09.2-epimachinery-ceRNA-network
================
Kathleen Durkin
2026-05-07

- [Apul](#apul)
- [Peve](#peve)
- [Ptuh](#ptuh)

For all 3 species, we have putative ceRNA modules, involving lncRNAs
which putatively sequester miRNAs from repressing their mRNA targets. As
an added layer of complexity, these de-repressed mRNA targets may encode
protein machinery relevant to other epigenetic processes! This is what
we’ll check for in this script.

Load libraries

``` r
library(dplyr)
```

    ## 
    ## Attaching package: 'dplyr'

    ## The following objects are masked from 'package:stats':
    ## 
    ##     filter, lag

    ## The following objects are masked from 'package:base':
    ## 
    ##     intersect, setdiff, setequal, union

Load data

``` r
Apul_ceRNA <- read.delim("../../D-Apul/output/33.1-Apul-ceRNA-network/pos_lncRNA_mRNA_hits.txt", header=TRUE, sep="\t")

Peve_ceRNA <- read.delim("../../E-Peve/output/31.1-Peve-ceRNA-network/pos_lncRNA_mRNA_hits.txt", header=TRUE, sep="\t")

Ptuh_ceRNA <- read.delim("../../F-Ptuh/output/31.1-Ptuh-ceRNA-network/pos_lncRNA_mRNA_hits.txt", header=TRUE, sep="\t")

# Since any ceRNA network requires the miRNA to also be putatively targeting the 
# derepressed mRNA, can just use the miRNA-epimachinery targeting results, instead 
# of the full set of epimachinery transcripts across all species. This is nicer, bc 
# the miRNA-epimachinery targeting results already include functional categorization 
# of the epimachinery proteins.

epi_targeting <- read.csv("../output/12-miRNA-epimachinery/miRNAtargets_mach.csv")
```

## Apul

bind to identify ceRNA sets involving epimachinery transcripts

``` r
Apul_epi <- left_join(Apul_ceRNA, epi_targeting, by = c("mRNA" = "target"))
Apul_epi <- Apul_epi %>% filter(!is.na(category))

cat("# distinct epimachinery transcripts involved in the ceRNA network:", length(unique(Apul_epi$mRNA)))
```

    ## # distinct epimachinery transcripts involved in the ceRNA network: 8

``` r
cat("\nFunctional categories represented:", unique(Apul_epi$category))
```

    ## 
    ## Functional categories represented: Chromatin signaling Ubiquitin signaling RNA modification ncRNA biogenesis & silencing

Summarize

``` r
Apul_epi %>%
  group_by(miRNA, mRNA, gene, type, category) %>%
  summarise(
    n_lncRNA_sponges   = n_distinct(lncRNA),
    .groups = "drop"
  ) %>%
  dplyr::select(-type, -mRNA) %>%
  arrange(desc(n_lncRNA_sponges))
```

    ## # A tibble: 8 × 4
    ##   miRNA                                gene            category n_lncRNA_sponges
    ##   <chr>                                <chr>           <chr>               <int>
    ## 1 apul-mir-novel-29                    PPP1R7-201      Chromat…               22
    ## 2 apul-mir-100                         TRMT1-202       RNA mod…               11
    ## 3 apul-mir-100                         USP31-201; USP… Ubiquit…               10
    ## 4 apul-mir-2022                        PSMD14-201      Ubiquit…                3
    ## 5 apul-mir-novel-10                    UBE4A-201       Ubiquit…                3
    ## 6 apul-mir-novel-28                    RDRP            ncRNA b…                3
    ## 7 apul-mir-novel-4                     PPP1CB-203      Chromat…                3
    ## 8 apul-mir-novel-8a; apul-mir-novel-8b JOSD1-210; JOS… Ubiquit…                1

## Peve

bind to identify ceRNA sets involving epimachinery transcripts

``` r
# Need to remove the "gene-" prefix that's currently appended in front of the mRNA names
Peve_ceRNA$mRNA <- sub("gene-", "", Peve_ceRNA$mRNA)

Peve_epi <- left_join(Peve_ceRNA, epi_targeting, by = c("mRNA" = "target"))
```

    ## Warning in left_join(Peve_ceRNA, epi_targeting, by = c(mRNA = "target")): Detected an unexpected many-to-many relationship between `x` and `y`.
    ## ℹ Row 318 of `x` matches multiple rows in `y`.
    ## ℹ Row 46 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.

``` r
Peve_epi <- Peve_epi %>% filter(!is.na(category))

cat("# distinct epimachinery transcripts involved in the ceRNA network:", length(unique(Peve_epi$mRNA)))
```

    ## # distinct epimachinery transcripts involved in the ceRNA network: 8

``` r
cat("\nFunctional categories represented:", unique(Peve_epi$category))
```

    ## 
    ## Functional categories represented: Ubiquitin signaling Histone modification ncRNA biogenesis & silencing

Summarize

``` r
Peve_epi %>%
  group_by(miRNA, mRNA, gene, type, category) %>%
  summarise(
    n_lncRNA_sponges   = n_distinct(lncRNA),
    .groups = "drop"
  ) %>%
  dplyr::select(-type, -mRNA) %>%
  arrange(desc(n_lncRNA_sponges))
```

    ## # A tibble: 8 × 4
    ##   miRNA            gene                               category  n_lncRNA_sponges
    ##   <chr>            <chr>                              <chr>                <int>
    ## 1 peve-mir-novel-2 UBE2D1-201; UBE2D2-202; UBE2D3-210 Ubiquiti…                3
    ## 2 peve-mir-novel-6 UBR2-202                           Ubiquiti…                3
    ## 3 peve-mir-novel-6 Riox1-201                          Histone …                3
    ## 4 peve-mir-novel-2 CUL4A-202                          Ubiquiti…                1
    ## 5 peve-mir-novel-2 SRRT                               ncRNA bi…                1
    ## 6 peve-mir-novel-2 ZFC3H1                             ncRNA bi…                1
    ## 7 peve-mir-novel-2 MCM3AP-201                         ncRNA bi…                1
    ## 8 peve-mir-novel-7 INTS4                              ncRNA bi…                1

## Ptuh

bind to identify ceRNA sets involving epimachinery transcripts

``` r
# Need to remove the "gene-" prefix that's currently appended in front of the mRNA names
Ptuh_ceRNA$mRNA <- sub("gene-", "", Ptuh_ceRNA$mRNA)

Ptuh_epi <- left_join(Ptuh_ceRNA, epi_targeting, by = c("mRNA" = "target"))
Ptuh_epi <- Ptuh_epi %>% filter(!is.na(category))

cat("# distinct epimachinery transcripts involved in the ceRNA network:", length(unique(Ptuh_epi$mRNA)))
```

    ## # distinct epimachinery transcripts involved in the ceRNA network: 1

``` r
cat("\nFunctional categories represented:", unique(Ptuh_epi$category))
```

    ## 
    ## Functional categories represented: ncRNA biogenesis & silencing