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_mirnagene
#> Warning in inner_join(., TCGA_E9_A1N5_normal, by = c(Target = "external_gene_name")): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 3405 of `x` matches multiple rows in `y`.
#> ℹ Row 842 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.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== "HIST1H3H")
#> Warning in inner_join(., TCGA_E9_A1N5_normal, by = "external_gene_name"): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 361 of `x` matches multiple rows in `y`.
#> ℹ Row 17044 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.
#> Warning in inner_join(., TCGA_E9_A1N5_mirnagene, by = c(external_gene_name = "Target")): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 3362 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.
#> # 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
#> # ℹ 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")
#> Warning in inner_join(., TCGA_E9_A1N5_normal, by = "external_gene_name"): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 361 of `x` matches multiple rows in `y`.
#> ℹ Row 17044 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.
#> Warning in inner_join(., TCGA_E9_A1N5_mirnagene, by = c(external_gene_name = "Target")): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 3362 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.
#> # 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
#> # ℹ 36 more rows
#> # ℹ 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 4.4.2 (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
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> 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
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ceRNAnetsim_1.19.0 tidygraph_1.3.1 dplyr_1.1.4 rmarkdown_2.29
#>
#> loaded via a namespace (and not attached):
#> [1] viridis_0.6.5 sass_0.4.9 utf8_1.2.4 future_1.34.0
#> [5] generics_0.1.3 tidyr_1.3.1 listenv_0.9.1 digest_0.6.37
#> [9] magrittr_2.0.3 evaluate_1.0.1 grid_4.4.2 fastmap_1.2.0
#> [13] jsonlite_1.8.9 ggrepel_0.9.6 gridExtra_2.3 purrr_1.0.2
#> [17] fansi_1.0.6 viridisLite_0.4.2 scales_1.3.0 tweenr_2.0.3
#> [21] codetools_0.2-20 jquerylib_0.1.4 cli_3.6.3 graphlayouts_1.2.1
#> [25] rlang_1.1.4 polyclip_1.10-7 parallelly_1.39.0 munsell_0.5.1
#> [29] withr_3.0.2 cachem_1.1.0 yaml_2.3.10 tools_4.4.2
#> [33] parallel_4.4.2 memoise_2.0.1 colorspace_2.1-1 ggplot2_3.5.1
#> [37] globals_0.16.3 buildtools_1.0.0 vctrs_0.6.5 R6_2.5.1
#> [41] lifecycle_1.0.4 MASS_7.3-61 ggraph_2.2.1 furrr_0.3.1
#> [45] pkgconfig_2.0.3 pillar_1.9.0 bslib_0.8.0 gtable_0.3.6
#> [49] Rcpp_1.0.13-1 glue_1.8.0 ggforce_0.4.2 xfun_0.49
#> [53] tibble_3.2.1 tidyselect_1.2.1 sys_3.4.3 knitr_1.49
#> [57] farver_2.1.2 htmltools_0.5.8.1 igraph_2.1.1 maketools_1.3.1
#> [61] compiler_4.4.2