Case Study n.1: Role of miRNAs in shared protein domains network in Prostate Cancer
In this case study, we downloaded shared protein domains network in Homo Sapiens, using SpidermiRquery, SpidermiRprepare, and SpidermiRdownload.
Then, we focused on role of miRNAs in this network. We integrated miRNA information using SpidermiRanalyze. We obtained a big network, and in order to understand the underlying biological process of a set of biomarker of interest (e.g. from DEG) we performed an analysis to identify their neighbor biomarkers in the shared protein domains network. Please see SpidermiR:Application Examples for more information.
SpidermiRvisualize was used to see the results.
a<-Case_Study1_loading_1_network(species)
b<-Case_Study1_loading_2_network(data=a)
c<-Case_Study1_loading_3_network(data=b,dataFilt=dataFilt,dataClin=dataClin)
d<-Case_Study1_loading_4_network(TERZA_NET=c)
Case Study n.2: miRNAs regulating degree centrality genes in physical interactions network in breast cancer
We studied in this case study the relationships between miRNAs with higher centrality and their target proteins with higher centrality. Please see SpidermiR:Application Examples for more information.
a2<-Case_Study2_loading_1_network(species)
b2<-Case_Study2_loading_2_network(data=a2)
c2<-Case_Study2_loading_3_network(sdas=a2,miRNA_NET=b2)
Session Information ******
## R version 3.3.3 (2017-03-06)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
##
## 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] visNetwork_1.0.3 networkD3_0.4 SpidermiR_1.4.8
## [4] miRNAtap_1.8.0 AnnotationDbi_1.36.2 IRanges_2.8.2
## [7] S4Vectors_0.12.2 Biobase_2.34.0 BiocGenerics_0.20.0
## [10] BiocStyle_2.2.1
##
## loaded via a namespace (and not attached):
## [1] proto_1.0.0 R.utils_2.5.0
## [3] RSQLite_1.1-2 htmlwidgets_0.8
## [5] grid_3.3.3 trimcluster_0.1-2
## [7] BiocParallel_1.8.2 devtools_1.12.0
## [9] DESeq_1.26.0 munsell_0.4.3
## [11] codetools_0.2-15 preprocessCore_1.36.0
## [13] chron_2.3-50 withr_1.0.2
## [15] colorspace_1.3-2 GOSemSim_2.0.4
## [17] BiocInstaller_1.24.0 highr_0.6
## [19] knitr_1.15.1 supraHex_1.12.0
## [21] robustbase_0.92-7 DOSE_3.0.10
## [23] pathview_1.14.0 labeling_0.3
## [25] KEGGgraph_1.32.0 KMsurv_0.1-5
## [27] mnormt_1.5-5 hwriter_1.3.2
## [29] rprojroot_1.2 downloader_0.4
## [31] c3net_1.1.1 ggthemes_3.4.0
## [33] EDASeq_2.8.0 diptest_0.75-7
## [35] R6_2.2.0 doParallel_1.0.10
## [37] GenomeInfoDb_1.10.3 locfit_1.5-9.1
## [39] flexmix_2.3-13 bitops_1.0-6
## [41] fgsea_1.0.2 assertthat_0.1
## [43] scales_0.4.1 nnet_7.3-12
## [45] gtable_0.2.0 affy_1.52.0
## [47] genefilter_1.56.0 cmprsk_2.2-7
## [49] GlobalOptions_0.0.11 splines_3.3.3
## [51] rtracklayer_1.34.2 lazyeval_0.2.0
## [53] hexbin_1.27.1 selectr_0.3-1
## [55] broom_0.4.2 yaml_2.1.14
## [57] reshape2_1.4.2 GenomicFeatures_1.26.4
## [59] backports_1.0.5 qvalue_2.6.0
## [61] clusterProfiler_3.2.14 tools_3.3.3
## [63] psych_1.7.3.21 ggplot2_2.2.1
## [65] affyio_1.44.0 gplots_3.0.1
## [67] RColorBrewer_1.1-2 gsubfn_0.6-6
## [69] Rcpp_0.12.10 plyr_1.8.4
## [71] zlibbioc_1.20.0 RCurl_1.95-4.8
## [73] ggpubr_0.1.2 sqldf_0.4-10
## [75] GetoptLong_0.1.6 viridis_0.4.0
## [77] zoo_1.7-14 SummarizedExperiment_1.4.0
## [79] ggrepel_0.6.5 cluster_2.0.6
## [81] magrittr_1.5 data.table_1.10.4
## [83] dnet_1.0.10 DO.db_2.9
## [85] circlize_0.3.10 survminer_0.3.1
## [87] mvtnorm_1.0-6 whisker_0.3-2
## [89] matrixStats_0.52.1 aroma.light_3.4.0
## [91] hms_0.3 evaluate_0.10
## [93] xtable_1.8-2 minet_3.32.0
## [95] XML_3.98-1.6 mclust_5.2.3
## [97] gridExtra_2.2.1 shape_1.4.2
## [99] testthat_1.0.2 biomaRt_2.30.0
## [101] tibble_1.3.0 crayon_1.3.2
## [103] KernSmooth_2.23-15 R.oo_1.21.0
## [105] htmltools_0.3.5 tidyr_0.6.1
## [107] geneplotter_1.52.0 DBI_0.6-1
## [109] matlab_1.0.2 ComplexHeatmap_1.12.0
## [111] MASS_7.3-45 fpc_2.1-10
## [113] ShortRead_1.32.1 Matrix_1.2-8
## [115] readr_1.1.0 parmigene_1.0.2
## [117] R.methodsS3_1.7.1 gdata_2.17.0
## [119] igraph_1.0.1 km.ci_0.5-2
## [121] GenomicRanges_1.26.4 GenomicAlignments_1.10.1
## [123] foreign_0.8-67 xml2_1.1.1
## [125] roxygen2_6.0.1 foreach_1.4.3
## [127] annotate_1.52.1 XVector_0.14.1
## [129] rvest_0.3.2 stringr_1.2.0
## [131] digest_0.6.12 ConsensusClusterPlus_1.38.0
## [133] graph_1.52.0 Biostrings_2.42.1
## [135] rmarkdown_1.4 fastmatch_1.1-0
## [137] survMisc_0.5.4 TCGAbiolinks_2.2.10
## [139] dendextend_1.5.2 edgeR_3.16.5
## [141] miRNAtap.db_0.99.10 curl_2.4
## [143] kernlab_0.9-25 Rsamtools_1.26.2
## [145] gtools_3.5.0 commonmark_1.2
## [147] modeltools_0.2-21 rjson_0.2.15
## [149] nlme_3.1-131 jsonlite_1.4
## [151] viridisLite_0.2.0 limma_3.30.13
## [153] lattice_0.20-35 KEGGREST_1.14.1
## [155] httr_1.2.1 DEoptimR_1.0-8
## [157] survival_2.41-3 GO.db_3.4.0
## [159] png_0.1-7 prabclus_2.2-6
## [161] iterators_1.0.8 Rgraphviz_2.18.0
## [163] class_7.3-14 stringi_1.1.5
## [165] org.Hs.eg.db_3.4.0 latticeExtra_0.6-28
## [167] caTools_1.17.1 memoise_1.0.0
## [169] dplyr_0.5.0 ape_4.1
