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 x 8
#> patient external_gene_n~ tumor_exp normal_exp miRNA total_read
#> <chr> <chr> <dbl> <dbl> <chr> <int>
#> 1 TCGA-E~ HIST1H3H 825 27 hsa-~ 193
#> 2 TCGA-E~ HIST1H3H 825 27 hsa-~ 7
#> 3 TCGA-E~ HIST1H3H 825 27 hsa-~ 3
#> 4 TCGA-E~ HIST1H3H 825 27 hsa-~ 450
#> 5 TCGA-E~ HIST1H3H 825 27 hsa-~ 1345
#> 6 TCGA-E~ HIST1H3H 825 27 hsa-~ 14
#> 7 TCGA-E~ HIST1H3H 825 27 hsa-~ 3
#> 8 TCGA-E~ HIST1H3H 825 27 hsa-~ 35
#> 9 TCGA-E~ HIST1H3H 825 27 hsa-~ 205
#> 10 TCGA-E~ HIST1H3H 825 27 hsa-~ 270
#> 11 TCGA-E~ HIST1H3H 825 27 hsa-~ 38
#> 12 TCGA-E~ HIST1H3H 825 27 hsa-~ 1
#> 13 TCGA-E~ HIST1H3H 825 27 hsa-~ 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 x 8
#> patient external_gene_n~ tumor_exp normal_exp miRNA total_read
#> <chr> <chr> <dbl> <dbl> <chr> <int>
#> 1 TCGA-E~ ACTB 191469 101917 hsa-~ 67599
#> 2 TCGA-E~ ACTB 191469 101917 hsa-~ 47266
#> 3 TCGA-E~ ACTB 191469 101917 hsa-~ 14554
#> 4 TCGA-E~ ACTB 191469 101917 hsa-~ 191
#> 5 TCGA-E~ ACTB 191469 101917 hsa-~ 5
#> 6 TCGA-E~ ACTB 191469 101917 hsa-~ 12625
#> 7 TCGA-E~ ACTB 191469 101917 hsa-~ 5297
#> 8 TCGA-E~ ACTB 191469 101917 hsa-~ 2379
#> 9 TCGA-E~ ACTB 191469 101917 hsa-~ 8041
#> 10 TCGA-E~ ACTB 191469 101917 hsa-~ 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 4.0.3 (2020-10-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 2012 R2 x64 (build 9600)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ceRNAnetsim_1.2.1 tidygraph_1.2.0 dplyr_1.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 pillar_1.4.7 compiler_4.0.3 viridis_0.5.1
#> [5] tools_4.0.3 digest_0.6.27 viridisLite_0.3.0 evaluate_0.14
#> [9] lifecycle_0.2.0 tibble_3.0.4 gtable_0.3.0 pkgconfig_2.0.3
#> [13] rlang_0.4.9 igraph_1.2.6 cli_2.2.0 ggrepel_0.8.2
#> [17] yaml_2.2.1 parallel_4.0.3 xfun_0.19 gridExtra_2.3
#> [21] furrr_0.2.1 stringr_1.4.0 knitr_1.30 graphlayouts_0.7.1
#> [25] generics_0.1.0 vctrs_0.3.5 globals_0.14.0 grid_4.0.3
#> [29] tidyselect_1.1.0 glue_1.4.2 listenv_0.8.0 R6_2.5.0
#> [33] ggraph_2.0.4 fansi_0.4.1 parallelly_1.21.0 rmarkdown_2.5
#> [37] polyclip_1.10-0 farver_2.0.3 tweenr_1.0.1 tidyr_1.1.2
#> [41] purrr_0.3.4 ggplot2_3.3.2 magrittr_2.0.1 MASS_7.3-53
#> [45] scales_1.1.1 codetools_0.2-18 ellipsis_0.3.1 htmltools_0.5.0
#> [49] assertthat_0.2.1 ggforce_0.3.2 colorspace_2.0-0 future_1.20.1
#> [53] utf8_1.1.4 stringi_1.5.3 munsell_0.5.0 crayon_1.3.4