3 KEGGgraph_1.48.0
## [23] urltools_1.7.3 GenomeInfoDbData_1.2.3
## [25] polyclip_1.10-0 bit64_4.0.5
## [27] farver_2.0.3 pheatmap_1.0.12
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## [45] assertthat_0.2.1 networkD3_0.4
## [47] scales_1.1.1 ggraph_2.0.3
## [49] enrichplot_1.8.1 gtable_0.3.0
## [51] sva_3.36.0 processx_3.4.4
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## [65] qvalue_2.20.0 clusterProfiler_3.16.1
## [67] tools_4.0.2 usethis_1.6.3
## [69] bookdown_0.20 ggplotify_0.0.5
## [71] ggplot2_3.3.2 ellipsis_0.3.1
## [73] gplots_3.1.0 RColorBrewer_1.1-2
## [75] ggridges_0.5.2 sessioninfo_1.1.1
## [77] gsubfn_0.7 Rcpp_1.0.5
## [79] plyr_1.8.6 visNetwork_2.0.9
## [81] progress_1.2.2 zlibbioc_1.34.0
## [83] purrr_0.3.4 RCurl_1.98-1.2
## [85] ps_1.3.4 prettyunits_1.1.1
## [87] ggpubr_0.4.0 openssl_1.4.3
## [89] sqldf_0.4-11 viridis_0.5.1
## [91] cowplot_1.1.0 SummarizedExperiment_1.18.2
## [93] haven_2.3.1 ggrepel_0.8.2
## [95] fs_1.5.0 magrittr_1.5
## [97] magick_2.4.0 data.table_1.13.0
## [99] DO.db_2.9 openxlsx_4.2.2
## [101] triebeard_0.3.0 matrixStats_0.57.0
## [103] pkgload_1.1.0 xtable_1.8-4
## [105] hms_0.5.3 evaluate_0.14
## [107] XML_3.99-0.5 rio_0.5.16
## [109] jpeg_0.1-8.1 readxl_1.3.1
## [111] gridExtra_2.3 testthat_2.3.2
## [113] compiler_4.0.2 biomaRt_2.44.1
## [115] tibble_3.0.3 KernSmooth_2.23-17
## [117] crayon_1.3.4 R.oo_1.24.0
## [119] htmltools_0.5.0 mgcv_1.8-33
## [121] tidyr_1.1.2 DBI_1.1.0
## [123] tweenr_1.0.1 dbplyr_1.4.4
## [125] MASS_7.3-53 rappdirs_0.3.1
## [127] Matrix_1.2-18 car_3.0-10
## [129] readr_1.3.1 cli_2.0.2
## [131] R.methodsS3_1.8.1 gdata_2.18.0
## [133] igraph_1.2.5 GenomicRanges_1.40.0
## [135] forcats_0.5.0 pkgconfig_2.0.3
## [137] rvcheck_0.1.8 foreign_0.8-80
## [139] xml2_1.3.2 annotate_1.66.0
## [141] XVector_0.28.0 rvest_0.3.6
## [143] stringr_1.4.0 callr_3.4.4
## [145] digest_0.6.25 graph_1.66.0
## [147] Biostrings_2.56.0 rmarkdown_2.4
## [149] cellranger_1.1.0 fastmatch_1.1-0
## [151] TCGAbiolinks_2.16.4 edgeR_3.30.3
## [153] dendextend_1.14.0 miRNAtap.db_0.99.10
## [155] curl_4.3 gtools_3.8.2
## [157] nlme_3.1-149 lifecycle_0.2.0
## [159] jsonlite_1.7.1 carData_3.0-4
## [161] limma_3.44.3 viridisLite_0.3.0
## [163] desc_1.2.0 askpass_1.1
## [165] fansi_0.4.1 pillar_1.4.6
## [167] ggsci_2.9 lattice_0.20-41
## [169] survival_3.2-7 KEGGREST_1.28.0
## [171] httr_1.4.2 pkgbuild_1.1.0
## [173] GO.db_3.11.4 glue_1.4.2
## [175] remotes_2.2.0 zip_2.1.1
## [177] png_0.1-7 bit_4.0.4
## [179] Rgraphviz_2.32.0 ggforce_0.3.2
## [181] stringi_1.5.3 blob_1.2.1
## [183] org.Hs.eg.db_3.11.4 latticeExtra_0.6-29
## [185] caTools_1.18.0 memoise_1.1.0
## [187] dplyr_1.0.2References
Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, Wollam A, Spies NC, Griffith OL, Griffith M. 2018. “DGIdb 3.0 a Redesign and Expansion of the Drug Gene Interaction Database.”
Csardi G, Nepusz T. 2006. “The Igraph Software Package for Complex Network Research.”
Dweep H, Sticht C, Pandey P, Gretz N. 2011. “miRWalk - Database Prediction of Possible miRNA Binding Sites by ‘Walking’ the Genes of 3 Genomes.”
GUnther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P, Preissner R. 2008. “SuperTarget and Matador Resources for Exploring Drug-Target Relationships.”
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. 2009. “miR2Disease a Manually Curated Database for microRNA Deregulation in Human Disease.”
Pajak M, Simpson TI. 2019. “miRNAtap microRNA Targets - Aggregated Predictions.”
Warde-Farley D, Donaldson S, Comes O, Zuberi K, Badrawi R, and others. 2010. “The Gene Mania Prediction Server Biological Network Integration for Gene Prioritization and Predicting Gene Function.”