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fix #18
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@lpantano
lpantano committed Aug 23, 2024
1 parent d568504 commit 45df1aa
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4 changes: 2 additions & 2 deletions DESCRIPTION
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@@ -1,6 +1,6 @@
Package: isomiRs
Version: 1.31.2
Date: 2024-04-30
Version: 1.33.2
Date: 2024-08-23
Type: Package
Title: Analyze isomiRs and miRNAs from small RNA-seq
Description: Characterization of miRNAs and isomiRs, clustering and
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4 changes: 4 additions & 0 deletions NEWS
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@@ -1,3 +1,7 @@
Changes in version 1.33.1

* fix isoAnnotate error due to duplicates lines https://github.com/lpantano/isomiRs/issues/18

Changes in version 1.31.2 (Ömer An)

* remove `assertive.sets`
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152 changes: 77 additions & 75 deletions R/isomiRs.R
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ isoDE <- function(ids, formula=NULL, ...){
#' @param ids Object of class [IsomirDataSeq].
#' @param top Number of isomiRs/miRNAs used.
#' @param condition Give condition to color PCA samples
#'
#'
#' @examples
#' data(mirData)
#' isoTop(mirData)
Expand Down Expand Up @@ -107,8 +107,8 @@ isoTop <- function(ids, top=20, condition=NULL){
#' will appear in the plot. When `type="subs"`, it will appear one
#' position for each nucleotide in the reference miRNA. Points
#' will indicate isomiRs with nucleotide changes at the given position.
#' When `type="all"` a colar coordinate map will show
#' the abundance of each isomiR type in a single plot.
#' When `type="all"` a colar coordinate map will show
#' the abundance of each isomiR type in a single plot.
#' Note the position is relatively to the
#' sequence not the miRNA.
#'
Expand All @@ -118,7 +118,7 @@ isoTop <- function(ids, top=20, condition=NULL){
#' @export
isoPlot <- function(ids, type="iso5", column=NULL,
use = NULL, nts = FALSE){

if (is.null(column)){
column <- names(colData(ids))[1]
}
Expand All @@ -128,14 +128,14 @@ isoPlot <- function(ids, type="iso5", column=NULL,
type <- "snv"
}
stopifnot(type %in% c("snv", "add", "iso5", "iso3"))

freq <- size <- group <- abundance <- NULL
codevn <- 3:6

names(codevn) <- supported
coden <- codevn[type]
des <- colData(ids) %>%
as.data.frame() %>%
des <- colData(ids) %>%
as.data.frame() %>%
rownames_to_column("iso_sample")
rawData <- metadata(ids)[["rawData"]]
if (!is.null(use)){
Expand All @@ -159,37 +159,37 @@ isoPlot <- function(ids, type="iso5", column=NULL,
rawData[["size"]] <- as.factor(.isomir_position(rawData[[coden]]))
xaxis <- "position respect to the reference"
}
freq_data <- rawData[,c("size", des[["iso_sample"]])] %>%
group_by(!!sym("size")) %>%
summarise(across(everything(), sum)) %>%
ungroup() %>%
freq_data <- rawData[,c("size", des[["iso_sample"]])] %>%
group_by(!!sym("size")) %>%
summarise(across(everything(), sum)) %>%
ungroup() %>%
gather("iso_sample", "sum", -!!sym("size"))

n_data <- rawData[,c("size", des[["iso_sample"]])] %>%
group_by(!!sym("size")) %>%
summarise(across(everything(), ~sum(. > 0))) %>%
ungroup() %>%
group_by(!!sym("size")) %>%
summarise(across(everything(), ~sum(. > 0))) %>%
ungroup() %>%
gather("iso_sample", "sum", -!!sym("size"))
freq_pct <- freq_data %>%
group_by(!!sym("iso_sample")) %>%

freq_pct <- freq_data %>%
group_by(!!sym("iso_sample")) %>%
summarise(total_sum = sum(sum)) %>%
left_join(freq_data, by = "iso_sample") %>%
left_join(freq_data, by = "iso_sample") %>%
mutate(pct_abundance = sum / total_sum * 100L,
id = paste(iso_sample, size)) %>%
id = paste(iso_sample, size)) %>%
.[,c("id", "iso_sample", "size", "pct_abundance")]
n_pct <- n_data %>%
group_by(!!sym("iso_sample")) %>%

n_pct <- n_data %>%
group_by(!!sym("iso_sample")) %>%
summarise(total_sum = sum(sum)) %>%
left_join(n_data, by = "iso_sample") %>%
left_join(n_data, by = "iso_sample") %>%
mutate(unique = sum / total_sum *100L,
id = paste(iso_sample, size)) %>%
id = paste(iso_sample, size)) %>%
.[,c("id", "unique")]

inner_join(freq_pct, n_pct, by="id") %>%
filter(size!="0") %>%
left_join(des, by ="iso_sample") %>%
filter(size!="0") %>%
left_join(des, by ="iso_sample") %>%
ggplot() +
geom_jitter(aes_string(x="size",y="unique", colour=column,
size="pct_abundance")) +
Expand Down Expand Up @@ -231,45 +231,45 @@ isoPlotPosition <- function(ids, position = 1L, column = NULL){
column <- names(colData(ids))[1]
}

des <- colData(ids) %>%
as.data.frame() %>%
des <- colData(ids) %>%
as.data.frame() %>%
rownames_to_column("iso_sample")
rawData <- metadata(ids)[["rawData"]]

parsed_change <- .subs_position(rawData[["mism"]])
rawData[["change"]] <- as.factor(parsed_change[["change"]])
rawData[["pos"]] <- as.character(parsed_change[["size"]])

freq_data <- rawData[,c("change", "pos", des[["iso_sample"]])] %>%
group_by(!!sym("change"), !!sym("pos")) %>%
summarise(across(everything(), sum)) %>%
ungroup() %>%
freq_data <- rawData[,c("change", "pos", des[["iso_sample"]])] %>%
group_by(!!sym("change"), !!sym("pos")) %>%
summarise(across(everything(), sum)) %>%
ungroup() %>%
gather(iso_sample, sum, -change, -pos)

n_data <- rawData[,c("change", "pos", des[["iso_sample"]])] %>%
group_by(!!sym("change"), !!sym("pos")) %>%
summarise(across(everything(), ~sum(. > 0))) %>%
ungroup() %>%
group_by(!!sym("change"), !!sym("pos")) %>%
summarise(across(everything(), ~sum(. > 0))) %>%
ungroup() %>%
gather(iso_sample, sum, -change, -pos)
freq_pct <- freq_data %>%
group_by(!!sym("iso_sample")) %>%

freq_pct <- freq_data %>%
group_by(!!sym("iso_sample")) %>%
summarise(total_sum = sum(sum)) %>%
left_join(freq_data, by = "iso_sample", suffix = c("", "_tmp")) %>%
left_join(freq_data, by = "iso_sample", suffix = c("", "_tmp")) %>%
mutate(pct_abundance = sum / total_sum * 100L,
id = paste(iso_sample, change)) %>%
.[.[["pos"]] == position,] %>%
id = paste(iso_sample, change)) %>%
.[.[["pos"]] == position,] %>%
.[,c("id", "iso_sample", "change", "pct_abundance")]
n_pct <- n_data %>%
group_by(!!sym("iso_sample")) %>%

n_pct <- n_data %>%
group_by(!!sym("iso_sample")) %>%
summarise(total_sum = sum(sum)) %>%
left_join(n_data, by = "iso_sample") %>%
left_join(n_data, by = "iso_sample") %>%
mutate(unique = sum / total_sum *100L,
id = paste(iso_sample, change)) %>%
.[.[["pos"]] == position,] %>%
id = paste(iso_sample, change)) %>%
.[.[["pos"]] == position,] %>%
.[,c("id", "unique")]

inner_join(freq_pct, n_pct, by="id") %>%
left_join(des, by ="iso_sample") %>%
ggplot() +
Expand All @@ -281,7 +281,7 @@ isoPlotPosition <- function(ids, position = 1L, column = NULL){
labs(list(title=paste("Change distribution"),
y="pct of isomiRs",
x=paste0("changes at postiion ",
position,
position,
" respect to the reference")))
}

Expand Down Expand Up @@ -318,7 +318,7 @@ isoPlotPosition <- function(ids, position = 1L, column = NULL){
#' You can get a table with 5' trimming isomiRS, miRBase reference and
#' the rest by calling with `isoCounts(ids, ref=TRUE, iso5=TRUE)`.
#' If you set up all parameters to TRUE, you will get a table for
#' each different sequence mapping to a miRNA (i.e. all isomiRs).
#' each different sequence mapping to a miRNA (i.e. all isomiRs).
#'
#' Examples for the naming used for the isomiRs are at
#' http://seqcluster.readthedocs.org/mirna_annotation.html#mirna-annotation.
Expand All @@ -334,7 +334,7 @@ isoPlotPosition <- function(ids, position = 1L, column = NULL){
#' head(counts(ids))
#' @export
isoCounts <- function(ids, ref=FALSE, iso5=FALSE, iso3=FALSE,
add=FALSE, snv=FALSE, seed=FALSE,
add=FALSE, snv=FALSE, seed=FALSE,
all = FALSE,minc=1, mins=1,
merge_by=NULL){
coldata <- colData(ids)
Expand All @@ -349,11 +349,11 @@ isoCounts <- function(ids, ref=FALSE, iso5=FALSE, iso3=FALSE,
combined <- .merge_counts_by(counts, colData(ids), merge_by)
counts <- combined[["counts"]]
coldata <- combined[["coldata"]]

raw_counts <- rawdata[,7:ncol(rawdata)] %>% as.matrix()
new <- .merge_counts_by(raw_counts, colData(ids), merge_by)
rawdata <- as.tibble(cbind(rawdata[,1:6], new[["counts"]]))

}

counts <- counts[rowSums(counts > minc) >= mins, ]
Expand All @@ -363,19 +363,19 @@ isoCounts <- function(ids, ref=FALSE, iso5=FALSE, iso3=FALSE,
}

#' Annotate the rawData of the [IsomirDataSeq] object
#'
#'
#' Get the sequence and the name information for each isomiR,
#' and the importance value (`isomir_reads/mirna_reads`) for
#' each sample.
#'
#'
#' @param ids Object of class [IsomirDataSeq].
#'
#' @details
#'
#' @details
#' `edit_mature_position` represents the position at the mature
#' sequence + nucleotide at reference + nucleotide at isomiR.
#' @return [data.frame] with the sequence, isomir name,
#' and importance for each sample and isomiR.
#'
#'
#' @examples
#' data(mirData)
#' head(isoAnnotate(mirData))
Expand All @@ -387,25 +387,27 @@ isoAnnotate <- function(ids){
dt <- rawData %>% # calculate pct
.[,c(1:2,7:ncol(.))] %>%
gather("sample", "value", -mir, -seq, -uid) %>%
group_by(sample, mir) %>%
group_by(sample, mir) %>%
filter(value > 0) %>%
group_by(mir, sample, seq, uid) %>%
summarise(value=sum(value)) %>%
group_by(mir, sample) %>%
group_by(mir, sample, seq, uid) %>%
summarise(value=sum(value)) %>%
group_by(mir, sample) %>%
arrange(sample, mir, desc(value)) %>%
mutate(pct = value / sum(value) * 100) %>%
ungroup() %>%
select(-value, -mir) %>%
spread(sample, pct) %>%
ungroup() %>%
select(-value, -mir) %>%
spread(sample, pct) %>%
as.data.frame()

lift <- ((grepl("[A-Z]", rawData[["t5"]]) * -1) + (grepl("[a-z]", rawData[["t5"]]) * 1)) * nchar(rawData[["t5"]])
pos <- as.numeric(str_extract(rawData[["mism"]], "[0-9]*")) + lift
dt_changes <- rawData %>% distinct(mir, uid, t5, mism)
lift <- ((grepl("[A-Z]", dt_changes[["t5"]]) * -1) + (grepl("[a-z]", dt_changes[["t5"]]) * 1)) * nchar(dt_changes[["t5"]])
pos <- as.numeric(str_extract(dt_changes[["mism"]], "[0-9]*")) + lift
pos <- ifelse(pos==0, -1, pos)
change <- reverse(str_extract(rawData[["mism"]], "[ACTG]+"))
change <- reverse(str_extract(dt_changes[["mism"]], "[ACTG]+"))
mature_pos <- ifelse(grepl("NA", change), "NA", paste0(pos, ":", change))

dt[["edit_mature_position"]] = mature_pos
dt_changes[["edit_mature_position"]] <- mature_pos

dt <- left_join(dt, dt_changes[,c("uid", "edit_mature_position")], by=c("uid"))
#dt[["edit_mature_position"]] = mature_pos
dt
}

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