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utils.R
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utils.R
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# show colors -------
library(scales)
#show_col(col)
# Read_files ---------
func_read_files<-function(sample.name,datadir){
seg = read.table(file = dir(datadir, full.names=T, pattern="uber.*.seg.txt"), header = T, sep = "\t")
bin = read.table(file = dir(datadir, full.names=T, pattern="uber.*.bin.txt"), header = T, sep = "\t")
ratio = read.table(file = dir(datadir, full.names=T, pattern="uber.*.ratio.txt"), header = T, sep = "\t")
seg = seg %>% dplyr::filter(!chrom %in% 24)
bin = bin %>% dplyr::filter(!chrom %in% 24)
ratio = ratio %>% dplyr::filter(!chrom %in% 24)
seg_cndata = data.frame(seg[,-c(1:3)])
bin_cndata = data.frame(bin[,-c(1:3)])
ratio_cndata = data.frame(ratio[,-c(1:3)])
chrom_chrompos_abspos = bin[1:3]
files = list(seg,bin,ratio,seg_cndata,bin_cndata,ratio_cndata,chrom_chrompos_abspos);
names(files) = c("seg","bin","ratio","seg_cndata","bin_cndata","ratio_cndata","chrom_chrompos_abspos")
message("import seg, bin, ratio matrics")
return(files)
}
# get_groups ------
get_timepoint <- function(x, ifrowname = T) {
#x = as.data.frame(t(files$seg_cndata))
Primary = grep(rownames(x),pattern = "P|_P_|primary",ignore.case = T) #P|
Recurrence = grep(rownames(x),pattern = "RE|recurrence",ignore.case = T) #R|_R_|
allcluster = c("Primary", "Recurrence")
Group = rep(NA, nrow(x))
Group[Primary] <- "Primary"
Group[Recurrence] <- "Recurrence"
xcluster = unique(Group)
levels = allcluster[allcluster %in% xcluster]
Groups <- data.frame(Group); rownames(Groups) <- rownames(x); table(Groups); colnames(Groups)<-"G"
Groups[which(is.na(Groups)),] <- "Primary"
Groups$G = factor(Groups$G, levels = levels)
Groups_2<-factor(Groups[,1],levels = levels)
if(ifrowname == T ){return(Groups)}
if(ifrowname == F ){return(Groups_2)}
}
Conv_ratio2int <- function(x, prim_ploidy, rec_ploidy) {
# get the df's
x_df = x$seg_cndata %>% tibble::as_tibble()
y_df = x$well %>% tibble::as_tibble()
z_df = x$Groups %>% tibble::as_tibble()
x_df_tr = t(x_df) %>% tibble::as_tibble()
x_df_tr$cells = as.character(colnames(x_df))
y_df$cells = as.character(y_df$cells)
z_df$cells = rownames(x$Groups)
#table(colnames(x_df) == y_df$cells)
# merge the df
xy_df = dplyr::left_join(x_df_tr, y_df)
xy_df = dplyr::left_join(xy_df, z_df)
# convert to long
xy_df_long = reshape2::melt(xy_df, id.vars = c("well", "cells", "G"),
variable.name = "seg", value.name = "seg_rat")
# convert to int CN
xy_df_long = xy_df_long %>% dplyr::mutate(seg_rat_int = dplyr::case_when((well == "a_well") & (G == "Primary") ~ seg_rat*prim_ploidy,
(well %in% "a_well") & (G %in% "Recurrence") ~ seg_rat*rec_ploidy,
(well %in% c("d_well", "pop_well") ~ seg_rat*2)))
# convert to int CN, all cells
xy_df_long = xy_df_long %>% dplyr::mutate(seg_rat_int_all = dplyr::case_when(G == "Primary" ~ seg_rat*prim_ploidy,
G == "Recurrence" ~ seg_rat*rec_ploidy))
xy_df_long$seg_rat_int = round(xy_df_long$seg_rat_int)
xy_df_long$seg_rat_int_all = round(xy_df_long$seg_rat_int_all)
# convert back to wide
xy_df_int = reshape2::recast(xy_df_long, cells ~ seg, measure.var = "seg_rat_int")
xy_df_int1 = xy_df_int %>% dplyr::select(-cells)
rownames(xy_df_int1) = xy_df_int$cells
xy_df_int_all = reshape2::recast(xy_df_long, cells ~ seg, measure.var = "seg_rat_int_all")
xy_df_int_all1 = xy_df_int_all %>% dplyr::select(-cells)
rownames(xy_df_int_all1) = xy_df_int_all$cells
#Groups_2<-factor(Groups[,1],levels = levels)
{return(list(int_col = t(xy_df_int1), int_all_col = t(xy_df_int_all1)))}
}
Conv_ratio2int_pcf <- function(x, prim_ploidy, rec_ploidy) {
# get the df's
x_df = x$pcf_seg_exp %>% tibble::as_tibble()
y_df = x$well %>% tibble::as_tibble()
z_df = x$Groups %>% tibble::as_tibble()
#x_df_tr = t(x_df) %>% tibble::as_tibble()
x_df$cells = as.character(rownames(x$pcf_seg_exp))
y_df$cells = as.character(y_df$cells)
z_df$cells = rownames(x$Groups)
#table(colnames(x_df) == y_df$cells)
# merge the df
xy_df = dplyr::left_join(x_df, y_df)
xy_df = dplyr::left_join(xy_df, z_df)
# convert to long
xy_df_long = reshape2::melt(xy_df, id.vars = c("well", "cells", "G"),
variable.name = "seg", value.name = "seg_rat")
# convert to int CN
xy_df_long = xy_df_long %>% dplyr::mutate(seg_rat_int = dplyr::case_when((well == "a_well") & (G == "Primary") ~ seg_rat*prim_ploidy,
(well %in% "a_well") & (G %in% "Recurrence") ~ seg_rat*rec_ploidy,
(well %in% c("d_well", "pop_well") ~ seg_rat*2)))
# convert to int CN, all cells
xy_df_long = xy_df_long %>% dplyr::mutate(seg_rat_int_all = dplyr::case_when(G == "Primary" ~ seg_rat*prim_ploidy,
G == "Recurrence" ~ seg_rat*rec_ploidy))
xy_df_long$seg_rat_int = round(xy_df_long$seg_rat_int)
xy_df_long$seg_rat_int_all = round(xy_df_long$seg_rat_int_all)
# convert back to wide
xy_df_int = reshape2::recast(xy_df_long, cells ~ seg, measure.var = "seg_rat_int")
xy_df_int1 = xy_df_int %>% dplyr::select(-cells)
rownames(xy_df_int1) = xy_df_int$cells
xy_df_int_all = reshape2::recast(xy_df_long, cells ~ seg, measure.var = "seg_rat_int_all")
xy_df_int_all1 = xy_df_int_all %>% dplyr::select(-cells)
rownames(xy_df_int_all1) = xy_df_int_all$cells
#Groups_2<-factor(Groups[,1],levels = levels)
{return(list(int_col = t(xy_df_int1), int_all_col = t(xy_df_int_all1)))}
}
# func_extract_well -------
func_extract_well <- function(x, return_df = TRUE) {
a_well = grep(rownames(x),pattern = "*A_",ignore.case = T)
d_well = grep(rownames(x),pattern = "*D_",ignore.case = T)
pop_well = grep(rownames(x),pattern = "*POP_",ignore.case = T)
allwells = c("a_well", "d_well", "pop_well")
Well = rep(NA, nrow(x))
Well[a_well] <- "a_well"
Well[d_well] <- "d_well"
Well[pop_well] <- "pop_well"
xcluster = unique(Well)
levels = allwells[allwells %in% xcluster]
Well <- data.frame(Well); rownames(Well) <- rownames(x); table(Well); colnames(Well)<-"W"
Well[which(is.na(Well)),] <- "a_well"
Well$W = factor(Well$W, levels = levels)
Well_df = data.frame(cells = rownames(x), well = Well$W)
if(return_df == TRUE ){return(Well_df)}
if(return_df == FALSE ){return(Well)}
}
# function to make heatmap ------
fun_htmap <- function(data, chr_lengths, show_row_names, Groups, ifCN = F){
if(ifCN == T){
title = "Copy Number"
breaks = c(0,1,2,4,6)
dat_mat = as.data.frame.matrix(data$seg_cndata_int_all) %>% dplyr::mutate(across(.fns = ~case_when(. > 8 ~ "8", TRUE ~ as.character(.))))
dat_mat = as.matrix(t((dat_mat)))
col_vec = structure(c("#181C43", "#2641A2", "#247ABA", "#73A8BD", "white","#E4CAC3", "#CF8971", "#B8462D", "#850E28"), #, "#3C0912"
names = c(0:8)
)
}else{
breaks = c(-2,0,2)
dat_mat = as.matrix(t(log2(data$seg_cndata)))
title = "Log2 (Ratio)"
col_vec = circlize::colorRamp2(breaks =breaks,
c("dodgerblue3", "white", "firebrick3"))
}
data$G$cells = rownames(data$G)
row_anno_df = dplyr::left_join(data$well, data$G, by = "cells")
col_grp = c("Primary" = "maroon", "Recurrence" = "#009999")
col_peak = c("a_well" = "grey", "d_well" = "seagreen", "pop_well" = "black")
ha_row=rowAnnotation(df = row_anno_df %>% dplyr::select(G, well),
col = list(G = col_grp, well = col_peak),
show_annotation_name = T)
chr_lengths = readRDS(file = "/seg_chrom_chrompos_abspos.rds")
chr_lengths = chr_lengths %>%
dplyr::select(abspos, chrom, chrompos) %>%
dplyr::group_by(chrom) %>%
dplyr::summarize(n = dplyr::n()) %>%
pull(n)
chr_lengths = chr_lengths[-24]
chr_binary = rep(c(2,1), 11);chr_binary = c(chr_binary, 2)
chr1 = data.frame(chr = rep.int(x = chr_binary, times = chr_lengths))
# getting lengths for chr numbers annotation
chr_rl_c = c(1, cumsum(chr_lengths))
# creating a data frame to calculate rowMeans
chr_df = data.frame(a = chr_rl_c[1:length(chr_rl_c)-1],b= chr_rl_c[2:length(chr_rl_c)])
chr_l_means = round(rowMeans(chr_df))
chrom.names = c(1:22,"X")
# creating the vector for chr number annotations
v = vector(mode = "character",length = cumsum(chr_lengths)[length(chr_lengths)])
v[chr_l_means] = chrom.names
v[is.na(v)] = ""
chr_bar = HeatmapAnnotation(chr_text = anno_text(v, gp = gpar(fontsize = 8)),
df = chr1,
show_legend = FALSE,
which = "column",
col = list(chr = c("1" = "grey88", "2" = "black")))
ht_all = Heatmap(dat_mat,
cluster_columns = FALSE,
border = TRUE,
cluster_rows = FALSE,
show_row_names = show_row_names,
row_names_gp = gpar(fontsize = 5),
row_names_side = "left",
show_column_names = FALSE,
use_raster = TRUE,
top_annotation = chr_bar,
left_annotation = ha_row,
col = col_vec,
heatmap_legend_param = list(title = title, title_gp = gpar(fontsize = 15, fontface = "bold"),
labels_gp = gpar(fontsize = 12)))
pdf(file = paste0("CN_",ifCN, "_nonfilter_heatmap0.pdf"), width = 10, height = 8)
hm = draw(ht_all, heatmap_legend_side = "right")
print(hm)
dev.off()
}
# func_rowAnno ---------
func_rowAnno <- function(clusterT = NA, clusterN = NA, timepoint_info, ifCF=F, ifCL =F){
if(ifCF == T){
#set color
Groups_set <- c("Primary","Recurrence")
col<-c("maroon", "#009999")
names(col) <- Groups_set
}
else{
#set color
Groups_set <- c("Primary", "Recurrence")
col<-c("maroon", "#009999")
col = as.vector(col)
names(col) <- Groups_set
}
if(ifCL == T){
n_colors = length(unique(clusterT))
row_col = c(pinks, escher, ernst)[1:n_colors]
names(row_col) = 1:n_colors
heat_row_col <- rowAnnotation(
Cl = c(clusterT),
Tx = timepoint_info,
col = list(Cl = row_col,
Tx= col),
annotation_legend_param = list(Cl = list(title_gp = gpar(fontsize = 12, fontface = "bold"),
labels_gp = gpar(fontsize = 12)),
Tx = list(title_gp = gpar(fontsize = 12, fontface = "bold"),
labels_gp = gpar(fontsize = 12))))
}
else{
heat_row_col <- rowAnnotation(
Tx = timepoint_info,
col = list(Tx= col),
annotation_legend_param = list(
Tx = list(title_gp = gpar(fontsize = 12,fontface = "bold"),
labels_gp = gpar(fontsize = 12))))
}
return(heat_row_col)
}
# Heatmap_hclust ----------
library(circlize)
library(ComplexHeatmap)
func_htmap_clus <- function(sample, FC,
data, chr_lengths, row_dend, row_split, ha_row, ifCN = F,
show_row_names = FALSE){
if(ifCN == T){
breaks = c(0,1,3)
}else{breaks = c(-1.5,0,1.5)}
chr_lengths = chr_lengths %>%
dplyr::select(abspos, chrom, chrompos) %>%
dplyr::group_by(chrom) %>%
dplyr::summarize(n = dplyr::n()) %>%
pull(n)
chr_lengths1 = chr_lengths[-24]
chr_binary = rep(c(2,1), 11);chr_binary = c(chr_binary, 2)
chr = data.frame(chr = rep.int(x = chr_binary, times = chr_lengths1))
# getting lengths for chr numbers annotation
chr_rl_c = c(1, cumsum(chr_lengths))
# creating a data frame to calculate rowMeans
chr_df = data.frame(a = chr_rl_c[1:length(chr_rl_c)-1],b= chr_rl_c[2:length(chr_rl_c)])
chr_l_means = round(rowMeans(chr_df))
chrom.names = c(1:22,"X")
# creating the vector for chr number annotations
v = vector(mode = "character",length = cumsum(chr_lengths1)[length(chr_lengths1)])
v[chr_l_means] = chrom.names
v[is.na(v)] = ""
chr_bar = HeatmapAnnotation(chr_text = anno_text(v, gp = gpar(fontsize = 8)),
df = chr,
show_legend = FALSE,
which = "column",
col = list(chr = c("1" = "grey88", "2" = "black")))
col_fun = circlize::colorRamp2(breaks = breaks, c("dodgerblue3", "white", "firebrick3"))
lgd = Legend(col_fun = col_fun, title = "logRatio")
if (show_row_names == FALSE){ ht = Heatmap(as.matrix(log2(data)),
name = "logRatio",
row_title = "single cells",
column_title = "genomic coordinates",
column_title_side = "bottom",
cluster_rows = row_dend,
row_title_gp = gpar(col = c("hotpink1", "royalblue", "darkgreen","purple","pink","yellow","orange"), font = 1:3),
row_split = row_split,
circlize::colorRamp2(breaks = breaks,
c("dodgerblue3", "white", "firebrick3")),
top_annotation = chr_bar,
left_annotation = ha_row,
show_row_dend = TRUE, row_dend_width=unit(40,"mm"), cluster_columns = FALSE,
show_row_names = F, show_column_names = FALSE, use_raster = TRUE)}
else { ht = Heatmap(as.matrix(log2(data)),
cluster_rows = row_dend,
name = "logRatio",
row_title = "single cells",
column_title = "genomic coordinates",
column_title_side = "bottom",
row_title_gp = gpar(col = c("hotpink1", "royalblue", "darkgreen","purple","pink","yellow","orange"), font = 1:3),
row_split = row_split,
circlize::colorRamp2(breaks = breaks,
c("dodgerblue3", "white", "firebrick3")),
top_annotation = chr_bar,
left_annotation = ha_row,
show_row_dend = TRUE, row_dend_width=unit(40,"mm"), cluster_columns = FALSE,
show_row_names = T, show_column_names = FALSE, use_raster = TRUE)}
pdf(file = paste0("CF_NULL_CN_",ifCN, "_filter_heatmap1.pdf"), width = 10, height = 8)
hm = draw(ht, heatmap_legend_side = "right")
print(hm)
dev.off()
}
# filter for outlier cells --------
filter_density<-function(object, resolution = 0.8, k = 5){
seg = object$seg_cndata
dst <- cor(seg)
df_dist <- apply(as.matrix(dst), 1, function(x) {
mean(sort(x, decreasing = T)[2:(k + 1)])
}) %>% tibble::enframe(name = "sample", value = "cor")
df_dist <- df_dist %>% dplyr::mutate(filtered = dplyr::case_when(cor >= resolution ~ "kept", cor < resolution ~ "removed"))
object$df_dist = df_dist
data<-as.data.frame(t(object$seg_cndata));dim(data);rownames(data)
d <- amap::Dist(data, method = "manhattan", nbproc = 50)
row_dend = hclust(d, method = "ward.D2");row_dend = color_branches(row_dend, k = 10)
object$well$cells = as.character(object$well$cells)
object$well$well = as.character(object$well$well)
object$well = tibble(object$well)
df_dist2 = dplyr::left_join(df_dist, object$well, by = c("sample" = "cells"))
col_fil = c("kept" = "#a8e6cf", "removed" = "firebrick3")
col_peak = c("a_well" = "grey", "d_well" = "seagreen", "pop_well" = "black")
heat_row_col = ComplexHeatmap::rowAnnotation(df = df_dist2 %>% dplyr::select(filtered, well) %>% as.data.frame(),
col = list(filtered = col_fil, well = col_peak),
show_annotation_name = T)
object$seg_cndata_fil = object$seg_cndata[,which(df_dist$filtered == 'kept')]
return(object)
}
func_umaps<-function(x, colour, title, shape, group){
if(shape == F){
x %>%
rownames_to_column(var = "Cell") %>%
ggplot() +
geom_point(aes(x = umap1,
y = umap2,
colour = colour
#shape = "21"
),
size = 0.2,#alpha = 0.5,stroke = 0.5,
show.legend = T) +
# geom_text(aes(x = umap1,
# y = umap2, label=colour), size=3) +
guides(color = guide_legend(title = title, override.aes = list(size=5))) +
theme(legend.position = "right",
legend.background = element_blank(),
legend.title = element_text(size=15),
legend.text = element_text(size=10),
legend.key = element_blank(),
panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.line = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = 20),
axis.ticks = element_blank(),
axis.title=element_text(size=20))
}
else{
x %>%
rownames_to_column(var = "Cell") %>%
ggplot() +
geom_point(aes(x = umap1,
y = umap2,
colour = colour,
shape = group),
size = 1,
show.legend = T) +
guides(color = guide_legend(title = title, override.aes = list(size=5))) +
theme(legend.position = "right",
legend.background = element_blank(),
legend.title = element_text(size=15),
legend.text = element_text(size=10),
legend.key = element_blank(),
panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.line = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = 20),
axis.ticks = element_blank(),
axis.title=element_text(size=20))
}
}
func_htmap <- function(ht.data, split, heat_row_col,
#top_annotation = readRDS(file = "/volumes/lab/users/tapsi/projects/ecis/analysis/20200610_dcis_cnv/king01_v3/chr_bar.rds"),
chr_top_annotation = chr_bar,
gene_annotation = NULL,
heatmap_legend_side = "right",
ifconcensus = F, save_pdf = TRUE, peak = T, ifgene_anno = F){
breaks = c(-1.5,0,1.5)
dat_mat = as.matrix(t(log2(ht.data)))
title = "Log2 (Ratio)"
col_vec = circlize::colorRamp2(breaks = breaks,
c("dodgerblue3", "white", "firebrick3"))
ht = Heatmap(t(dat_mat),
# cluster_rows = function(x){
# fastcluster::hclust(amap::Dist(x, method = "manhattan"), method = "ward.D2")
# },
cluster_columns = FALSE,
border = TRUE,
cluster_rows = FALSE,
name = title,
row_title = "single cells",
column_title = "genomic coordinates",
column_title_side = "bottom",
show_row_names = F,
row_names_gp = gpar(fontsize = 8),
row_names_side = "right",
show_column_names = F,
split = split,
show_parent_dend_line = FALSE,
use_raster = TRUE,
top_annotation = chr_top_annotation,
bottom_annotation = gene_annotation,
left_annotation = heat_row_col,
col = col_vec,
heatmap_legend_param = list(title = title, title_gp = gpar(fontsize = 12, fontface = "bold"),
labels_gp = gpar(fontsize = 12)))
hm = draw(ht, heatmap_legend_side = heatmap_legend_side)
pdf(file = paste0("clusteredhtmap_paper1.pdf"), width = 8, height = 7)
print(hm)
dev.off()
return(hm)
}
# consensus heatmaps
func_consensus <- function(ht.data, split, heat_row_col,
chr_top_annotation = chr_bar,
anno_bar_df = df_grp_count_prop,
gene_annotation = NULL,
heatmap_legend_side = "right",
gene_anno = TRUE){
breaks = c(-1.5,0,1.5)
dat_mat = as.matrix(t(log2(ht.data)))
title = "Log2 (Ratio)"
col_vec = circlize::colorRamp2(breaks = breaks,
c("dodgerblue3", "white", "firebrick3"))
col_tp = c("Primary" = "maroon", "Recurrence" = "#009999")
if(bar_anno == T){
ha = HeatmapAnnotation('Cell counts' = anno_barplot(anno_bar_df, which = "row", gp=gpar(fill=col_tp,border = NA, lty = "blank")), border = F, which = "row",
width = unit(1.2, "cm"),
#size = unit(0.95, "mm"),
#bar_width = 0.85,
show_legend = T)
legend_list = list(Legend(labels = c(names(col_tp)), title = "Timepoint", legend_gp =gpar(fill=col_tp)))
ht = Heatmap(dat_mat,
cluster_columns = FALSE,
border = TRUE,
cluster_rows = FALSE,
name = title,
row_title = "Subclones",
column_title = "genomic coordinates",
column_title_side = "bottom",
show_row_names = F,
row_names_gp = gpar(fontsize = 8),
row_names_side = "right",
show_column_names = F,
show_parent_dend_line = FALSE,
use_raster = TRUE,
bottom_annotation = gene_annotation,
top_annotation = chr_top_annotation,
left_annotation = heat_row_col,
right_annotation = ha,
col = col_vec,
heatmap_legend_param = list(title = title, title_gp = gpar(fontsize = 12, fontface = "bold"),
labels_gp = gpar(fontsize = 12)))
}
hm = draw(ht, heatmap_legend_side = heatmap_legend_side, annotation_legend_list = legend_list)
pdf(file = paste0("consensus_clusteredhtmap.pdf"), width = 10, height = 4.5)
print(hm)
dev.off()
return(hm)
}
gene_anno <- function(gene_list, remove_Y = TRUE) {
bins_in_cna_pipeline <- read.delim("bins_in_cna_pipeline_bands.txt", sep = "\t",
stringsAsFactors = FALSE,
header = T)
library(GenomicRanges)
library(Homo.sapiens)
hg_genes_grange <- genes(Homo.sapiens, columns = "SYMBOL")
hg_genes_grange_df <- as.data.frame(hg_genes_grange)
hg_genes_grange_df$SYMBOL <- unlist(unname(as.vector(hg_genes_grange_df$SYMBOL)))
gene_anno_df <- hg_genes_grange_df[which(hg_genes_grange_df$SYMBOL %in% gene_list),]
names(gene_anno_df)[6] <- "gene"
if (remove_Y == TRUE){
bins_in_cna_pipeline <- bins_in_cna_pipeline %>% dplyr::filter(chr != "chrY")
}
genes.annotation = character(length = nrow(bins_in_cna_pipeline))
for (i in 1:nrow(bins_in_cna_pipeline)) {
for (j in 1:nrow(gene_anno_df)) {
if ((findInterval(gene_anno_df$start[j],c(bins_in_cna_pipeline$start[i], bins_in_cna_pipeline$end[i])) == 1) && (bins_in_cna_pipeline$chr[i] == gene_anno_df$seqnames[j])){
genes.annotation[i] <- as.character(gene_anno_df$gene[j])
}
}
}
genes.annotation[genes.annotation == ""] <- NA
labels <- genes.annotation[!is.na(genes.annotation)]
positions <- which(!is.na(genes.annotation))
padding = unit.c(unit(2, "cm"), unit(2, "cm"),
unit(c(2, 1), "cm"))
gene_anno_return <- HeatmapAnnotation(link = anno_mark(at = positions, side = "bottom", labels = labels, labels_gp = gpar(fontsize = 10, col = "black")), height = unit(2, "cm"), which = "column")
return(gene_anno_return)
}
library("ape")
func_make_tree <- function(x,add.edge = 100, dismethod = "manhattan", tree_method = "NJ"){
clusters_consensus = x$clusters_consensus
clusters_consensus = clusters_consensus
colnames(clusters_consensus) = c(1:ncol(clusters_consensus))
outgroupclone = x$outgroupclone
cluster_col = x$cluster_col
seg_data <- clusters_consensus %>% as.data.frame()
normal1 = data.frame(normal1 = c(rep(1, dim(clusters_consensus)[1])))
seg_data1 = cbind(seg_data, normal1)
colnames(seg_data1) = c(1:length(seg_data),"normal")
dist_mat <- amap::Dist(t(seg_data1), method = dismethod, nbproc = 25)
tree <- ape::nj(dist_mat)
# define the root
tree_r <- ape::root(tree, outgroup = "normal", resolve.root = TRUE) # outgroupclone
tree_r <- ape::drop.tip(tree_r, "normal")
tree_r$edge.length = tree_r$edge.length+add.edge
tree_r$root.edge = add.edge
#plot(tree_r)
list_samples <- split(rownames(t(clusters_consensus)), rownames(t(clusters_consensus)))
names(list_samples) = c(1:length(list_samples))
tree_r <- ggtree::groupOTU(tree_r, list_samples); x$tree_r = tree_r
treeplt<-ggtree::ggtree(tree_r,
ladderize = FALSE,
size = .6, root.position = 0) +
ggtree::geom_tippoint(aes(color = group), size = 6) +
ggtree::geom_tiplab(size=5, color="black")+
scale_colour_manual(values = c(cluster_col)) +
theme(legend.position = "right") +
labs(color = "Clones") + geom_rootedge()
print(treeplt)
message("saving tree plot...")
x$treeplt = treeplt
return(x)
}