inst/shinyapp/make_lace_final_input.R
In BIMIB-DISCo/LACE: Longitudinal Analysis of Cancer Evolution (LACE)
#################################################
########## NA2 ###########
#################################################
NA_compute2_load <- function(data_dir, depth_dir, out_dir) {
browser()
dir.create(out_dir, showWarnings = FALSE)
if (!dir.exists(out_dir))
showNotification(paste("Filtered data output folder,", out_dir, ", cannot be created"), duration = 10, type = "warning")
# load data
cells_aggregate_info <- NULL
snpMut_filt_freq <- NULL
depth <-NULL
if (file.exists(file.path( data_dir, "cells_aggregate_info.rds")))
cells_aggregate_info <- readRDS(file=paste0(file.path( data_dir, "cells_aggregate_info.rds")))
if (file.exists(file.path( out_dir, "snpMut_filt_freq_reduced.rds")))
snpMut_filt_freq <- readRDS(file=paste0(file.path( out_dir, "snpMut_filt_freq_reduced.rds")))
if (file.exists(file.path( depth_dir, "final_data_depth.txt")))
depth <- as.matrix(read.table( file.path(depth_dir, "final_data_depth.txt")))
return( list(cells_aggregate_info=cells_aggregate_info, snpMut_filt_freq=snpMut_filt_freq, depth=depth))
}
NA_compute2 <- function(depth_minimum, minumum_median_total, minumum_median_mutation, data_dir, depth_dir, out_dir, time_points, verified_genes, non_NA_genes, files) {
browser()
cells_aggregate_info <- files$cells_aggregate_info
snpMut_filt_freq <- files$snpMut_filt_freq
depth <- files$depth
# set working directory
#baseDir = "/data_directory/BimiB/share/LACE/MELANOMA/"
#setwd(baseDir)
# load required libraries
# library("TRONCO")
# dir.create(out_dir, showWarnings = F)
# if (!dir.exists(out_dir))
# showNotification(paste("Filtered data output folder,", out_dir, ", cannot be created"), duration = 10, type = "warning")
# list of manually verified mutations
#verified_genes = c("ARPC2","CCT8","COL1A2","CYCS","HNRNPC","PCBP1","PRAME","RPL5")
#depth_minimum = 3 # minimum depth to set values to NA
#minumum_median_total = 10 # minimum median depth for total reads
#minumum_median_mutation = 4 # minimum median depth for reads supporting mutations
# load data
# cells_aggregate_info <- readRDS(file=paste0(file.path( data_dir, "cells_aggregate_info.rds")))
# snpMut_filt_freq <- readRDS(file=paste0(file.path( data_dir, "snpMut_filt_freq.rds")))
# depth = as.matrix(read.table( file.path(depth_dir, "final_data_depth.txt")))
# select only verified mutations
if (!is.null(non_NA_genes))
snpMut_filt_freq = snpMut_filt_freq[
which(snpMut_filt_freq$Gene %in% non_NA_genes),
c("scID","Time","Gene","Chr","PosStart","PosEnd","REF",
"ALT","MutType","depth","Allele_Ratio")
]
snpMut_filt_freq = snpMut_filt_freq[order(snpMut_filt_freq[,3],snpMut_filt_freq[,4],snpMut_filt_freq[,5],snpMut_filt_freq[,6],snpMut_filt_freq[,7],snpMut_filt_freq[,8],snpMut_filt_freq[,9],snpMut_filt_freq[,1],snpMut_filt_freq[,2],snpMut_filt_freq[,10],snpMut_filt_freq[,11]),]
# compute frequency of each mutation
#distinct_mutations = unique(snpMut_filt_freq[,c("Gene","Chr","PosStart","PosEnd","REF","ALT","ALT","ALT","ALT","ALT","ALT","ALT")])
distinct_mutations = unique(snpMut_filt_freq[,c("Gene","Chr","PosStart","PosEnd","REF","ALT")])
if (nrow(distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
#time_points=c("before treatment", "4d on treatment", "28d on treatment", "57d on treatment")
#for (t in seq(1,length(time_points)) )
# distinct_mutations[[paste0('FreqT',t)]] = NA
#distinct_mutations[['MedianDepth']] = NA
#distinct_mutations[['MedianDepthMut']] = NA
#colnames(distinct_mutations)[7] = "FreqT1"
#colnames(distinct_mutations)[8] = "FreqT2"
#colnames(distinct_mutations)[9] = "FreqT3"
#colnames(distinct_mutations)[10] = "FreqT4"
#colnames(distinct_mutations)[11] = "MedianDepth"
#colnames(distinct_mutations)[12] = "MedianDepthMut"
#cells_timepoints = unique(snpMut_filt_freq[,c("scID","Time")])
#distinct_mutations$FreqT1 = NA
#distinct_mutations$FreqT2 = NA
#distinct_mutations$FreqT3 = NA
#distinct_mutations$FreqT4 = NA
#distinct_mutations$MedianDepth = NA
#distinct_mutations$MedianDepthMut = NA
#times = list()
#for (t in time_points)
# times[[t]] = as.numeric(table(cells_timepoints$Time)[t])
# browser()
#
# for(i in 1:nrow(distinct_mutations)) {
# curr = snpMut_filt_freq[
# which(
# snpMut_filt_freq$Gene %in% distinct_mutations[i,"Gene"] &
# snpMut_filt_freq$Chr %in% distinct_mutations[i,"Chr"] &
# snpMut_filt_freq$PosStart %in% distinct_mutations[i,"PosStart"] &
# snpMut_filt_freq$PosEnd %in% distinct_mutations[i,"PosEnd"] &
# snpMut_filt_freq$REF %in% distinct_mutations[i,"REF"] &
# snpMut_filt_freq$ALT %in% distinct_mutations[i,"ALT"]),]
#
# for (t in seq(1,length(time_points)) )
# distinct_mutations[i,paste0('FreqT',t)] = as.numeric(table(curr$Time)[time_points[[t]]]) / times[[t]]
#
# #distinct_mutations[i,"FreqT1"] = as.numeric(table(curr$Time)["before treatment"]) / t1
# #distinct_mutations[i,"FreqT2"] = as.numeric(table(curr$Time)["4d on treatment"]) / t2
# #distinct_mutations[i,"FreqT3"] = as.numeric(table(curr$Time)["28d on treatment"]) / t3
# #distinct_mutations[i,"FreqT4"] = as.numeric(table(curr$Time)["57d on treatment"]) / t4
# #print('a')
# #print(curr$depth)
# #print(median(curr$depth))
# #print(median(curr$Allele_Ratio))
# distinct_mutations[i,"MedianDepth"] = as.numeric(median(curr$depth))
# distinct_mutations[i,"MedianDepthMut"] = as.numeric(median(curr$Allele_Ratio))
# }
distinct_mutations <- snpMut_filt_freq %>%
group_by(Time) %>%
mutate(tot_per_time=n_distinct(scID)) %>% #cells per time point
group_by(Gene,Chr,PosStart,PosEnd,REF,ALT,tot_per_time) %>%
add_count(Time) %>% #cells per mutations
mutate(fr=n/tot_per_time) %>%
group_by(Gene,Chr,PosStart,PosEnd,REF,ALT) %>%
mutate(MedianDepth=median(depth), MedianDepthMut=median(Allele_Ratio)) %>%
ungroup() %>%
select(-c(scID,MutType, Allele_Ratio, depth, n, tot_per_time)) %>% group_by_all() %>%
mutate(Time= as.integer(factor(Time, levels = time_points, ordered = TRUE))) %>%
distinct() %>%
#summarise() %>% #arrange(Time) %>%
pivot_wider(names_from = Time, values_from = fr, names_prefix = "FreqT")
# distinct_mutations %>% add_count(Time, name= tot_per_time) %>%
# arrange(name) %>%
# group_by(Gene,Chr,PosStart,PosEnd,REF,ALT,Time) %>%
# pivot_wider(names_from = wool, values_from = Time,
#
# mutate( )%>%
#
# group_by(Gene,Chr,PosStart,PosEnd,REF,ALT) %>%
# mutate(MedianDepth"] = median(depth)) %>%
# mutate(MedianDepth"] = median(Allele_Ratio))
#
#distinct_mutations = distinct_mutations[-c(3,5,6,8),]
# if (
# length(
# sort(
# unique(
# c(
# which(distinct_mutations$MedianDepth<minumum_median_total),
# which(distinct_mutations$MedianDepthMut<minumum_median_mutation)
# )
# )
# )
# )>0)
# distinct_mutations = distinct_mutations[
# -sort(
# unique(c(
# which(distinct_mutations$MedianDepth<minumum_median_total),
# which(distinct_mutations$MedianDepthMut<minumum_median_mutation)
# ))
# ),]
distinct_mutations <- distinct_mutations %>%
filter(MedianDepth>=minumum_median_total & MedianDepthMut>=minumum_median_mutation) %>%
ungroup()
if (nrow(distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
#rownames(distinct_mutations) = 1:nrow(distinct_mutations)
valid_distinct_mutations = distinct_mutations
if (!is.null(verified_genes))
valid_distinct_mutations = subset(distinct_mutations,distinct_mutations$Gene %in% verified_genes) %>% ## mod
filter(REF!="-") ## mod
if (nrow(valid_distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
valid_distinct_mutations_values = NULL
for(i in 1:nrow(valid_distinct_mutations)) {
valid_distinct_mutations_values = c(valid_distinct_mutations_values,paste0(valid_distinct_mutations[i,c("Chr","PosStart")],collapse="_"))
}
save(valid_distinct_mutations,file=file.path(out_dir,"valid_distinct_mutations.RData"))
# make final mutations data structures
#load("valid_clones_mapping.RData")
#mutations = array(0,c(length(unique(valid_clones_mapping$Run)),6))
#rownames(mutations) = sort(unique(valid_clones_mapping$Run))
#colnames(mutations) = paste0(valid_distinct_mutations$Gene,"_",valid_distinct_mutations_values,"_",valid_distinct_mutations$REF,"_",valid_distinct_mutations$ALT)
mutations = array(0,c(length(unique(colnames(depth))),nrow(valid_distinct_mutations)))
rownames(mutations) = sort(unique(colnames(depth)))
colnames(mutations) = paste0(valid_distinct_mutations$Gene,"_",valid_distinct_mutations_values,"_",valid_distinct_mutations$REF,"_",valid_distinct_mutations$ALT)
#mutations = array(0,c(nrow(valid_distinct_mutations), length(unique(o_snpMut_filt_freq$scID))))
#colnames(mutations) = sort(unique(o_snpMut_filt_freq$scID))
#rownames(mutations) = valid_distinct_mutations_values
#mutations = mutations[valid_distinct_mutations_values,]
#browser()
# set NA values
depth = t(depth)
#dim(depth)
#depth = depth[colnames(mutations), rownames(mutations), drop=FALSE]
depth = depth[sort(unique(snpMut_filt_freq$scID)), valid_distinct_mutations_values, drop=FALSE]
mutations <- inner_join(cells_aggregate_info, valid_distinct_mutations) %>%
filter( scID %in% rownames({{depth}}))%>%
mutate(idx = paste(Gene,Chr,PosStart,REF,ALT,sep='_')) %>%
mutate(val=1) %>%
distinct() %>%
#select(scID,idx,val) %>%
pivot_wider(id_cols=scID, names_from = idx, values_from=val, values_fill=0) %>%
full_join(data.frame(scID=rownames({{depth}})))
mutations[is.na(mutations)] <- 0
mutations=data.frame(mutations,row.names = 1)
col_n <-
apply(str_split(colnames(mutations), pattern = "_", simplify = TRUE)[,2:3, drop=FALSE], 1, paste, collapse="_")
depth = depth[rownames(mutations), col_n, drop=FALSE]
colnames(depth) = colnames(mutations)
mutations[which(depth<=depth_minimum,arr.ind=TRUE)] = NA # missing values rate equals to 359/2850, that is approx 12.6%
cells_aggregate_info = cells_aggregate_info[which(cells_aggregate_info$scID%in%rownames(mutations)), , drop=FALSE]
mycellsdata = list()
for ( t in time_points )
mycellsdata[[t]] = as.matrix.data.frame(mutations[sort(unique(cells_aggregate_info$scID[which(cells_aggregate_info$Time==t)])), , drop=FALSE], rownames.force = TRUE)
D = mycellsdata
#browser()
save(D,file=file.path(out_dir,"D.RData"))
####
#browser()
g <- plot_D(D)
####
# make Oncoprint
n_rows=0
for (t in seq(1, length(time_points)))
n_rows=n_rows+nrow(D[[t]])
clusters = array(NA,c(n_rows,1))
init_i=1
end_i=0
r_names= NULL
data = NULL
for (t in seq(1, length(time_points))) {
if(nrow(D[[t]])>0) {
end_i <- end_i+nrow(D[[t]])
r_names=c(r_names, rownames(D[[t]]))
clusters[init_i:end_i,1] = time_points[[t]]
init_i <- end_i+1
data <- rbind(data, D[[t]])
}
}
rownames(clusters) <- r_names
#clusters[1:nrow(D[[1]]),1] = "T1_before_treatment"
#clusters[(nrow(D[[1]])+1):(nrow(D[[1]])+nrow(D[[2]])),1] = "T2_4_days_treatment"
#clusters[(nrow(D[[1]])+nrow(D[[2]])+1):(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])),1] = "T3_28_days_treatment"
#clusters[(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])+1):(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])+nrow(D[[4]])),1] = "T4_57_days_treatment"
#rownames(clusters) = c(rownames(D[[1]]),rownames(D[[2]]),rownames(D[[3]]),rownames(D[[4]]))
#data = rbind(D[[1]],D[[2]],D[[3]],D[[4]])
#data[which(is.na(data))] = 0
#data = import.genotypes(data)
# data = annotate.stages(data,clusters)
## if (ncol(data$genotypes)>1 || length(unique(data$genotypes[,1]))>1) #errore generico
## oncoprint(data,excl.sort=FALSE,group.by.stage=TRUE)
#distinct_mutations[['ResultantMeanDepth']]<-apply(depth,2,mean)
#distinct_mutations[['ResultantVarDepth']]<-apply(depth,2,var)
#browser()
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
return(list("distinct_mutations"=distinct_mutations, "g"=g))
}
plot_D <-function(D) {
D <- D %>%
setNames({names(.) %>%
str_replace_all(pattern = "_", replacement = " ") %>%
data.frame("times"=.) %>%
mutate(
times=ifelse(str_starts(times, pattern = "T[0-9] ", negate = TRUE),
paste0('T', row_number(), " ",times), times)
)}$times)
#names(D) <- t_order$times
D1 <- lapply(D, FUN=function(x){as_tibble(x,rownames = "cell")}) %>%
bind_rows(.id = "time") %>%
mutate(time=forcats::fct_inorder(factor(time, ordered=TRUE))) %>%
relocate(cell, .after = last_col()) %>% ##
arrange(across(everything())) %>%
mutate(ordered=factor(cell, levels = unique(cell, fromLast=TRUE), ordered = TRUE))
#D1 <- D1 %>%
# mutate(ordered=cell) %>%
# arrange(across(everything())) %>%
# mutate(ordered=factor(ordered, levels = unique(ordered, fromLast=TRUE), ordered = TRUE))
s <- D1 %>% group_by(time) %>%
summarise(across(where(is.numeric), ~sum(.,na.rm = TRUE))) %>%
pivot_longer(-1) %>%
pivot_wider(names_from = 1, values_from = value) #%>%
#arrange(across(everything())) not needed
Dw <- D1 %>%
pivot_longer( col = -c(time, cell,ordered), names_to = "mutation", values_to = "value") %>%
mutate(value = factor(as.integer(value), levels=c(0,1,"NA"), exclude = NULL, ordered = T)) %>%
mutate(mutation = factor(mutation, levels=s$name, ordered=TRUE))
per_mut_site <- as.data.frame(Dw) %>% group_by(mutation)
n_na <- (per_mut_site %>% mutate(cnt=sum(is.na(value))) )$cnt
n_one <- (per_mut_site %>% mutate(cnt=sum(value==1, na.rm = TRUE)) )$cnt
n_zero <- (per_mut_site %>% mutate(cnt=sum(value==0, na.rm = TRUE)) )$cnt
str__na <- paste0("# \"NA\" = [", paste(range(n_na), collapse = ", "),"]")
str__zero <- paste0("# \"0\" = [", paste(range(n_zero), collapse = ", "),"]")
str__one <- paste0("# \"1\" = [", paste(range(n_one), collapse = ", "),"]")
g <- ggplot(Dw, aes(mutation, y=ordered, fill= value)) +
geom_tile()+
scale_fill_manual(values = c("black", "white", "orange"), na.value="gray50",
aesthetics = c("colour", "fill")#,
#labels = c("passing filter(s)", "filtered out", "NA")
)+ #orange=2
facet_grid(
rows = vars(time),
scales = "free",
space = "free",
labeller = label_wrap_gen(width=15)
) +
scale_x_discrete(expand = c(0,0))+
theme(axis.text.x = element_text(angle=45, hjust=1), ##it is shifted why?
axis.text.y = element_blank(),
axis.ticks.y= element_blank()
)+
theme(
strip.background = element_rect(
color="black", fill="#FC8207", size=1.5, linetype="solid"
),
panel.border = element_rect(color = "#FC8207", fill = NA, size = 2)
) +
theme(legend.key=element_rect(colour="black")) +
theme(panel.spacing = unit(0, "pt"))+
labs(y = "cells")+
labs(x = "selected mutations")+
#labs(title = paste("Pre-inference input matrix {",str__na, ";", str__one, ";", str__zero,"}"))+
labs(title = paste("Filtered binary data matrix"))+
theme(text = element_text(size = 14))
return(g)
}
BIMIB-DISCo/LACE documentation built on Nov. 1, 2024, 11:06 p.m.
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#################################################
########## NA2 ###########
#################################################
NA_compute2_load <- function(data_dir, depth_dir, out_dir) {
browser()
dir.create(out_dir, showWarnings = FALSE)
if (!dir.exists(out_dir))
showNotification(paste("Filtered data output folder,", out_dir, ", cannot be created"), duration = 10, type = "warning")
# load data
cells_aggregate_info <- NULL
snpMut_filt_freq <- NULL
depth <-NULL
if (file.exists(file.path( data_dir, "cells_aggregate_info.rds")))
cells_aggregate_info <- readRDS(file=paste0(file.path( data_dir, "cells_aggregate_info.rds")))
if (file.exists(file.path( out_dir, "snpMut_filt_freq_reduced.rds")))
snpMut_filt_freq <- readRDS(file=paste0(file.path( out_dir, "snpMut_filt_freq_reduced.rds")))
if (file.exists(file.path( depth_dir, "final_data_depth.txt")))
depth <- as.matrix(read.table( file.path(depth_dir, "final_data_depth.txt")))
return( list(cells_aggregate_info=cells_aggregate_info, snpMut_filt_freq=snpMut_filt_freq, depth=depth))
}
NA_compute2 <- function(depth_minimum, minumum_median_total, minumum_median_mutation, data_dir, depth_dir, out_dir, time_points, verified_genes, non_NA_genes, files) {
browser()
cells_aggregate_info <- files$cells_aggregate_info
snpMut_filt_freq <- files$snpMut_filt_freq
depth <- files$depth
# set working directory
#baseDir = "/data_directory/BimiB/share/LACE/MELANOMA/"
#setwd(baseDir)
# load required libraries
# library("TRONCO")
# dir.create(out_dir, showWarnings = F)
# if (!dir.exists(out_dir))
# showNotification(paste("Filtered data output folder,", out_dir, ", cannot be created"), duration = 10, type = "warning")
# list of manually verified mutations
#verified_genes = c("ARPC2","CCT8","COL1A2","CYCS","HNRNPC","PCBP1","PRAME","RPL5")
#depth_minimum = 3 # minimum depth to set values to NA
#minumum_median_total = 10 # minimum median depth for total reads
#minumum_median_mutation = 4 # minimum median depth for reads supporting mutations
# load data
# cells_aggregate_info <- readRDS(file=paste0(file.path( data_dir, "cells_aggregate_info.rds")))
# snpMut_filt_freq <- readRDS(file=paste0(file.path( data_dir, "snpMut_filt_freq.rds")))
# depth = as.matrix(read.table( file.path(depth_dir, "final_data_depth.txt")))
# select only verified mutations
if (!is.null(non_NA_genes))
snpMut_filt_freq = snpMut_filt_freq[
which(snpMut_filt_freq$Gene %in% non_NA_genes),
c("scID","Time","Gene","Chr","PosStart","PosEnd","REF",
"ALT","MutType","depth","Allele_Ratio")
]
snpMut_filt_freq = snpMut_filt_freq[order(snpMut_filt_freq[,3],snpMut_filt_freq[,4],snpMut_filt_freq[,5],snpMut_filt_freq[,6],snpMut_filt_freq[,7],snpMut_filt_freq[,8],snpMut_filt_freq[,9],snpMut_filt_freq[,1],snpMut_filt_freq[,2],snpMut_filt_freq[,10],snpMut_filt_freq[,11]),]
# compute frequency of each mutation
#distinct_mutations = unique(snpMut_filt_freq[,c("Gene","Chr","PosStart","PosEnd","REF","ALT","ALT","ALT","ALT","ALT","ALT","ALT")])
distinct_mutations = unique(snpMut_filt_freq[,c("Gene","Chr","PosStart","PosEnd","REF","ALT")])
if (nrow(distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
#time_points=c("before treatment", "4d on treatment", "28d on treatment", "57d on treatment")
#for (t in seq(1,length(time_points)) )
# distinct_mutations[[paste0('FreqT',t)]] = NA
#distinct_mutations[['MedianDepth']] = NA
#distinct_mutations[['MedianDepthMut']] = NA
#colnames(distinct_mutations)[7] = "FreqT1"
#colnames(distinct_mutations)[8] = "FreqT2"
#colnames(distinct_mutations)[9] = "FreqT3"
#colnames(distinct_mutations)[10] = "FreqT4"
#colnames(distinct_mutations)[11] = "MedianDepth"
#colnames(distinct_mutations)[12] = "MedianDepthMut"
#cells_timepoints = unique(snpMut_filt_freq[,c("scID","Time")])
#distinct_mutations$FreqT1 = NA
#distinct_mutations$FreqT2 = NA
#distinct_mutations$FreqT3 = NA
#distinct_mutations$FreqT4 = NA
#distinct_mutations$MedianDepth = NA
#distinct_mutations$MedianDepthMut = NA
#times = list()
#for (t in time_points)
# times[[t]] = as.numeric(table(cells_timepoints$Time)[t])
# browser()
#
# for(i in 1:nrow(distinct_mutations)) {
# curr = snpMut_filt_freq[
# which(
# snpMut_filt_freq$Gene %in% distinct_mutations[i,"Gene"] &
# snpMut_filt_freq$Chr %in% distinct_mutations[i,"Chr"] &
# snpMut_filt_freq$PosStart %in% distinct_mutations[i,"PosStart"] &
# snpMut_filt_freq$PosEnd %in% distinct_mutations[i,"PosEnd"] &
# snpMut_filt_freq$REF %in% distinct_mutations[i,"REF"] &
# snpMut_filt_freq$ALT %in% distinct_mutations[i,"ALT"]),]
#
# for (t in seq(1,length(time_points)) )
# distinct_mutations[i,paste0('FreqT',t)] = as.numeric(table(curr$Time)[time_points[[t]]]) / times[[t]]
#
# #distinct_mutations[i,"FreqT1"] = as.numeric(table(curr$Time)["before treatment"]) / t1
# #distinct_mutations[i,"FreqT2"] = as.numeric(table(curr$Time)["4d on treatment"]) / t2
# #distinct_mutations[i,"FreqT3"] = as.numeric(table(curr$Time)["28d on treatment"]) / t3
# #distinct_mutations[i,"FreqT4"] = as.numeric(table(curr$Time)["57d on treatment"]) / t4
# #print('a')
# #print(curr$depth)
# #print(median(curr$depth))
# #print(median(curr$Allele_Ratio))
# distinct_mutations[i,"MedianDepth"] = as.numeric(median(curr$depth))
# distinct_mutations[i,"MedianDepthMut"] = as.numeric(median(curr$Allele_Ratio))
# }
distinct_mutations <- snpMut_filt_freq %>%
group_by(Time) %>%
mutate(tot_per_time=n_distinct(scID)) %>% #cells per time point
group_by(Gene,Chr,PosStart,PosEnd,REF,ALT,tot_per_time) %>%
add_count(Time) %>% #cells per mutations
mutate(fr=n/tot_per_time) %>%
group_by(Gene,Chr,PosStart,PosEnd,REF,ALT) %>%
mutate(MedianDepth=median(depth), MedianDepthMut=median(Allele_Ratio)) %>%
ungroup() %>%
select(-c(scID,MutType, Allele_Ratio, depth, n, tot_per_time)) %>% group_by_all() %>%
mutate(Time= as.integer(factor(Time, levels = time_points, ordered = TRUE))) %>%
distinct() %>%
#summarise() %>% #arrange(Time) %>%
pivot_wider(names_from = Time, values_from = fr, names_prefix = "FreqT")
# distinct_mutations %>% add_count(Time, name= tot_per_time) %>%
# arrange(name) %>%
# group_by(Gene,Chr,PosStart,PosEnd,REF,ALT,Time) %>%
# pivot_wider(names_from = wool, values_from = Time,
#
# mutate( )%>%
#
# group_by(Gene,Chr,PosStart,PosEnd,REF,ALT) %>%
# mutate(MedianDepth"] = median(depth)) %>%
# mutate(MedianDepth"] = median(Allele_Ratio))
#
#distinct_mutations = distinct_mutations[-c(3,5,6,8),]
# if (
# length(
# sort(
# unique(
# c(
# which(distinct_mutations$MedianDepth<minumum_median_total),
# which(distinct_mutations$MedianDepthMut<minumum_median_mutation)
# )
# )
# )
# )>0)
# distinct_mutations = distinct_mutations[
# -sort(
# unique(c(
# which(distinct_mutations$MedianDepth<minumum_median_total),
# which(distinct_mutations$MedianDepthMut<minumum_median_mutation)
# ))
# ),]
distinct_mutations <- distinct_mutations %>%
filter(MedianDepth>=minumum_median_total & MedianDepthMut>=minumum_median_mutation) %>%
ungroup()
if (nrow(distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
#rownames(distinct_mutations) = 1:nrow(distinct_mutations)
valid_distinct_mutations = distinct_mutations
if (!is.null(verified_genes))
valid_distinct_mutations = subset(distinct_mutations,distinct_mutations$Gene %in% verified_genes) %>% ## mod
filter(REF!="-") ## mod
if (nrow(valid_distinct_mutations)==0){
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
if (file.exists(file.path( out_dir, "D.RData")))
file.remove(file.path(out_dir,"D.RData"))
return(list("distinct_mutations"=distinct_mutations, "g"=NULL))
}
valid_distinct_mutations_values = NULL
for(i in 1:nrow(valid_distinct_mutations)) {
valid_distinct_mutations_values = c(valid_distinct_mutations_values,paste0(valid_distinct_mutations[i,c("Chr","PosStart")],collapse="_"))
}
save(valid_distinct_mutations,file=file.path(out_dir,"valid_distinct_mutations.RData"))
# make final mutations data structures
#load("valid_clones_mapping.RData")
#mutations = array(0,c(length(unique(valid_clones_mapping$Run)),6))
#rownames(mutations) = sort(unique(valid_clones_mapping$Run))
#colnames(mutations) = paste0(valid_distinct_mutations$Gene,"_",valid_distinct_mutations_values,"_",valid_distinct_mutations$REF,"_",valid_distinct_mutations$ALT)
mutations = array(0,c(length(unique(colnames(depth))),nrow(valid_distinct_mutations)))
rownames(mutations) = sort(unique(colnames(depth)))
colnames(mutations) = paste0(valid_distinct_mutations$Gene,"_",valid_distinct_mutations_values,"_",valid_distinct_mutations$REF,"_",valid_distinct_mutations$ALT)
#mutations = array(0,c(nrow(valid_distinct_mutations), length(unique(o_snpMut_filt_freq$scID))))
#colnames(mutations) = sort(unique(o_snpMut_filt_freq$scID))
#rownames(mutations) = valid_distinct_mutations_values
#mutations = mutations[valid_distinct_mutations_values,]
#browser()
# set NA values
depth = t(depth)
#dim(depth)
#depth = depth[colnames(mutations), rownames(mutations), drop=FALSE]
depth = depth[sort(unique(snpMut_filt_freq$scID)), valid_distinct_mutations_values, drop=FALSE]
mutations <- inner_join(cells_aggregate_info, valid_distinct_mutations) %>%
filter( scID %in% rownames({{depth}}))%>%
mutate(idx = paste(Gene,Chr,PosStart,REF,ALT,sep='_')) %>%
mutate(val=1) %>%
distinct() %>%
#select(scID,idx,val) %>%
pivot_wider(id_cols=scID, names_from = idx, values_from=val, values_fill=0) %>%
full_join(data.frame(scID=rownames({{depth}})))
mutations[is.na(mutations)] <- 0
mutations=data.frame(mutations,row.names = 1)
col_n <-
apply(str_split(colnames(mutations), pattern = "_", simplify = TRUE)[,2:3, drop=FALSE], 1, paste, collapse="_")
depth = depth[rownames(mutations), col_n, drop=FALSE]
colnames(depth) = colnames(mutations)
mutations[which(depth<=depth_minimum,arr.ind=TRUE)] = NA # missing values rate equals to 359/2850, that is approx 12.6%
cells_aggregate_info = cells_aggregate_info[which(cells_aggregate_info$scID%in%rownames(mutations)), , drop=FALSE]
mycellsdata = list()
for ( t in time_points )
mycellsdata[[t]] = as.matrix.data.frame(mutations[sort(unique(cells_aggregate_info$scID[which(cells_aggregate_info$Time==t)])), , drop=FALSE], rownames.force = TRUE)
D = mycellsdata
#browser()
save(D,file=file.path(out_dir,"D.RData"))
####
#browser()
g <- plot_D(D)
####
# make Oncoprint
n_rows=0
for (t in seq(1, length(time_points)))
n_rows=n_rows+nrow(D[[t]])
clusters = array(NA,c(n_rows,1))
init_i=1
end_i=0
r_names= NULL
data = NULL
for (t in seq(1, length(time_points))) {
if(nrow(D[[t]])>0) {
end_i <- end_i+nrow(D[[t]])
r_names=c(r_names, rownames(D[[t]]))
clusters[init_i:end_i,1] = time_points[[t]]
init_i <- end_i+1
data <- rbind(data, D[[t]])
}
}
rownames(clusters) <- r_names
#clusters[1:nrow(D[[1]]),1] = "T1_before_treatment"
#clusters[(nrow(D[[1]])+1):(nrow(D[[1]])+nrow(D[[2]])),1] = "T2_4_days_treatment"
#clusters[(nrow(D[[1]])+nrow(D[[2]])+1):(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])),1] = "T3_28_days_treatment"
#clusters[(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])+1):(nrow(D[[1]])+nrow(D[[2]])+nrow(D[[3]])+nrow(D[[4]])),1] = "T4_57_days_treatment"
#rownames(clusters) = c(rownames(D[[1]]),rownames(D[[2]]),rownames(D[[3]]),rownames(D[[4]]))
#data = rbind(D[[1]],D[[2]],D[[3]],D[[4]])
#data[which(is.na(data))] = 0
#data = import.genotypes(data)
# data = annotate.stages(data,clusters)
## if (ncol(data$genotypes)>1 || length(unique(data$genotypes[,1]))>1) #errore generico
## oncoprint(data,excl.sort=FALSE,group.by.stage=TRUE)
#distinct_mutations[['ResultantMeanDepth']]<-apply(depth,2,mean)
#distinct_mutations[['ResultantVarDepth']]<-apply(depth,2,var)
#browser()
num_columns <- sapply(distinct_mutations, is.numeric)
distinct_mutations[num_columns] <- lapply(distinct_mutations[num_columns], round, 3)
return(list("distinct_mutations"=distinct_mutations, "g"=g))
}
plot_D <-function(D) {
D <- D %>%
setNames({names(.) %>%
str_replace_all(pattern = "_", replacement = " ") %>%
data.frame("times"=.) %>%
mutate(
times=ifelse(str_starts(times, pattern = "T[0-9] ", negate = TRUE),
paste0('T', row_number(), " ",times), times)
)}$times)
#names(D) <- t_order$times
D1 <- lapply(D, FUN=function(x){as_tibble(x,rownames = "cell")}) %>%
bind_rows(.id = "time") %>%
mutate(time=forcats::fct_inorder(factor(time, ordered=TRUE))) %>%
relocate(cell, .after = last_col()) %>% ##
arrange(across(everything())) %>%
mutate(ordered=factor(cell, levels = unique(cell, fromLast=TRUE), ordered = TRUE))
#D1 <- D1 %>%
# mutate(ordered=cell) %>%
# arrange(across(everything())) %>%
# mutate(ordered=factor(ordered, levels = unique(ordered, fromLast=TRUE), ordered = TRUE))
s <- D1 %>% group_by(time) %>%
summarise(across(where(is.numeric), ~sum(.,na.rm = TRUE))) %>%
pivot_longer(-1) %>%
pivot_wider(names_from = 1, values_from = value) #%>%
#arrange(across(everything())) not needed
Dw <- D1 %>%
pivot_longer( col = -c(time, cell,ordered), names_to = "mutation", values_to = "value") %>%
mutate(value = factor(as.integer(value), levels=c(0,1,"NA"), exclude = NULL, ordered = T)) %>%
mutate(mutation = factor(mutation, levels=s$name, ordered=TRUE))
per_mut_site <- as.data.frame(Dw) %>% group_by(mutation)
n_na <- (per_mut_site %>% mutate(cnt=sum(is.na(value))) )$cnt
n_one <- (per_mut_site %>% mutate(cnt=sum(value==1, na.rm = TRUE)) )$cnt
n_zero <- (per_mut_site %>% mutate(cnt=sum(value==0, na.rm = TRUE)) )$cnt
str__na <- paste0("# \"NA\" = [", paste(range(n_na), collapse = ", "),"]")
str__zero <- paste0("# \"0\" = [", paste(range(n_zero), collapse = ", "),"]")
str__one <- paste0("# \"1\" = [", paste(range(n_one), collapse = ", "),"]")
g <- ggplot(Dw, aes(mutation, y=ordered, fill= value)) +
geom_tile()+
scale_fill_manual(values = c("black", "white", "orange"), na.value="gray50",
aesthetics = c("colour", "fill")#,
#labels = c("passing filter(s)", "filtered out", "NA")
)+ #orange=2
facet_grid(
rows = vars(time),
scales = "free",
space = "free",
labeller = label_wrap_gen(width=15)
) +
scale_x_discrete(expand = c(0,0))+
theme(axis.text.x = element_text(angle=45, hjust=1), ##it is shifted why?
axis.text.y = element_blank(),
axis.ticks.y= element_blank()
)+
theme(
strip.background = element_rect(
color="black", fill="#FC8207", size=1.5, linetype="solid"
),
panel.border = element_rect(color = "#FC8207", fill = NA, size = 2)
) +
theme(legend.key=element_rect(colour="black")) +
theme(panel.spacing = unit(0, "pt"))+
labs(y = "cells")+
labs(x = "selected mutations")+
#labs(title = paste("Pre-inference input matrix {",str__na, ";", str__one, ";", str__zero,"}"))+
labs(title = paste("Filtered binary data matrix"))+
theme(text = element_text(size = 14))
return(g)
}
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