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@KJeynesCupper
KJeynesCupper committed Jul 12, 2023
1 parent 44ad686 commit f0b7fdd
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241 changes: 123 additions & 118 deletions R/RNAdistribution.R
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Original file line number Diff line number Diff line change
Expand Up @@ -102,8 +102,7 @@
#' facet = TRUE, facet.arrange = 2 )
#'
#'data("sRNA_data_consensus")
#'p6 <- RNAdistribution(data = sRNA_data_consensus, style = "consensus",
#'consensus = TRUE)
#'p6 <- RNAdistribution(data = sRNA_data_consensus, consensus = TRUE)
#'
#' @export
#' @importFrom BiocGenerics "grep"
Expand All @@ -128,18 +127,13 @@
#' @importFrom ggplot2 "ylab"
#' @importFrom ggplot2 "labs"
#'
RNAdistribution <- function (data, samples = NULL, style = c("bar", "line", "consensus"),
facet = TRUE, facet.arrange = 3, colour = "black", together = TRUE,
consensus = FALSE, relative = FALSE)
{
RNAdistribution <- function (data, samples = NULL, style,
facet = TRUE, facet.arrange = 3, colour = "black", together = TRUE,
consensus = FALSE, relative = FALSE) {
if (base::missing(data) || !base::inherits(data, c("data.frame"))) {
stop("data must be a data frame, see ?help for more details")
}
if (base::missing(style) || !style %in% c("bar", "line",
"consensus")) {
stop(paste("Please specify type of plot to produce",
"(\"line\", or\n \"bar\")"))
}

if (consensus == TRUE) {
x <- data %>% dplyr::count(sRNA_Consensus)
x$sRNA_Consensus <- gsub("nt_", "", x$sRNA_Consensus)
Expand All @@ -160,8 +154,11 @@ RNAdistribution <- function (data, samples = NULL, style = c("bar", "line", "co
print(p1)
}
else {
if (base::missing(style) || !style %in% c("bar", "line")) {
stop(paste("Please specify type of plot to produce",
"(\"line\", or\n \"bar\")"))
}
data.cols <- data %>% dplyr::select(tidyselect::starts_with("DicerCall_"))
}
counts.df <- apply(data.cols, MARGIN = 2, table)
# if a replicate only has unclassified sRNAs (N), then we need to alter
# beware that any tables which do not have the required columsn
Expand All @@ -176,125 +173,133 @@ RNAdistribution <- function (data, samples = NULL, style = c("bar", "line", "co
missing_columns <- setdiff(required_columns, names(table_i))
# add missing columns to table, and assign a value of 0
table_i[missing_columns] <- 0

# check order:
if(!identical(names(table_i),required_columns)){
# match order to required order
table_i <- table_i[match(required_columns, names(table_i))]
}
# Update the table in counts.df
counts.df[[i]] <- table_i
}
}
# transpose, make dataframe of results.
counts.df <- data.frame(t(do.call(rbind, counts.df)))
}
else
if(!base::inherits(counts.df, c("list"))) {
counts.df <- data.frame(counts.df)
colnames(counts.df) <- gsub("DicerCall_", "", colnames(counts.df))
counts.df <- data.table::setDT(counts.df, keep.rownames = "Class")[]
counts.df <- counts.df[!(counts.df$Class == "N"), ]
style <- base::match.arg(style)
if (style == "bar") {
plist = sapply(names(counts.df)[-grep("Class", names(counts.df))],
function(col) {
ggplot2::ggplot(counts.df, ggplot2::aes_string(x = "Class",
y = col)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::labs(title = col, x = "RNA Class",
y = "Count")
}, simplify = FALSE)
sn <- names(plist)
if (facet == TRUE) {
if (is.null(samples)) {
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}

out <- list(plot = p, data = counts.df)
return(out)
}
colnames(counts.df) <- gsub("DicerCall_", "", colnames(counts.df))
counts.df <- data.table::setDT(counts.df, keep.rownames = "Class")[]
counts.df <- counts.df[!(counts.df$Class == "N"), ]
style <- base::match.arg(style)
if (style == "bar") {
plist = sapply(names(counts.df)[-grep("Class", names(counts.df))],
function(col) {
ggplot2::ggplot(counts.df, ggplot2::aes_string(x = "Class",
y = col)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::labs(title = col, x = "RNA Class",
y = "Count")
}, simplify = FALSE)
sn <- names(plist)
if (facet == TRUE) {
if (is.null(samples)) {
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count)) + ggplot2::geom_bar(stat = "identity",
fill = colour) + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}

out <- list(plot = p, data = counts.df)
return(out)
}
else if (facet == FALSE) {

save <- list()
for (i in 1:length(plist)) {
print(plist[i])
save <- list(save, plist[i])
}
out <- list(plot = save, data = counts.df)
return(out)
}
}
if (style == "line") {
if (together == TRUE) {
if (is.null(samples)) {
counts.df <- data.table::melt(counts.df, id.vars = "Class")
p <- ggplot2::ggplot(counts.df, ggplot2::aes(Class,
value, col = variable, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::xlab("RNA Class") + ggplot2::ylab("Counts") +
ggplot2::labs(color = "Samples")
}
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
counts.df <- data.table::melt(counts.df, id.vars = "Class")
p <- ggplot2::ggplot(counts.df, ggplot2::aes(Class,
value, col = variable, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::xlab("RNA Class") + ggplot2::ylab("Counts") +
ggplot2::labs(color = "Samples")
out <- list(plot = p, data = counts.df)
return(out)
}
}
else if (together == FALSE) {
if (facet == TRUE) {
if (is.null(samples)) {
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
else if (facet == FALSE) {

save <- list()
for (i in 1:length(plist)) {
print(plist[i])
save <- list(save, plist[i])
}
out <- list(plot = save, data = counts.df)
return(out)
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
out <- list(plot = p, data = counts.df)
return(out)
}
if (style == "line") {
if (together == TRUE) {
if (is.null(samples)) {
counts.df <- data.table::melt(counts.df, id.vars = "Class")
p <- ggplot2::ggplot(counts.df, ggplot2::aes(Class,
value, col = variable, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::xlab("RNA Class") + ggplot2::ylab("Counts") +
ggplot2::labs(color = "Samples")
}
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
counts.df <- data.table::melt(counts.df, id.vars = "Class")
p <- ggplot2::ggplot(counts.df, ggplot2::aes(Class,
value, col = variable, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::xlab("RNA Class") + ggplot2::ylab("Counts") +
ggplot2::labs(color = "Samples")
out <- list(plot = p, data = counts.df)
return(out)
}
}
else if (together == FALSE) {
if (facet == TRUE) {
if (is.null(samples)) {
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
else if (!is.null(samples)) {
counts.df <- counts.df %>% select(!all_of(samples))
p <- ggplot2::ggplot(tidyr::gather(counts.df,
key, Count, -Class), ggplot2::aes(Class,
Count, group = 1)) + ggplot2::geom_point() +
ggplot2::geom_line() + ggplot2::theme_classic() +
ggplot2::facet_wrap(~key, scales = "free_y",
ncol = facet.arrange)
}
out <- list(plot = p, data = counts.df)
return(out)
}
else if (facet == FALSE) {
plist2 = sapply(names(counts.df)[-grep("Class",
names(counts.df))], function(col) {
ggplot2::ggplot(counts.df, ggplot2::aes_string(x = "Class",
y = col, group = 1)) + ggplot2::geom_point(colour = colour) +
ggplot2::theme_classic() + ggplot2::geom_line(colour = colour) +
ggplot2::labs(title = col, x = "RNA Class",
y = "Count")
}, simplify = FALSE)
sn <- names(plist2)
p <- plist2
out <- list(plot = p, data = counts.df)
return(out)

for (J in 1:length(plist2)) {
print(plist2[J])
}
}
else if (facet == FALSE) {
plist2 = sapply(names(counts.df)[-grep("Class",
names(counts.df))], function(col) {
ggplot2::ggplot(counts.df, ggplot2::aes_string(x = "Class",
y = col, group = 1)) + ggplot2::geom_point(colour = colour) +
ggplot2::theme_classic() + ggplot2::geom_line(colour = colour) +
ggplot2::labs(title = col, x = "RNA Class",
y = "Count")
}, simplify = FALSE)
sn <- names(plist2)
p <- plist2
out <- list(plot = p, data = counts.df)
return(out)

for (J in 1:length(plist2)) {
print(plist2[J])
}
}
}
}
}
}
8 changes: 8 additions & 0 deletions vignettes/mobileRNA.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -848,6 +848,14 @@ the local functionality. As well as, retrieving the nucleotide sequence of
the most abundant sRNA within a cluster and locating complementary sequences in
both the genome of origin and genome of destination using BLAST.

If you have different datatypes, for example RNAseq and BSseq, and want to
overlap your data it might be interesting to see whether there are complementary
DNA sequences at the promoter regions of your deferentially expressed genes of
interest to the identified potential mobile sRNAs. Moreover, in theory, if these
regions also show differential methylation it could be a potential indication of
a molecular pathway.



IMPORTANT: If chromosome names were altered in either of genome assemblies used
in the mapping steps, the chromosome names in the genome annotation files must
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