#' Break graph paths which connect sources.
#'
#' \code{break_connecting_source_paths} returns a graph where only one source
#' is present per cluster.
#'
#' @description Given a list of unique integration site positions (reduced
#' GRanges object) and a directed graph of connected components, this function
#' identifies clusters with multiple sources, the paths between those sources,
#' and removes edges along the path so that each cluster only has one source
#' node. Edge removal is first based on nucleotide distance (greater distance
#' prefered), then based on abundance (lowest abundance prefered), then on an
#' upstream bias (downstream connection will be removed when everything ties).
#'
#' @usage
#' break_connecting_source_paths(red.sites, graph, bias)
#'
#' @param red.sites GRanges object which has been reduced to single nt positions
#' and contains the revmap from the original GRanges object. The object must
#' also contain a column for cluster membership (clusID) and a column for
#' abundance (fragLengths).
#'
#' @param graph a directed graph built from the red.sites object. Each node
#' corresponds to a row in the red.sites object.
#'
#' @param bias either "upsteam" or "downstream", designating which position to
#' choose if other decision metrics are tied.
#'
#' @examples
#' gr <- generate_test_granges(stdev = 3)
#' red.sites <- reduce(
#' flank(gr, -1, start = TRUE),
#' min.gapwidth = 0L,
#' with.revmap = TRUE)
#' red.sites$siteID <- seq(1:length(red.sites))
#' revmap <- as.list(red.sites$revmap)
#' red.sites$abundance <- sapply(revmap, length)
#' red.hits <- GenomicRanges::as.data.frame(
#' findOverlaps(red.sites, maxgap = 0L, drop.self = TRUE))
#' red.hits <- red.hits %>%
#' mutate(q_pos = start(red.sites[queryHits])) %>%
#' mutate(s_pos = start(red.sites[subjectHits])) %>%
#' mutate(q_abund = red.sites[queryHits]$abundance) %>%
#' mutate(s_abund = red.sites[subjectHits]$abundance) %>%
#' mutate(strand = unique(strand(
#' c(red.sites[queryHits], red.sites[subjectHits])))) %>%
#' mutate(is.upstream = ifelse(
#' strand == "+",
#' q_pos < s_pos,
#' q_pos > s_pos)) %>%
#' mutate(keep = q_abund > s_abund) %>%
#' mutate(keep = ifelse(
#' q_abund == s_abund,
#' is.upstream,
#' keep)) %>%
#' filter(keep)
#' g <- make_empty_graph(n = length(red.sites), directed = TRUE) %>%
#' add_edges(unlist(mapply(
#' c, red.hits$queryHits, red.hits$subjectHits, SIMPLIFY = FALSE)))
#' red.sites$clusID <- clusters(g)$membership
#' g <- connect_satalite_vertices(red.sites, g, gap = 2L, bias = "upstream")
#' red.sites$clusID <- clusters(g)$membership
#' break_connecting_source_paths(red.sites, g, "upstream")
#'
#' @author Christopher Nobles, Ph.D.
#'
#' @importFrom magrittr %>%
#'
break_connecting_source_paths <- function(red.sites, graph, bias){
src_nodes <- sources(graph)
sources_p_clus <- IRanges::IntegerList(split(
src_nodes, igraph::clusters(graph)$membership[src_nodes]))
clus_w_multi_sources <- sources_p_clus[S4Vectors::lengths(sources_p_clus) > 1]
if(length(clus_w_multi_sources) > 0){
adj_pairs <- do.call(c, lapply(clus_w_multi_sources, function(x){
lapply(1:(length(x)-1), function(i) c(x[i], x[i+1]))
}))
snk_nodes <- sinks(graph)
edges_to_edit <- data.frame(
src.node.i = unlist(adj_pairs)[
IRanges::start(IRanges::IntegerList(adj_pairs)@partitioning)],
src.node.j = unlist(adj_pairs)[
IRanges::end(IRanges::IntegerList(adj_pairs)@partitioning)]) %>%
dplyr::mutate(
src.node.i.abund = as.numeric(red.sites[src.node.i]$abund),
src.node.j.abund = as.numeric(red.sites[src.node.j]$abund),
sink.node = IRanges::start(
IRanges::findOverlapPairs(
IRanges::IRanges(src.node.i, src.node.j),
IRanges::IRanges(snk_nodes, width = 1))@second))
# Identify the nodes adjacent to sinks between connected sources
# then filter adjacent pairs to identify which edge should be 'clipped'.
# Filtering based first on adjacent node distance (edges with greater
# distance get clipped), then abundance (lower abund gets clipped), then
# biasing on upstream edges over downstream (downstream is clipped for
# tie breaking).
if(bias == "upstream"){
target_edges <- dplyr::bind_rows(lapply(
seq_len(nrow(edges_to_edit)), function(i){
sink <- edges_to_edit[i, "sink.node"]
path <- unlist(igraph::all_simple_paths(
igraph::as.undirected(graph),
edges_to_edit[i, "src.node.i"],
edges_to_edit[i, "src.node.j"]))
pos <- which(path == sink)
data.frame(
sink = rep(sink, 2),
adj.node = c(path[pos-1], path[pos+1])) })) %>%
dplyr::mutate(
sink.pos = GenomicRanges::start(red.sites[sink]),
adj.pos = GenomicRanges::start(red.sites[adj.node]),
adj.abund = red.sites[adj.node]$abund,
nt.dist = abs(sink.pos - adj.pos),
strand = as.character(GenomicRanges::strand(red.sites[sink])),
is.upstream = ifelse(
strand == "+", sink.pos < adj.pos, sink.pos > adj.pos)) %>%
dplyr::group_by(sink) %>%
dplyr::filter(nt.dist == max(nt.dist)) %>%
dplyr::filter(adj.abund == min(adj.abund)) %>%
dplyr::mutate(
group_size = n(),
keep = ifelse(group_size == 1, TRUE, !is.upstream)) %>%
dplyr::filter(keep) %>%
dplyr::ungroup() %>%
as.data.frame()
}else if(bias == "downstream"){
target_edges <- dplyr::bind_rows(lapply(
seq_len(nrow(edges_to_edit)), function(i){
sink <- edges_to_edit[i, "sink.node"]
path <- unlist(igraph::all_simple_paths(
igraph::as.undirected(graph),
edges_to_edit[i, "src.node.i"],
edges_to_edit[i, "src.node.j"]))
pos <- which(path == sink)
data.frame(
sink = rep(sink, 2),
adj.node = c(path[pos-1], path[pos+1])) })) %>%
dplyr::mutate(
sink.pos = GenomicRanges::start(red.sites[sink]),
adj.pos = GenomicRanges::start(red.sites[adj.node]),
adj.abund = red.sites[adj.node]$abund,
nt.dist = abs(sink.pos - adj.pos),
strand = as.character(
GenomicRanges::strand(red.sites[sink])),
is.downstream = ifelse(
strand == "+", sink.pos > adj.pos, sink.pos < adj.pos)) %>%
dplyr::group_by(sink) %>%
dplyr::filter(nt.dist == max(nt.dist)) %>%
dplyr::filter(adj.abund == min(adj.abund)) %>%
dplyr::mutate(
group_size = n(),
keep = ifelse(group_size == 1, TRUE, !is.downstream)) %>%
dplyr::filter(keep) %>%
dplyr::ungroup() %>%
as.data.frame()
}else{
stop("No bias specified. Please choose either 'upstream' or 'downstream'.")
}
break_edges <- with(target_edges, vzip(sink, adj.node))
edge_ids_to_break <- igraph::get.edge.ids(
graph, break_edges, directed = FALSE)
}else{
edge_ids_to_break <- c()
}
igraph::delete_edges(graph, edge_ids_to_break)
}
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