In the other package vignettes, usage of ceRNAnetsim is explained in details. But in this vignette, some of commands which facitate to use of other vignettes.
data("TCGA_E9_A1N5_tumor")
data("TCGA_E9_A1N5_normal")
data("mirtarbasegene")
data("TCGA_E9_A1N5_mirnanormal")TCGA_E9_A1N5_mirnanormal %>%
inner_join(mirtarbasegene, by= "miRNA") %>%
inner_join(TCGA_E9_A1N5_normal,
by = c("Target"= "external_gene_name")) %>%
select(Target, miRNA, total_read, gene_expression) %>%
distinct() -> TCGA_E9_A1N5_mirnageneTCGA_E9_A1N5_tumor%>%
inner_join(TCGA_E9_A1N5_normal, by= "external_gene_name")%>%
select(patient = patient.x,
external_gene_name,
tumor_exp = gene_expression.x,
normal_exp = gene_expression.y)%>%
distinct()%>%
inner_join(TCGA_E9_A1N5_mirnagene, by = c("external_gene_name"= "Target"))%>%
filter(tumor_exp != 0, normal_exp != 0)%>%
mutate(FC= tumor_exp/normal_exp)%>%
filter(external_gene_name== "HIST1H3H")
#> # A tibble: 13 × 8
#> patient external_gene_name tumor_exp normal_exp miRNA total_read
#> <chr> <chr> <dbl> <dbl> <chr> <int>
#> 1 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-193b… 193
#> 2 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-299-… 7
#> 3 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-34a-… 3
#> 4 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-34a-… 450
#> 5 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-378a… 1345
#> 6 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-379-… 14
#> 7 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-380-… 3
#> 8 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-411-… 35
#> 9 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-484 205
#> 10 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-497-… 270
#> 11 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-503-… 38
#> 12 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-6793… 1
#> 13 TCGA-E9-A1N5 HIST1H3H 825 27 hsa-miR-760 4
#> # … with 2 more variables: gene_expression <dbl>, FC <dbl>
#HIST1H3H: interacts with various miRNA in dataset, so we can say that HIST1H3H is non-isolated competing element and increases to 30-fold.TCGA_E9_A1N5_tumor%>%
inner_join(TCGA_E9_A1N5_normal, by= "external_gene_name") %>%
select(patient = patient.x,
external_gene_name,
tumor_exp = gene_expression.x,
normal_exp = gene_expression.y) %>%
distinct() %>%
inner_join(TCGA_E9_A1N5_mirnagene,
by = c("external_gene_name"= "Target")) %>%
filter(tumor_exp != 0, normal_exp != 0) %>%
mutate(FC= tumor_exp/normal_exp) %>%
filter(external_gene_name == "ACTB")
#> # A tibble: 46 × 8
#> patient external_gene_name tumor_exp normal_exp miRNA total_read
#> <chr> <chr> <dbl> <dbl> <chr> <int>
#> 1 TCGA-E9-A1N5 ACTB 191469 101917 hsa-let-7a-5p 67599
#> 2 TCGA-E9-A1N5 ACTB 191469 101917 hsa-let-7b-5p 47266
#> 3 TCGA-E9-A1N5 ACTB 191469 101917 hsa-let-7c-5p 14554
#> 4 TCGA-E9-A1N5 ACTB 191469 101917 hsa-let-7i-3p 191
#> 5 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-1-3p 5
#> 6 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-100-… 12625
#> 7 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-127-… 5297
#> 8 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-1307… 2379
#> 9 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-145-… 8041
#> 10 TCGA-E9-A1N5 ACTB 191469 101917 hsa-miR-16-5p 1522
#> # … with 36 more rows, and 2 more variables: gene_expression <dbl>, FC <dbl>
#ACTB: interacts with various miRNA in dataset, so ACTB is not isolated node in network and increases to 1.87-fold.Firstly, clean dataset as individual gene has one expression value. And then filter genes which have expression values greater than 10.
TCGA_E9_A1N5_mirnagene %>%
group_by(Target) %>%
mutate(gene_expression= max(gene_expression)) %>%
distinct() %>%
ungroup() -> TCGA_E9_A1N5_mirnagene
TCGA_E9_A1N5_mirnagene%>%
filter(gene_expression > 10)->TCGA_E9_A1N5_mirnageneWe can determine perturbation efficiency of an element on entire network as following:
TCGA_E9_A1N5_mirnagene %>%
priming_graph(competing_count = gene_expression,
miRNA_count = total_read)%>%
calc_perturbation(node_name= "ACTB", cycle=10, how= 1.87,limit = 0.1)On the other hand, the perturbation eficiency of ATCB gene is higher, when this gene is regulated with 30-fold upregulation like in HIST1H3H.
sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
#>
#> 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=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ceRNAnetsim_1.6.99 tidygraph_1.2.0 dplyr_1.0.8
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.1.2 xfun_0.30 bslib_0.3.1 graphlayouts_0.8.0
#> [5] purrr_0.3.4 listenv_0.8.0 colorspace_2.0-3 vctrs_0.3.8
#> [9] generics_0.1.2 viridisLite_0.4.0 htmltools_0.5.2 yaml_2.3.5
#> [13] utf8_1.2.2 rlang_1.0.2 jquerylib_0.1.4 pillar_1.7.0
#> [17] glue_1.6.2 DBI_1.1.2 tweenr_1.0.2 lifecycle_1.0.1
#> [21] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 future_1.24.0
#> [25] codetools_0.2-18 evaluate_0.15 knitr_1.37 fastmap_1.1.0
#> [29] parallel_4.1.2 fansi_1.0.2 furrr_0.2.3 Rcpp_1.0.8
#> [33] scales_1.1.1 jsonlite_1.8.0 farver_2.1.0 parallelly_1.30.0
#> [37] gridExtra_2.3 ggforce_0.3.3 ggplot2_3.3.5 digest_0.6.29
#> [41] stringi_1.7.6 ggrepel_0.9.1 polyclip_1.10-0 grid_4.1.2
#> [45] cli_3.2.0 tools_4.1.2 magrittr_2.0.2 sass_0.4.0
#> [49] tibble_3.1.6 ggraph_2.0.5 crayon_1.5.0 tidyr_1.2.0
#> [53] pkgconfig_2.0.3 ellipsis_0.3.2 MASS_7.3-55 viridis_0.6.2
#> [57] assertthat_0.2.1 rmarkdown_2.12 R6_2.5.1 globals_0.14.0
#> [61] igraph_1.2.11 compiler_4.1.2