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functional_predictions_all_circRNAs.Rmd
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functional_predictions_all_circRNAs.Rmd
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---
title: "CRAFT functional predictions"
subtitle: "Overview of circRNA functional predictions for miRNA and RBP binding sites, and coding potential"
author: "Please cite: _Dal Molin et al. CRAFT: a bioinformatics software for custom prediction of circular RNA functions_"
date: "`r Sys.Date()`"
header-includes:
\usepackage{caption}
output:
rmdformats::readthedown:
html_document:
fig_caption: yes
number_sections: yes
toc: yes
toc_float: yes
pdf_document:
toc: yes
editor_options:
chunk_output_type: inline
params:
l: 50000
QUANTILE1: "FALSE"
thr1: 0.95
score_miRNA: 80
energy_miRNA: -15
QUANTILE2: "FALSE"
thr2: 0.95
dGduplex_miRNA: -15
dGopen_miRNA: -15
QUANTILE3: "FALSE"
thr3: 0.9
voteFrac_RBP: 0.15
orgdb: "org.Hs.eg.db"
symbol2eg: "org.Hs.egSYMBOL2EG"
eg2uniprot: "org.Hs.egUNIPROT"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE, dpi = 600)
```
```{r libraries, echo=FALSE, include=FALSE}
.libPaths( c( '/data/.R/lib', .libPaths()) )
# su "/usr/local/lib/R/site-library":
library("knitr")
library(BiocManager)
library(data.table)
library(devtools)
library(dplyr)
library(DT)
library(ggplot2)
library(gridExtra)
library(gtable)
library(igraph)
library(lattice)
library(pheatmap)
library(RColorBrewer)
library(reshape2)
library(rmdformats)
library(tibble)
library(viridis)
# su "/usr/lib/R/library":
if (!dir.exists("/data/.R/lib/grid")) {
BiocManager::install("grid")
}
library(grid)
# su "/usr/lib/R/site-library":
#devtools::install_github("kassambara/easyGgplot2")
if (!dir.exists("/data/.R/lib/easyGgplot2")) {
withr::with_libpaths(new="/data/.R/lib", devtools::install_github("kassambara/easyGgplot2") )
}
library(easyGgplot2)
library(multiMiR)
```
```{r input, echo=FALSE, include=FALSE}
## Upload input files
# file of parameters
file_parameters <- "/data/params_R.txt"
parameters <- read.table(file_parameters, header=F)
parameters <- as.data.table(parameters)
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
## miRNA prediction
# miRanda
file_miRanda <- "/data/functional_predictions/miRNA_detection/miRanda/output_miRanda_per_R.txt"
miRanda_pred <- read.table(file_miRanda, header=T, sep="\t")
miRanda_pred <- as.data.table(miRanda_pred)
# PITA
file_PITA <- "/data/functional_predictions/miRNA_detection/PITA/pred_pita_results_per_R.txt"
PITA_pred <- read.table(file_PITA, header=T, sep="\t")
PITA_pred <- as.data.table(PITA_pred)
}
if (parameters[1] == "R" | parameters[1] == "MR" | parameters[1] == "RO" | parameters[1] == "MRO") {
# RBP predictions
file_beRBP <- "/data/functional_predictions/RBP_detection/beRBP/analysis_RBP/resultMatrix_b.txt"
beRBP_pred <- read.table(file_beRBP, header=T, sep="\t")
beRBP_pred <- as.data.table(beRBP_pred)
}
if (parameters[1] == "O" | parameters[1] == "MO" | parameters[1] == "RO" | parameters[1] == "MRO") {
# ORF prediction
file_ORFfinder <- "/data/functional_predictions/ORF_detection/ORFfinder/ORF_backsplice.txt"
ORFfinder_pred <- read.table(file_ORFfinder, header=T, sep="\t")
ORFfinder_pred <- as.data.table(ORFfinder_pred)
file_openORF <- "/data/functional_predictions/ORF_detection/ORFfinder/ORF_backsplice_open.txt"
openORF <- read.table(file_openORF, header=T, sep="\t")
openORF <- as.data.table(openORF)
}
# circRNA length
file_circ_length <- "/data/functional_predictions/backsplice_circRNA_length_1.txt"
circ_length <- read.table(file_circ_length, header=T, sep="\t")
circ_length <- as.data.table(circ_length)
# gene names
file_gene_names <- "/data/functional_predictions/backsplice_gene_name.txt"
gene_names <- read.table(file_gene_names, header=T, sep="\t")
gene_names <- as.data.table(gene_names)
```
```{r output, echo=FALSE, include=FALSE}
output_dir <- "/data/graphical_output/general/"
```
```{r, echo=FALSE, include=TRUE}
htmltools::img(src = knitr::image_uri("/input/CRAFT_logo.png"),
alt = 'logo',
style = 'position:absolute; top: -25px; right: 700px; padding:12px; height:160px; width:160px')
```
```{r, echo=FALSE, include=FALSE}
# remove circRNAs with length > "l"
circ_l <- circ_length[length < params$l, circ_id]
```
Number of circRNAs analyzed: `r length(circ_l)`
```{r info_circ_length, echo=FALSE, include=TRUE}
setkey(gene_names, circ_id)
setkey(circ_length, circ_id)
circ_info <- merge(gene_names, circ_length)
datatable(data = circ_info,
rownames = F,
colnames = c("CircRNA", "Gene name", "CircRNA length"),
style = "bootstrap",
class = "compact display",
fillContainer = F,
autoHideNavigation = T,
filter = "top",
escape = FALSE,
extensions = c('ColReorder', 'Buttons', 'KeyTable'),
options = list(colReorder = TRUE,
searching = TRUE,
pageLength = 10,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
keys = TRUE
)
)
```
# MiRNA binding sites prediction
```{r print_0, echo=FALSE, include=FALSE}
print_0 <- "Analysis not performed."
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
print_0 <- ""
}
```
`r print_0`
## Method statistics
```{r, echo=FALSE, include=FALSE}
### miRNA predictions
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
## miRanda
# remove circRNAs with length > "l"
miRanda_pred_l <- miRanda_pred[circ_id %in% circ_l,]
# filter on "score" ed "energy"
if (params$QUANTILE1 == "TRUE") {
score_miRNA <- quantile(miRanda_pred_l$score, params$thr1)
energy_miRNA <- quantile(miRanda_pred_l$energy, 1-params$thr1)
} else {
score_miRNA <- params$score_miRNA
energy_miRNA <- params$energy_miRNA
}
miRanda_pred_l_sel <- miRanda_pred_l[(score > score_miRNA & energy < energy_miRNA),]
}
```
```{r, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
## PITA
# remove circRNAs with length > "l"
PITA_pred_l <- PITA_pred[circ_id %in% circ_l,]
# filter on "dGduplex" and "dGopen"
if (params$QUANTILE2 == "TRUE") {
dGduplex_miRNA <- quantile(PITA_pred_l$dGduplex, 1-params$thr2)
dGopen_miRNA <- quantile(PITA_pred_l$dGopen, params$thr2)
} else {
dGduplex_miRNA <- params$dGduplex_miRNA
dGopen_miRNA <- params$dGopen_miRNA
}
PITA_pred_l_sel <- PITA_pred_l[(dGduplex < dGduplex_miRNA & dGopen > dGopen_miRNA),]
}
```
```{r, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
# merge predictions of miRanda and PITA
miRanda_pred_l_sel$circ_miRNA_end_id <- paste0(miRanda_pred_l_sel$circ_id, "__", miRanda_pred_l_sel$miRNA_id, "__", miRanda_pred_l_sel$circ_end)
PITA_pred_l_sel$circ_miRNA_end_id <- paste0(PITA_pred_l_sel$circ_id, "__", PITA_pred_l_sel$miRNA_id, "__", PITA_pred_l_sel$circ_end)
miRNA_pred <- miRanda_pred_l_sel[which(miRanda_pred_l_sel$circ_miRNA_end_id %in% PITA_pred_l_sel$circ_miRNA_end_id), c("circ_id", "miRNA_id", "circ_end")]
miRNA_pred$putative_circ_start <- miRNA_pred$circ_end - 22
miRNA_pred <- data.table("circ_id" = miRNA_pred$circ_id, "miRNA_id" = miRNA_pred$miRNA_id, "putative_circ_start" = miRNA_pred$putative_circ_start, "circ_end" = miRNA_pred$circ_end)
} else {
score_miRNA <- params$score_miRNA
energy_miRNA <- params$energy_miRNA
miRanda_pred_l_sel <- data.table()
dGduplex_miRNA <- params$dGduplex_miRNA
dGopen_miRNA <- params$dGopen_miRNA
PITA_pred_l_sel <- data.table()
miRNA_pred <- data.table()
}
```
```{r print_1a, echo=FALSE, include=FALSE}
print_1a <- ""
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
print_1a <- paste0("miRanda thresholds: score > ", score_miRNA, ", energy < ", energy_miRNA)
}
```
`r print_1a`
| | miRanda |
|----------------------------------------|----------------------------- |
| Number of miRNA binding over threshold | `r miRanda_pred_l_sel[, .N]` |
```{r print_1b, echo=FALSE, include=FALSE}
print_1b <- ""
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
print_1b <- paste0("PITA thresholds: dGduplex < ", dGduplex_miRNA, ", dGopen > ", dGopen_miRNA)
}
```
`r print_1b`
| | PITA |
|----------------------------------------|-------------------------- |
| Number of miRNA binding over threshold | `r PITA_pred_l_sel[, .N]` |
Number of common miRNA binding sites: `r miRNA_pred[, .N]`.
## MiRNA binding sites per circRNA
```{r pre_barplot_1_miRNA, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
setkey(miRNA_pred, circ_id)
setkey(gene_names, circ_id)
miRNA_pred <- miRNA_pred[gene_names, nomatch = 0]
miRNA_pred$gene_circ_names <- paste0("circ", miRNA_pred$gene_names, "_", miRNA_pred$circ_id)
bar1a <- miRNA_pred[, .N, by = gene_circ_names]
w <- bar1a[, .N]
w <- w/2
if (w < 5) {
w <- 5
}
}
```
```{r barplot_1_miRNA, echo=FALSE, include=TRUE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
# barplot of the total number of detected miRNAs in each circRNA
bar_plot1 <- ggplot(bar1a,
aes(x = gene_circ_names, y = N, fill = gene_circ_names)
) +
geom_bar(stat = "identity") +
geom_text(aes(label = N),
vjust = -0.3,
size = 3.5) +
scale_fill_viridis(discrete = TRUE) +
xlab("CircRNA") +
ylab("Number of miRNA binding sites") +
ggtitle(paste0("Overall number of miRNA binding sites per circRNA")) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
text = element_text(size=10),
axis.text.x = element_text(angle=70, hjust=1),
legend.position = "none")
print(bar_plot1)
# Save at png
ggsave(bar_plot1, file=paste0(output_dir, "Overall_number_of_miRNA_binding_sites_per_circRNA.png"), width=w, height=7, dpi=600, limitsize = TRUE, type = "cairo")
}
```
```{r datatable_circ_miRNA_length, echo=FALSE, include=TRUE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
bar1a$gene_names <- gsub("circ", "", bar1a$gene_circ_names)
bar1a$gene_names <- gsub("_.*$", "", bar1a$gene_names)
bar1a$circ_id <- gsub("^.*_", "", bar1a$gene_circ_names)
bar1a2 <- miRNA_pred[, .(length(unique(miRNA_id))), by = gene_circ_names]
setkey(bar1a, gene_circ_names)
setkey(bar1a2, gene_circ_names)
bar1a <- bar1a[bar1a2, nomatch = 0]
colnames(bar1a)[5] <- "N_unique"
setkey(bar1a, circ_id)
setkey(circ_length, circ_id)
table1a <- merge(bar1a, circ_length)
table1a <- table1a[, c(1,4,3,5,6)]
datatable(data = table1a,
rownames = F,
colnames = c("CircRNA", "Gene name", "Number of miRNA binding sites", "Number of miRNAs", "CircRNA length"),
style = "bootstrap",
class = "compact display",
fillContainer = F,
autoHideNavigation = T,
filter = "top",
escape = FALSE,
extensions = c('ColReorder', 'Buttons', 'KeyTable'),
options = list(colReorder = TRUE,
searching = TRUE,
pageLength = 10,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
keys = TRUE
)
)
}
```
```{r density_miRNA, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
bar1b <- miRNA_pred[, .N, by = .(circ_id, miRNA_id)]
setkey(bar1b, circ_id)
setkey(circ_length, circ_id)
bar1c <- bar1b[circ_length, nomatch = 0]
colnames(bar1c)[3] <- "num_siti"
bar1c <- data.table(bar1c, "density" = bar1c$num_siti/bar1c$length*100)
} else {
miRNA_pred <- data.table(miRNA_id = integer())
}
```
Number of different identified miRNAs: `r length(unique(miRNA_pred$miRNA_id))`.
```{r pre_heatmap_num, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
setkey(bar1b, circ_id)
setkey(gene_names, circ_id)
bar1b <- bar1b[gene_names, nomatch = 0]
bar1b$gene_circ_names <- paste0("circ", bar1b$gene_names, "_", bar1b$circ_id)
if (dim(bar1b)[1] > 0) {
num_miRNA_per_circRNA <- as.data.table(dcast(bar1b, miRNA_id ~ gene_circ_names, value.var = "N", fill = 0))
}
# pre heatmaps
# input
if (dim(bar1b)[1] > 0) {
test <- num_miRNA_per_circRNA[, !"miRNA_id"]
rownames(test) <- num_miRNA_per_circRNA$miRNA_id
#print only common miRNAs
test$fun <- apply(test, 1, function(x) length(which(as.integer(x)>0)))
test <- tibble::rownames_to_column(test, "miRNA_id")
test <- test[fun >= 2,]
test <- test[, -"fun"]
test <- tibble::column_to_rownames(test, "miRNA_id")
w <- dim(test)[2]/1.67
if (w < 5) {
w <- 5
}
h <- dim(test)[1]/5
if (h < 5) {
h <- 5
}
}
}
```
```{r canberra_heatmap_num, echo=FALSE, include=FALSE}
# heatmap
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
if (dim(test)[1]>=2 & dim(test)[1]<=500 & dim(test)[2]>=2 & dim(test)[2]<=30) {
out <- 0
i <- 1
while (out == 0) {
brks <- quantile(test[,i])
if (length(brks) != length(unique(brks))) {
out <- 1
}
i <- i+1
}
if (out == 0) {
dist = "canberra"
canb_heatmap <- pheatmap(mat = test,
clustering_distance_rows = dist,
clustering_distance_cols = dist,
color = colorRampPalette(c("white","#1565C0"))(1000),
cellheight = 10, cellwidth = 20, border_color="gray",
treeheight_row = 20, treeheight_col = 20,
main = paste0("MiRNA binding sites shared by circRNAs")
)
#print(canb_heatmap)
# Save at png
ggsave(canb_heatmap, file=paste0(output_dir, "Heatmap_number_miRNA_binding_sites_per_circRNA.png"), width=w, height=h, dpi=600, limitsize = FALSE, type="cairo")
}
}
}
}
```
```{r, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
dt6 <- tibble::rownames_to_column(test, "miRNA_id")
dt6 <- melt(dt6, id.vars = "miRNA_id", variable.name = "gene_circ_names", value.name = "count")
dt6 <- as.data.table(dt6)
dt6 <- dt6[count>0,]
dt6$circ_id <- gsub("^.*_", "", dt6$gene_circ_names)
#dt6$gene_circ_names <- paste0("<a href=\"", "/data/graphical_output/",dt6$circ_id, "/functional_predictions_", dt6$circ_id, ".html", "\" target=\"_blank\">", dt6$gene_circ_names, "</a>")
dt6 <- dt6[, .(list(as.character(gene_circ_names))), by = miRNA_id]
dt6$V1 <- gsub("^c", "", dt6$V1)
dt6$V1 <- gsub("\"", "", dt6$V1)
colnames(dt6) <- c("miRNA_id", "gene_circ_names")
}
}
```
```{r pre_heatmap_density, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1c)[1] > 0) {
density_miRNA_per_circRNA <- as.data.table(dcast(bar1c, miRNA_id ~ circ_id, value.var = "density", fill = 0))
}
}
```
```{r table_miRNA_density, echo=FALSE, include=TRUE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
setkey(bar1c, circ_id)
setkey(gene_names, circ_id)
bar1c <- bar1c[gene_names, nomatch = 0]
dt1 <- data.table(bar1c[, c(1,6,2,3,4)], round(bar1c[, 5], 2))
dt1$gene_names <- paste0("<a href=\"", "https://www.genecards.org/cgi-bin/carddisp.pl?gene=", dt1$gene_names, "\" target=\"_blank\">", dt1$gene_names, "</a>")
dt1$miRNA_id <- paste0("<a href=\"", "http://www.mirbase.org/textsearch.shtml?q=", dt1$miRNA_id, "&submit=submit", "\" target=\"_blank\">", dt1$miRNA_id, "</a>")
datatable(data = dt1,
rownames = F,
colnames = c("CircRNA", "Gene name", "MiRNA", "Number of miRNA binding sites", "CircRNA length", "Density"),
style = "bootstrap",
class = "compact display",
caption = paste0("CircRNAs with the correspondent number of miRNAs identified. CircRNA host gene name is linked to GeneCards, miRNA name is linked to miRBase. \"Density\" = ratio of the number of miRNA binding sites in a circRNA to circRNA length. Colored barplots highlight values in \"Number of miRNA binding sites\", \"CircRNA length\" and \"Density\" columns."),
fillContainer = F,
autoHideNavigation = T,
filter = "top",
escape = FALSE,
extensions = c('ColReorder', 'Buttons', 'KeyTable'),
options = list(colReorder = TRUE,
searching = TRUE,
pageLength = 10,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
keys = TRUE
)
) %>%
formatStyle('num_siti',
background = styleColorBar(dt1$num_siti, 'mediumorchid'),
backgroundColor = "lightyellow",
backgroundSize = '100% 90%',
backgroundRepeat = 'no-repeat',
backgroundPosition = 'center'
) %>%
formatStyle('length',
background = styleColorBar(dt1$length, 'steelblue'),
backgroundColor = "lightyellow",
backgroundSize = '100% 90%',
backgroundRepeat = 'no-repeat',
backgroundPosition = 'center'
) %>%
formatStyle('density',
background = styleColorBar(dt1$density, 'yellow'),
backgroundColor = "lightyellow",
backgroundSize = '100% 90%',
backgroundRepeat = 'no-repeat',
backgroundPosition = 'center'
)
}
```
```{r table_miRNA_pos, echo=FALSE, include=TRUE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
dt2 <- miRNA_pred
dt2$miRNA_id <- paste0("<a href=\"", "http://www.mirbase.org/textsearch.shtml?q=", dt2$miRNA_id, "&submit=submit", "\" target=\"_blank\">", dt2$miRNA_id, "</a>")
dt2$gene_names <- paste0("<a href=\"", "https://www.genecards.org/cgi-bin/carddisp.pl?gene=", dt2$gene_names, "\" target=\"_blank\">", dt2$gene_names, "</a>")
dt2 <- dt2[, c(1,5,2,3,4)]
datatable(data = dt2,
rownames = F,
colnames = c("CircRNA", "Gene name", "MRE", "MRE start", "MiRNA end"),
style = "bootstrap",
class = "compact display",
caption = paste0("MiRNAs identified with respective binding position. MRE: miRNA recognition element. CircRNA host gene name is linked to GeneCards, miRNA name is linked to miRBase. \"MRE start\" = MRE start position estimated from \"MRE end\" position for a 22 nt miRNA binding. MRE start and end positions are relative to circRNA sequence."),
fillContainer = F,
autoHideNavigation = T,
filter = "top",
escape = FALSE,
extensions = c('ColReorder', 'Buttons', 'KeyTable'),
options = list(colReorder = TRUE,
searching = TRUE,
pageLength = 10,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
keys = TRUE
)
)
}
```
```{r table_common_miRNA, echo=FALSE, include=TRUE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
dt6$miRNA_id <- paste0("<a href=\"", "http://www.mirbase.org/textsearch.shtml?q=", dt6$miRNA_id, "&submit=submit", "\" target=\"_blank\">", dt6$miRNA_id, "</a>")
datatable(data = dt6,
rownames = F,
colnames = c("MiRNA", "CircRNA"),
style = "bootstrap",
class = "compact display",
#caption = paste0("MiRNAs shared by circRNAs. MiRNA name is linked to miRBase, circRNA name is linked to functional_predictions_circNAME.html."),
caption = paste0("MiRNAs shared by circRNAs. MiRNA name is linked to miRBase."),
fillContainer = F,
autoHideNavigation = T,
filter = "top",
escape = FALSE,
extensions = c('ColReorder', 'Buttons', 'KeyTable'),
options = list(colReorder = TRUE,
searching = TRUE,
pageLength = 10,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
keys = TRUE
)
)
}
}
```
```{r print_2, echo=FALSE, include=FALSE}
print_2 <- ""
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
if (dim(test)[1]>=2 & dim(test)[1]<=500 & dim(test)[2]>=2 & dim(test)[2]<=30) {
if (out == 0) {
print_2 <- "The heatmap of common miRNAs (shared by at least two circRNAs) is saved in \"Heatmap_number_miRNA_binding_sites_per_circRNA.png\"."
}
}
}
}
```
`r print_2`
```{r pre_heatmap_example_miRNA, echo=FALSE, include=FALSE}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
if (dim(test)[1]>=2 & dim(test)[1]<=500 & dim(test)[2]>=2 & dim(test)[2]<=30) {
if (out == 0) {
test_1 <- matrix(c(0,1,4, 0,3,2, 2,2,0, 1,0,3), byrow = T, ncol = 3)
rownames(test_1) <- c("miRNA 1", "miRNA 2", "miRNA 3", "miRNA 4")
colnames(test_1) <- c("circRNA 1", "circRNA 2", "circRNA 3")
}
}
}
}
```
```{r heatmap_example_miRNA, echo=FALSE, include=TRUE, width=4, height=4}
# heatmap
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (dim(bar1b)[1] > 0) {
if (dim(test)[1]>=2 & dim(test)[1]<=500 & dim(test)[2]>=2 & dim(test)[2]<=30) {
if (out == 0) {
dist = "canberra"
canb_heatmap <- pheatmap(mat = test_1,
clustering_distance_rows = dist,
clustering_distance_cols = dist,
color = colorRampPalette(c("white","#1565C0"))(1000),
cellheight = 10, cellwidth = 20, border_color="gray",
treeheight_row = 20, treeheight_col = 20,
main = "Example heatmap"
)
print(canb_heatmap)
}
}
}
}
```
## Validated target genes (TGs)
```{r validated_TG, echo=FALSE, include=FALSE}
# validated target genes
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (parameters[2] == "hsa" | parameters[2] == "mmu" | parameters[2] == "rno") {
## multiMir
if (dim(bar1b)[1] > 0) {
multimir_results_all <- data.table()
for (circ in circ_length$circ_id) {
miRNA_id <- bar1b[circ_id == circ,]$miRNA_id
if (length(miRNA_id) > 0) {
specie <- case_when(
parameters[2] == "hsa" ~ "hsa",
parameters[2] == "mmu" ~ "mmu",
parameters[2] == "rno" ~ "rno"
)
multimir_results <- get_multimir(org = specie, mirna = miRNA_id, table = 'validated', summary = TRUE)
multimir_results <- as.data.table(multimir_results@data)
#filter for strong categories
multimir_results <- multimir_results[grep("PAR-CLIP|HITS-CLIP|CLASH|Luciferase|Degradome|ChIP-seq|ELISA|Immuno.*", experiment),]
multimir_results <- multimir_results[support_type != "Functional MTI (Weak)",]
if (multimir_results[, .N] != 0) {
multimir_results <- data.table("circ_id" = circ, "miRNA_id" = multimir_results$mature_mirna_id, "target_gene" = multimir_results$target_symbol)
multimir_results_all <- rbind(multimir_results_all, multimir_results)
}
}
}
if (multimir_results_all[, .N] > 0) {
multimir_results_all <- unique(multimir_results_all)
multimir_results_all <- multimir_results_all[target_gene != "",]
setkey(multimir_results_all, circ_id)
setkey(gene_names, circ_id)
multimir_results_all <- multimir_results_all[gene_names, nomatch = 0]
multimir_results_all <- multimir_results_all[, c(1,4,2,3)]
# Save table
write.table(multimir_results_all, paste0(output_dir, "All_validated_TGs.csv"), sep = ",", row.names = F, quote = F, dec = ",")
}
}
}
}
```
```{r print_3, echo=FALSE, include=FALSE}
print_3 <- "Number of identified TGs: 0."
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (parameters[2] == "hsa" | parameters[2] == "mmu" | parameters[2] == "rno") {
if (dim(bar1b)[1] > 0) {
if (multimir_results_all[, .N] > 0) {
print_3 <- "Network of common TGs shared by at least two circRNAs."
}
}
}
}
```
`r print_3`
```{r print_3b, echo=FALSE, include=FALSE}
print_3b <- ""
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (parameters[2] == "hsa" | parameters[2] == "mmu" | parameters[2] == "rno") {
if (dim(bar1b)[1] > 0) {
if (multimir_results_all[, .N] > 0) {
print_3b <- "Tip: if the network is too crowded, use more stringent parameters."
}
}
}
}
```
`r print_3b`
```{r network_TG, echo=FALSE, include=TRUE, fig.width=12, fig.height=9}
if (parameters[1] == "M" | parameters[1] == "MR" | parameters[1] == "MO" | parameters[1] == "MRO") {
if (parameters[2] == "hsa" | parameters[2] == "mmu" | parameters[2] == "rno") {
if (dim(bar1b)[1] > 0) {
if (multimir_results_all[, .N] > 0) {
multimir_results_all$gene_circ_names <- paste0("circ", multimir_results_all$gene_names, "_", multimir_results_all$circ_id)
# select only TGs common to alt 2 circRNAs
dt0 <- multimir_results_all[, .(list(unique(gene_circ_names)), length(unique(gene_circ_names))), by = target_gene]
colnames(dt0) <- c("target_gene", "gene_circ_names", "N")
common_TGs <- dt0[N >= 2, "target_gene"]
multimir_results_all <- multimir_results_all[target_gene %in% common_TGs$target_gene,]
if (multimir_results_all[, .N] > 0) {
dt1 <- multimir_results_all[, .N, by = .(gene_circ_names, miRNA_id)]
dt2 <- multimir_results_all[, .N, by = .(miRNA_id, target_gene)]
colnames(dt1) <- c("term1", "term2", "freq")
colnames(dt2) <- c("term1", "term2", "freq")
dt <- rbind(dt1, dt2)
dt <- as.data.frame(dt)
gr <- graph.data.frame(dt[dt$freq!=0,])
a <- length(unique(multimir_results_all$gene_circ_names))
b <- length(unique(multimir_results_all$miRNA_id))
c <- length(unique(multimir_results_all$target_gene))
# default version
V(gr)$type <- c(rep("circRNA", a), rep("miRNA", b), rep("TG", c))
ty <- match(V(gr)$type, c("circRNA", "miRNA", "TG") )
#p1 <- plot(gr, vertex.color=ty)
# custom version