# The auxiliary commands which can help to the users

#### Selcen Ari

#### 2022-04-26

    library(ceRNAnetsim)

# 1. Introduction

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.

# 2. Installation

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("ceRNAnetsim")

# 3. Selection of perturbing element from dataset

    data("TCGA_E9_A1N5_tumor")
    data("TCGA_E9_A1N5_normal")
    data("mirtarbasegene")
    data("TCGA_E9_A1N5_mirnanormal")

## 3.1. Selection of HIST1H3H gene at vignette How does the system behave in mirtarbase dataset without interaction factors?

    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

    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")
    #> # 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.

## 3.2. Selection of ACTB gene at vignette How does the system behave in mirtarbase dataset without interaction factors?

    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.

## 4. Determination of ACTB gene perturbation efficiency with different expression level changes

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_mirnagene

We 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.

    TCGA_E9_A1N5_mirnagene %>%
      priming_graph(competing_count = gene_expression,
                    miRNA_count = total_read)%>%
      calc_perturbation(node_name= "ACTB", cycle=10, how= 30,limit = 0.1)

# 5. Session Info

    sessionInfo()
    #> R version 4.2.0 RC (2022-04-19 r82224 ucrt)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows Server x64 (build 20348)
    #>
    #> Matrix products: default
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] ceRNAnetsim_1.8.0 tidygraph_1.2.1   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_0.4.1
    #>  [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_1.25.0
    #> [25] codetools_0.2-18   evaluate_0.15      knitr_1.38         fastmap_1.1.0
    #> [29] parallel_4.2.0     fansi_1.0.3        furrr_0.2.3        Rcpp_1.0.8.3
    #> [33] scales_1.2.0       jsonlite_1.8.0     farver_2.1.0       parallelly_1.31.1
    #> [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_4.2.0
    #> [45] cli_3.3.0          tools_4.2.0        magrittr_2.0.3     sass_0.4.1
    #> [49] tibble_3.1.6       ggraph_2.0.5       crayon_1.5.1       tidyr_1.2.0
    #> [53] pkgconfig_2.0.3    ellipsis_0.3.2     MASS_7.3-57        viridis_0.6.2
    #> [57] assertthat_0.2.1   rmarkdown_2.14     R6_2.5.1           globals_0.14.0
    #> [61] igraph_1.3.1       compiler_4.2.0